mosek.Task

Task.Task
Task(env=None, maxnumcon=0, maxnumvar=0, other=None)

Task(other)


Constructor of a new optimization task.

Parameters:

• env (Env) – Parent environment. (input)
• maxnumcon (int) – An optional hint about the maximal number of constraints in the task. (input)
• maxnumvar (int) – An optional hint about the maximal number of variables in the task. (input)
• other (Task) – A task that will be cloned. (input)
Task.__del__
def __del__ ()


Free the underlying native allocation.

Task.analyzenames
def analyzenames (whichstream, nametype)


The function analyzes the names and issues an error if a name is invalid.

Parameters:

Groups:

Task.analyzeproblem
def analyzeproblem (whichstream)


The function analyzes the data of a task and writes out a report.

Parameters:
whichstream (mosek.streamtype) – Index of the stream. (input)
Groups:
Task.analyzesolution
def analyzesolution (whichstream, whichsol)


Print information related to the quality of the solution and other solution statistics.

By default this function prints information about the largest infeasibilites in the solution, the primal (and possibly dual) objective value and the solution status.

Following parameters can be used to configure the printed statistics:

Parameters:

Groups:

Task.appendbarvars
def appendbarvars (dim)


Appends positive semidefinite matrix variables of dimensions given by dim to the problem.

Parameters:
dim (int[]) – Dimensions of symmetric matrix variables to be added. (input)
Groups:
Symmetric matrix variable data
Task.appendcone
def appendcone (ct, conepar, submem)


Appends a new conic constraint to the problem. Hence, add a constraint

$\hat{x} \in \K$

to the problem where $$\K$$ is a convex cone. $$\hat{x}$$ is a subset of the variables which will be specified by the argument submem.

Depending on the value of ct this function appends a normal (conetype.quad) or rotated quadratic cone (conetype.rquad).

Define

$\hat{x} = x_{\mathtt{submem}[0]},\ldots,x_{\mathtt{submem}[\mathtt{nummem}-1]}.$

Depending on the value of ct this function appends one of the constraints:

• Quadratic cone (conetype.quad) :

$\hat{x}_0 \geq \sqrt{\sum_{i=1}^{i<\mathtt{nummem}} \hat{x}_i^2}$
• Rotated quadratic cone (conetype.rquad) :

$2 \hat{x}_0 \hat{x}_1 \geq \sum_{i=2}^{i<\mathtt{nummem}} \hat{x}^2_i, \quad \hat{x}_{0}, \hat{x}_1 \geq 0$

Please note that the sets of variables appearing in different conic constraints must be disjoint.

For an explained code example see Section Conic Quadratic Optimization.

Parameters:

• ct (mosek.conetype) – Specifies the type of the cone. (input)
• conepar (float) – This argument is currently not used. It can be set to 0 (input)
• submem (int[]) – Variable subscripts of the members in the cone. (input)
Groups:

Conic constraint data

Task.appendconeseq
def appendconeseq (ct, conepar, nummem, j)


Appends a new conic constraint to the problem, as in Task.appendcone. The function assumes the members of cone are sequential where the first member has index j and the last j+nummem-1.

Parameters:

• ct (mosek.conetype) – Specifies the type of the cone. (input)
• conepar (float) – This argument is currently not used. It can be set to 0 (input)
• nummem (int) – Number of member variables in the cone. (input)
• j (int) – Index of the first variable in the conic constraint. (input)
Groups:

Conic constraint data

Task.appendconesseq
def appendconesseq (ct, conepar, nummem, j)


Appends a number of conic constraints to the problem, as in Task.appendcone. The $$k$$th cone is assumed to be of dimension nummem[k]. Moreover, it is assumed that the first variable of the first cone has index $$j$$ and starting from there the sequentially following variables belong to the first cone, then to the second cone and so on.

Parameters:

• ct (mosek.conetype[]) – Specifies the type of the cone. (input)
• conepar (float[]) – This argument is currently not used. It can be set to 0 (input)
• nummem (int[]) – Numbers of member variables in the cones. (input)
• j (int) – Index of the first variable in the first cone to be appended. (input)
Groups:

Conic constraint data

Task.appendcons
def appendcons (num)


Appends a number of constraints to the model. Appended constraints will be declared free. Please note that MOSEK will automatically expand the problem dimension to accommodate the additional constraints.

Parameters:
num (int) – Number of constraints which should be appended. (input)
Groups:
Linear constraint data
Task.appendsparsesymmat
def appendsparsesymmat (dim, subi, subj, valij) -> idx


MOSEK maintains a storage of symmetric data matrices that is used to build $$\barC$$ and $$\barA$$. The storage can be thought of as a vector of symmetric matrices denoted $$E$$. Hence, $$E_i$$ is a symmetric matrix of certain dimension.

This function appends a general sparse symmetric matrix on triplet form to the vector $$E$$ of symmetric matrices. The vectors subi, subj, and valij contains the row subscripts, column subscripts and values of each element in the symmetric matrix to be appended. Since the matrix that is appended is symmetric, only the lower triangular part should be specified. Moreover, duplicates are not allowed.

Observe the function reports the index (position) of the appended matrix in $$E$$. This index should be used for later references to the appended matrix.

Parameters:

• dim (int) – Dimension of the symmetric matrix that is appended. (input)
• subi (int[]) – Row subscript in the triplets. (input)
• subj (int[]) – Column subscripts in the triplets. (input)
• valij (float[]) – Values of each triplet. (input)
Return:

idx (int) – Unique index assigned to the inputted matrix that can be used for later reference.

Groups:

Symmetric matrix variable data

Task.appendvars
def appendvars (num)


Appends a number of variables to the model. Appended variables will be fixed at zero. Please note that MOSEK will automatically expand the problem dimension to accommodate the additional variables.

Parameters:
num (int) – Number of variables which should be appended. (input)
Groups:
Scalar variable data
Task.asyncgetresult
def asyncgetresult (server, port, token) -> respavailable, resp, trm


Request a response from a remote job. If successful, solver response, termination code and solutions are retrieved.

Parameters:

• server (str) – Name or IP address of the solver server. (input)
• port (str) – Network port of the solver service. (input)
• token (str) – The task token. (input)
Return:

• respavailable (int) – Indicates if a remote response is available. If this is not true, resp and trm should be ignored.
• resp (mosek.rescode) – Is the response code from the remote solver.
• trm (mosek.rescode) – Is either rescode.ok or a termination response code.
Task.asyncoptimize
def asyncoptimize (server, port) -> token


Offload the optimization task to a solver server defined by server:port. The call will return immediately and not wait for the result.

If the string parameter sparam.remote_access_token is not blank, it will be passed to the server as authentication.

Parameters:

• server (str) – Name or IP address of the solver server (input)
• port (str) – Network port of the solver service (input)
Return:

token (str) – Returns the task token

Task.asyncpoll
def asyncpoll (server, port, token) -> respavailable, resp, trm


Requests information about the status of the remote job.

Parameters:

• server (str) – Name or IP address of the solver server (input)
• port (str) – Network port of the solver service (input)
• token (str) – The task token (input)
Return:

• respavailable (int) – Indicates if a remote response is available. If this is not true, resp and trm should be ignored.
• resp (mosek.rescode) – Is the response code from the remote solver.
• trm (mosek.rescode) – Is either rescode.ok or a termination response code.
Task.asyncstop
def asyncstop (server, port, token)


Request that the job identified by the token is terminated.

Parameters:

• server (str) – Name or IP address of the solver server (input)
• port (str) – Network port of the solver service (input)
• token (str) – The task token (input)
Task.basiscond
def basiscond () -> nrmbasis, nrminvbasis


If a basic solution is available and it defines a nonsingular basis, then this function computes the 1-norm estimate of the basis matrix and a 1-norm estimate for the inverse of the basis matrix. The 1-norm estimates are computed using the method outlined in [Ste98], pp. 388-391.

By definition the 1-norm condition number of a matrix $$B$$ is defined as

$\kappa_1(B) := \|B\|_1 \|B^{-1}\|_1.$

Moreover, the larger the condition number is the harder it is to solve linear equation systems involving $$B$$. Given estimates for $$\|B\|_1$$ and $$\|B^{-1}\|_1$$ it is also possible to estimate $$\kappa_1(B)$$.

Return:

• nrmbasis (float) – An estimate for the 1-norm of the basis.
• nrminvbasis (float) – An estimate for the 1-norm of the inverse of the basis.
Groups:

Basis matrix

Task.checkconvexity
def checkconvexity ()


This function checks if a quadratic optimization problem is convex. The amount of checking is controlled by iparam.check_convexity.

The function reports an error if the problem is not convex.

Groups:
Task.checkmem
def checkmem (file, line)


Checks the memory allocated by the task.

Parameters:

• file (str) – File from which the function is called. (input)
• line (int) – Line in the file from which the function is called. (input)
Groups:

Memory

Task.chgbound Deprecated
def chgbound (accmode, i, lower, finite, value)


Changes a bound for one constraint or variable. If accmode equals accmode.con, a constraint bound is changed, otherwise a variable bound is changed.

If lower is non-zero, then the lower bound is changed as follows:

$\begin{split}\mbox{new lower bound} = \left\{ \begin{array}{ll} - \infty, & \mathtt{finite}=0, \\ \mathtt{value} & \mbox{otherwise}. \end{array} \right.\end{split}$

Otherwise if lower is zero, then

$\begin{split}\mbox{new upper bound} = \left\{ \begin{array}{ll} \infty, & \mathtt{finite}=0, \\ \mathtt{value} & \mbox{otherwise}. \end{array} \right.\end{split}$

Please note that this function automatically updates the bound key for bound, in particular, if the lower and upper bounds are identical, the bound key is changed to fixed.

Parameters:

• accmode (mosek.accmode) – Defines if operations are performed row-wise (constraint-oriented) or column-wise (variable-oriented). (input)
• i (int) – Index of the constraint or variable for which the bounds should be changed. (input)
• lower (int) – If non-zero, then the lower bound is changed, otherwise the upper bound is changed. (input)
• finite (int) – If non-zero, then value is assumed to be finite. (input)
• value (float) – New value for the bound. (input)
Groups:

Bound data

Task.chgconbound
def chgconbound (i, lower, finite, value)


Changes a bound for one constraint.

If lower is non-zero, then the lower bound is changed as follows:

$\begin{split}\mbox{new lower bound} = \left\{ \begin{array}{ll} - \infty, & \mathtt{finite}=0, \\ \mathtt{value} & \mbox{otherwise}. \end{array} \right.\end{split}$

Otherwise if lower is zero, then

$\begin{split}\mbox{new upper bound} = \left\{ \begin{array}{ll} \infty, & \mathtt{finite}=0, \\ \mathtt{value} & \mbox{otherwise}. \end{array} \right.\end{split}$

Please note that this function automatically updates the bound key for the bound, in particular, if the lower and upper bounds are identical, the bound key is changed to fixed.

Parameters:

• i (int) – Index of the constraint for which the bounds should be changed. (input)
• lower (int) – If non-zero, then the lower bound is changed, otherwise the upper bound is changed. (input)
• finite (int) – If non-zero, then value is assumed to be finite. (input)
• value (float) – New value for the bound. (input)
Groups:

Bound data

Task.chgvarbound
def chgvarbound (j, lower, finite, value)


Changes a bound for one variable.

If lower is non-zero, then the lower bound is changed as follows:

$\begin{split}\mbox{new lower bound} = \left\{ \begin{array}{ll} - \infty, & \mathtt{finite}=0, \\ \mathtt{value} & \mbox{otherwise}. \end{array} \right.\end{split}$

Otherwise if lower is zero, then

$\begin{split}\mbox{new upper bound} = \left\{ \begin{array}{ll} \infty, & \mathtt{finite}=0, \\ \mathtt{value} & \mbox{otherwise}. \end{array} \right.\end{split}$

Please note that this function automatically updates the bound key for the bound, in particular, if the lower and upper bounds are identical, the bound key is changed to fixed.

Parameters:

• j (int) – Index of the variable for which the bounds should be changed. (input)
• lower (int) – If non-zero, then the lower bound is changed, otherwise the upper bound is changed. (input)
• finite (int) – If non-zero, then value is assumed to be finite. (input)
• value (float) – New value for the bound. (input)
Groups:

Bound data

Task.commitchanges
def commitchanges ()


Commits all cached problem changes to the task. It is usually not necessary to call this function explicitly since changes will be committed automatically when required.

Groups:
Scalar variable data
Task.deletesolution
def deletesolution (whichsol)


Undefine a solution and free the memory it uses.

Parameters:
whichsol (mosek.soltype) – Selects a solution. (input)
Groups:
Task.dualsensitivity
def dualsensitivity (subj, leftpricej, rightpricej, leftrangej, rightrangej)


Calculates sensitivity information for objective coefficients. The indexes of the coefficients to analyze are

$\{\mathtt{subj}[i] ~|~ i = 0,\ldots,\mathtt{numj}-1\}$

The type of sensitivity analysis to perform (basis or optimal partition) is controlled by the parameter iparam.sensitivity_type.

For an example, please see Section Example: Sensitivity Analysis.

Parameters:

• subj (int[]) – Indexes of objective coefficients to analyze. (input)
• leftpricej (float[]) – $$\mathtt{leftpricej}[j]$$ is the left shadow price for the coefficient with index $$\mathtt{subj[j]}$$. (output)
• rightpricej (float[]) – $$\mathtt{rightpricej}[j]$$ is the right shadow price for the coefficient with index $$\mathtt{subj[j]}$$. (output)
• leftrangej (float[]) – $$\mathtt{leftrangej}[j]$$ is the left range $$\beta_1$$ for the coefficient with index $$\mathtt{subj[j]}$$. (output)
• rightrangej (float[]) – $$\mathtt{rightrangej}[j]$$ is the right range $$\beta_2$$ for the coefficient with index $$\mathtt{subj[j]}$$. (output)
Groups:

Sensitivity analysis

Task.getacol
def getacol (j, subj, valj) -> nzj


Obtains one column of $$A$$ in a sparse format.

Parameters:

• j (int) – Index of the column. (input)
• subj (int[]) – Row indices of the non-zeros in the column obtained. (output)
• valj (float[]) – Numerical values in the column obtained. (output)
Return:

nzj (int) – Number of non-zeros in the column obtained.

Groups:

Scalar variable data

Task.getacolnumnz
def getacolnumnz (i) -> nzj


Obtains the number of non-zero elements in one column of $$A$$.

Parameters:
i (int) – Index of the column. (input)
Return:
nzj (int) – Number of non-zeros in the $$j$$-th column of $$A$$.
Groups:
Scalar variable data
Task.getacolslicetrip
def getacolslicetrip (first, last, subi, subj, val)


Obtains a sequence of columns from $$A$$ in sparse triplet format. The function returns the content of all columns whose index j satisfies first <= j < last. The triplets corresponding to nonzero entries are stored in the arrays subi, subj and val.

Parameters:

• first (int) – Index of the first column in the sequence. (input)
• last (int) – Index of the last column in the sequence plus one. (input)
• subi (int[]) – Constraint subscripts. (output)
• subj (int[]) – Column subscripts. (output)
• val (float[]) – Values. (output)
Groups:

Scalar variable data

Task.getaij
def getaij (i, j) -> aij


Obtains a single coefficient in $$A$$.

Parameters:

• i (int) – Row index of the coefficient to be returned. (input)
• j (int) – Column index of the coefficient to be returned. (input)
Return:

aij (float) – The required coefficient $$a_{i,j}$$.

Groups:

Scalar variable data

Task.getapiecenumnz
def getapiecenumnz (firsti, lasti, firstj, lastj) -> numnz


Obtains the number non-zeros in a rectangular piece of $$A$$, i.e. the number of elements in the set

$\{ (i,j)~:~ a_{i,j} \neq 0,~ \mathtt{firsti} \leq i \leq \mathtt{lasti}-1, ~\mathtt{firstj} \leq j \leq \mathtt{lastj}-1\}$

This function is not an efficient way to obtain the number of non-zeros in one row or column. In that case use the function Task.getarownumnz or Task.getacolnumnz.

Parameters:

• firsti (int) – Index of the first row in the rectangular piece. (input)
• lasti (int) – Index of the last row plus one in the rectangular piece. (input)
• firstj (int) – Index of the first column in the rectangular piece. (input)
• lastj (int) – Index of the last column plus one in the rectangular piece. (input)
Return:

numnz (int) – Number of non-zero $$A$$ elements in the rectangular piece.

Task.getarow
def getarow (i, subi, vali) -> nzi


Obtains one row of $$A$$ in a sparse format.

Parameters:

• i (int) – Index of the row. (input)
• subi (int[]) – Column indices of the non-zeros in the row obtained. (output)
• vali (float[]) – Numerical values of the row obtained. (output)
Return:

nzi (int) – Number of non-zeros in the row obtained.

Groups:

Scalar variable data

Task.getarownumnz
def getarownumnz (i) -> nzi


Obtains the number of non-zero elements in one row of $$A$$.

Parameters:
i (int) – Index of the row. (input)
Return:
nzi (int) – Number of non-zeros in the $$i$$-th row of $$A$$.
Groups:
Scalar variable data
Task.getarowslicetrip
def getarowslicetrip (first, last, subi, subj, val)


Obtains a sequence of rows from $$A$$ in sparse triplet format. The function returns the content of all rows whose index i satisfies first <= i < last. The triplets corresponding to nonzero entries are stored in the arrays subi, subj and val.

Parameters:

• first (int) – Index of the first row in the sequence. (input)
• last (int) – Index of the last row in the sequence plus one. (input)
• subi (int[]) – Constraint subscripts. (output)
• subj (int[]) – Column subscripts. (output)
• val (float[]) – Values. (output)
Groups:

Scalar variable data

Task.getaslice Deprecated
def getaslice (accmode, first, last, ptrb, ptre, sub, val)


Obtains a sequence of rows or columns from $$A$$ in sparse format.

Parameters:

• accmode (mosek.accmode) – Defines whether a column slice or a row slice is requested. (input)
• first (int) – Index of the first row or column in the sequence. (input)
• last (int) – Index of the last row or column in the sequence plus one. (input)
• ptrb (int[]) – ptrb[t] is an index pointing to the first element in the $$t$$-th row or column obtained. (output)
• ptre (int[]) – ptre[t] is an index pointing to the last element plus one in the $$t$$-th row or column obtained. (output)
• sub (int[]) – Contains the row or column subscripts. (output)
• val (float[]) – Contains the coefficient values. (output)
Groups:

Scalar variable data

Task.getaslicenumnz Deprecated
def getaslicenumnz (accmode, first, last) -> numnz


Obtains the number of non-zeros in a slice of rows or columns of $$A$$.

Parameters:

• accmode (mosek.accmode) – Defines whether non-zeros are counted in a column slice or a row slice. (input)
• first (int) – Index of the first row or column in the sequence. (input)
• last (int) – Index of the last row or column plus one in the sequence. (input)
Return:

numnz (int) – Number of non-zeros in the slice.

Task.getbarablocktriplet
def getbarablocktriplet (subi, subj, subk, subl, valijkl) -> num


Obtains $$\barA$$ in block triplet form.

Parameters:

• subi (int[]) – Constraint index. (output)
• subj (int[]) – Symmetric matrix variable index. (output)
• subk (int[]) – Block row index. (output)
• subl (int[]) – Block column index. (output)
• valijkl (float[]) – The numerical value associated with each block triplet. (output)
Return:

num (int) – Number of elements in the block triplet form.

Groups:

Symmetric matrix variable data

Task.getbaraidx
def getbaraidx (idx, sub, weights) -> i, j, num


Obtains information about an element in $$\barA$$. Since $$\barA$$ is a sparse matrix of symmetric matrices, only the nonzero elements in $$\barA$$ are stored in order to save space. Now $$\barA$$ is stored vectorized i.e. as one long vector. This function makes it possible to obtain information such as the row index and the column index of a particular element of the vectorized form of $$\barA$$.

Please observe if one element of $$\barA$$ is inputted multiple times then it may be stored several times in vectorized form. In that case the element with the highest index is the one that is used.

Parameters:

• idx (int) – Position of the element in the vectorized form. (input)
• sub (int[]) – A list indexes of the elements from symmetric matrix storage that appear in the weighted sum. (output)
• weights (float[]) – The weights associated with each term in the weighted sum. (output)
Return:

• i (int) – Row index of the element at position idx.
• j (int) – Column index of the element at position idx.
• num (int) – Number of terms in weighted sum that forms the element.
Groups:

Symmetric matrix variable data

Task.getbaraidxij
def getbaraidxij (idx) -> i, j


Obtains information about an element in $$\barA$$. Since $$\barA$$ is a sparse matrix of symmetric matrices, only the nonzero elements in $$\barA$$ are stored in order to save space. Now $$\barA$$ is stored vectorized i.e. as one long vector. This function makes it possible to obtain information such as the row index and the column index of a particular element of the vectorized form of $$\barA$$.

Please note that if one element of $$\barA$$ is inputted multiple times then it may be stored several times in vectorized form. In that case the element with the highest index is the one that is used.

Parameters:

idx (int) – Position of the element in the vectorized form. (input)

Return:

• i (int) – Row index of the element at position idx.
• j (int) – Column index of the element at position idx.
Groups:

Symmetric matrix variable data

Task.getbaraidxinfo
def getbaraidxinfo (idx) -> num


Each nonzero element in $$\barA_{ij}$$ is formed as a weighted sum of symmetric matrices. Using this function the number of terms in the weighted sum can be obtained. See description of Task.appendsparsesymmat for details about the weighted sum.

Parameters:
idx (int) – The internal position of the element for which information should be obtained. (input)
Return:
num (int) – Number of terms in the weighted sum that form the specified element in $$\barA$$.
Groups:
Symmetric matrix variable data
Task.getbarasparsity
def getbarasparsity (idxij) -> numnz


The matrix $$\barA$$ is assumed to be a sparse matrix of symmetric matrices. This implies that many of the elements in $$\barA$$ are likely to be zero matrices. Therefore, in order to save space, only nonzero elements in $$\barA$$ are stored on vectorized form. This function is used to obtain the sparsity pattern of $$\barA$$ and the position of each nonzero element in the vectorized form of $$\barA$$. From the index detailed information about each nonzero $$\barA_{i,j}$$ can be obtained using Task.getbaraidxinfo and Task.getbaraidx.

Parameters:
idxij (int[]) – Position of each nonzero element in the vectorized form of $$\barA$$. (output)
Return:
numnz (int) – Number of nonzero elements in $$\barA$$.
Groups:
Symmetric matrix variable data
Task.getbarcblocktriplet
def getbarcblocktriplet (subj, subk, subl, valjkl) -> num


Obtains $$\barC$$ in block triplet form.

Parameters:

• subj (int[]) – Symmetric matrix variable index. (output)
• subk (int[]) – Block row index. (output)
• subl (int[]) – Block column index. (output)
• valjkl (float[]) – The numerical value associated with each block triplet. (output)
Return:

num (int) – Number of elements in the block triplet form.

Groups:

Symmetric matrix variable data

Task.getbarcidx
def getbarcidx (idx, sub, weights) -> j, num


Obtains information about an element in $$\barC$$.

Parameters:

• idx (int) – Index of the element for which information should be obtained. (input)
• sub (int[]) – Elements appearing the weighted sum. (output)
• weights (float[]) – Weights of terms in the weighted sum. (output)
Return:

• j (int) – Row index in $$\barC$$.
• num (int) – Number of terms in the weighted sum.
Groups:

Symmetric matrix variable data

Task.getbarcidxinfo
def getbarcidxinfo (idx) -> num


Obtains the number of terms in the weighted sum that forms a particular element in $$\barC$$.

Parameters:
idx (int) – Index of the element for which information should be obtained. The value is an index of a symmetric sparse variable. (input)
Return:
num (int) – Number of terms that appear in the weighted sum that forms the requested element.
Groups:
Symmetric matrix variable data
Task.getbarcidxj
def getbarcidxj (idx) -> j


Obtains the row index of an element in $$\barC$$.

Parameters:
idx (int) – Index of the element for which information should be obtained. (input)
Return:
j (int) – Row index in $$\barC$$.
Groups:
Symmetric matrix variable data
Task.getbarcsparsity
def getbarcsparsity (idxj) -> numnz


Internally only the nonzero elements of $$\barC$$ are stored in a vector. This function is used to obtain the nonzero elements of $$\barC$$ and their indexes in the internal vector representation (in idx). From the index detailed information about each nonzero $$\barC_j$$ can be obtained using Task.getbarcidxinfo and Task.getbarcidx.

Parameters:
idxj (int[]) – Internal positions of the nonzeros elements in $$\barC$$. (output)
Return:
numnz (int) – Number of nonzero elements in $$\barC$$.
Groups:
Symmetric matrix variable data
Task.getbarsj
def getbarsj (whichsol, j, barsj)


Obtains the dual solution for a semidefinite variable. Only the lower triangular part of $$\barS_j$$ is returned because the matrix by construction is symmetric. The format is that the columns are stored sequentially in the natural order.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• j (int) – Index of the semidefinite variable. (input)
• barsj (float[]) – Value of $$\barS_j$$. (output)
Groups:

Solution (get)

Task.getbarvarname
def getbarvarname (i) -> name


Obtains the name of a semidefinite variable.

Parameters:
i (int) – Index of the variable. (input)
Return:
name (str) – The requested name is copied to this buffer.
Groups:
Naming
Task.getbarvarnameindex
def getbarvarnameindex (somename) -> asgn, index


Obtains the index of semidefinite variable from its name.

Parameters:

somename (str) – The name of the variable. (input)

Return:

• asgn (int) – Non-zero if the name somename is assigned to some semidefinite variable.
• index (int) – The index of a semidefinite variable with the name somename (if one exists).
Groups:

Naming

Task.getbarvarnamelen
def getbarvarnamelen (i) -> len


Obtains the length of the name of a semidefinite variable.

Parameters:
i (int) – Index of the variable. (input)
Return:
len (int) – Returns the length of the indicated name.
Groups:
Naming
Task.getbarxj
def getbarxj (whichsol, j, barxj)


Obtains the primal solution for a semidefinite variable. Only the lower triangular part of $$\barX_j$$ is returned because the matrix by construction is symmetric. The format is that the columns are stored sequentially in the natural order.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• j (int) – Index of the semidefinite variable. (input)
• barxj (float[]) – Value of $$\barX_j$$. (output)
Groups:

Solution (get)

Task.getbound Deprecated
def getbound (accmode, i) -> bk, bl, bu


Obtains bound information for one constraint or variable.

Parameters:

• accmode (mosek.accmode) – Defines if operations are performed row-wise (constraint-oriented) or column-wise (variable-oriented). (input)
• i (int) – Index of the constraint or variable for which the bound information should be obtained. (input)
Return:

• bk (mosek.boundkey) – Bound keys.
• bl (float) – Values for lower bounds.
• bu (float) – Values for upper bounds.
Groups:

Bound data

Task.getboundslice Deprecated
def getboundslice (accmode, first, last, bk, bl, bu)


Obtains bounds information for a slice of variables or constraints.

Parameters:

• accmode (mosek.accmode) – Defines if operations are performed row-wise (constraint-oriented) or column-wise (variable-oriented). (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• bk (mosek.boundkey[]) – Bound keys. (output)
• bl (float[]) – Values for lower bounds. (output)
• bu (float[]) – Values for upper bounds. (output)
Groups:

Bound data

Task.getc
def getc (c)


Obtains all objective coefficients $$c$$.

Parameters:
c (float[]) – Linear terms of the objective as a dense vector. The length is the number of variables. (output)
Groups:
Scalar variable data
Task.getcfix
def getcfix () -> cfix


Obtains the fixed term in the objective.

Return:
cfix (float) – Fixed term in the objective.
Groups:
Scalar variable data
Task.getcj
def getcj (j) -> cj


Obtains one coefficient of $$c$$.

Parameters:
j (int) – Index of the variable for which the $$c$$ coefficient should be obtained. (input)
Return:
cj (float) – The value of $$c_j$$.
Groups:
Scalar variable data
Task.getconbound
def getconbound (i) -> bk, bl, bu


Obtains bound information for one constraint.

Parameters:

i (int) – Index of the constraint for which the bound information should be obtained. (input)

Return:

• bk (mosek.boundkey) – Bound keys.
• bl (float) – Values for lower bounds.
• bu (float) – Values for upper bounds.
Groups:

Bound data

Task.getconboundslice
def getconboundslice (first, last, bk, bl, bu)


Obtains bounds information for a slice of the constraints.

Parameters:

• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• bk (mosek.boundkey[]) – Bound keys. (output)
• bl (float[]) – Values for lower bounds. (output)
• bu (float[]) – Values for upper bounds. (output)
Groups:

Bound data

Task.getcone
def getcone (k, submem) -> ct, conepar, nummem


Obtains a cone.

Parameters:

• k (int) – Index of the cone. (input)
• submem (int[]) – Variable subscripts of the members in the cone. (output)
Return:

• ct (mosek.conetype) – Specifies the type of the cone.
• conepar (float) – This argument is currently not used. It can be set to 0
• nummem (int) – Number of member variables in the cone.
Groups:

Conic constraint data

Task.getconeinfo
def getconeinfo (k) -> ct, conepar, nummem


Parameters:

k (int) – Index of the cone. (input)

Return:

• ct (mosek.conetype) – Specifies the type of the cone.
• conepar (float) – This argument is currently not used. It can be set to 0
• nummem (int) – Number of member variables in the cone.
Groups:

Conic constraint data

Task.getconename
def getconename (i) -> name


Obtains the name of a cone.

Parameters:
i (int) – Index of the cone. (input)
Return:
name (str) – The required name.
Groups:
Naming
Task.getconenameindex
def getconenameindex (somename) -> asgn, index


Checks whether the name somename has been assigned to any cone. If it has been assigned to a cone, then the index of the cone is reported.

Parameters:

somename (str) – The name which should be checked. (input)

Return:

• asgn (int) – Is non-zero if the name somename is assigned to some cone.
• index (int) – If the name somename is assigned to some cone, then index is the index of the cone.
Groups:

Naming

Task.getconenamelen
def getconenamelen (i) -> len


Obtains the length of the name of a cone.

Parameters:
i (int) – Index of the cone. (input)
Return:
len (int) – Returns the length of the indicated name.
Groups:
Naming
Task.getconname
def getconname (i) -> name


Obtains the name of a constraint.

Parameters:
i (int) – Index of the constraint. (input)
Return:
name (str) – The required name.
Groups:
Naming
Task.getconnameindex
def getconnameindex (somename) -> asgn, index


Checks whether the name somename has been assigned to any constraint. If so, the index of the constraint is reported.

Parameters:

somename (str) – The name which should be checked. (input)

Return:

• asgn (int) – Is non-zero if the name somename is assigned to some constraint.
• index (int) – If the name somename is assigned to a constraint, then index is the index of the constraint.
Groups:

Naming

Task.getconnamelen
def getconnamelen (i) -> len


Obtains the length of the name of a constraint.

Parameters:
i (int) – Index of the constraint. (input)
Return:
len (int) – Returns the length of the indicated name.
Groups:
Naming
Task.getcslice
def getcslice (first, last, c)


Obtains a sequence of elements in $$c$$.

Parameters:

• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• c (float[]) – Linear terms of the requested slice of the objective as a dense vector. The length is last-first. (output)
Groups:

Scalar variable data

Task.getdimbarvarj
def getdimbarvarj (j) -> dimbarvarj


Obtains the dimension of a symmetric matrix variable.

Parameters:
j (int) – Index of the semidefinite variable whose dimension is requested. (input)
Return:
dimbarvarj (int) – The dimension of the $$j$$-th semidefinite variable.
Groups:
Symmetric matrix variable data
Task.getdouinf
def getdouinf (whichdinf) -> dvalue


Obtains a double information item from the task information database.

Parameters:
whichdinf (mosek.dinfitem) – Specifies a double information item. (input)
Return:
dvalue (float) – The value of the required double information item.
Groups:
Optimizer statistics
Task.getdouparam
def getdouparam (param) -> parvalue


Obtains the value of a double parameter.

Parameters:
param (mosek.dparam) – Which parameter. (input)
Return:
parvalue (float) – Parameter value.
Groups:
Parameters (get)
Task.getdualobj
def getdualobj (whichsol) -> dualobj


Computes the dual objective value associated with the solution. Note that if the solution is a primal infeasibility certificate, then the fixed term in the objective value is not included.

Moreover, since there is no dual solution associated with an integer solution, an error will be reported if the dual objective value is requested for the integer solution.

Parameters:
whichsol (mosek.soltype) – Selects a solution. (input)
Return:
dualobj (float) – Objective value corresponding to the dual solution.
Groups:
Solution information
Task.getdualsolutionnorms
def getdualsolutionnorms (whichsol) -> nrmy, nrmslc, nrmsuc, nrmslx, nrmsux, nrmsnx, nrmbars


Compute norms of the dual solution.

Parameters:

whichsol (mosek.soltype) – Selects a solution. (input)

Return:

• nrmy (float) – The norm of the $$y$$ vector.
• nrmslc (float) – The norm of the $$s_l^c$$ vector.
• nrmsuc (float) – The norm of the $$s_u^c$$ vector.
• nrmslx (float) – The norm of the $$s_l^x$$ vector.
• nrmsux (float) – The norm of the $$s_u^x$$ vector.
• nrmsnx (float) – The norm of the $$s_n^x$$ vector.
• nrmbars (float) – The norm of the $$\barS$$ vector.
Groups:

Solution information

Task.getdviolbarvar
def getdviolbarvar (whichsol, sub, viol)


Let $$(\barS_j)^*$$ be the value of variable $$\barS_j$$ for the specified solution. Then the dual violation of the solution associated with variable $$\barS_j$$ is given by

$\max(-\lambda_{\min}(\barS_j),\ 0.0).$

Both when the solution is a certificate of primal infeasibility and when it is dual feasible solution the violation should be small.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• sub (int[]) – An array of indexes of $$\barX$$ variables. (input)
• viol (float[]) – viol[k] is the violation of the solution for the constraint $$\barS_{\mathtt{sub}[k]} \in \PSD$$. (output)
Groups:

Solution information

Task.getdviolcon
def getdviolcon (whichsol, sub, viol)


The violation of the dual solution associated with the $$i$$-th constraint is computed as follows

$\max( \rho( (s_l^c)_i^*,(b_l^c)_i ),\ \rho( (s_u^c)_i^*, -(b_u^c)_i ),\ |-y_i+(s_l^c)_i^*-(s_u^c)_i^*| )$

where

$\begin{split}\rho(x,l) = \left\{ \begin{array}{ll} -x, & l > -\infty , \\ |x|, & \mbox{otherwise}.\\ \end{array} \right.\end{split}$

Both when the solution is a certificate of primal infeasibility or it is a dual feasible solution the violation should be small.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• sub (int[]) – An array of indexes of constraints. (input)
• viol (float[]) – viol[k] is the violation of dual solution associated with the constraint sub[k]. (output)
Groups:

Solution information

Task.getdviolcones
def getdviolcones (whichsol, sub, viol)


Let $$(s_n^x)^*$$ be the value of variable $$(s_n^x)$$ for the specified solution. For simplicity let us assume that $$s_n^x$$ is a member of a quadratic cone, then the violation is computed as follows

$\begin{split}\left\{ \begin{array}{ll} \max(0,(\|s_n^x\|_{2:n}^*-(s_n^x)_1^*) / \sqrt{2}, & (s_n^x)^* \geq -\|(s_n^x)_{2:n}^*\|, \\ \|(s_n^x)^*\|, & \mbox{otherwise.} \end{array} \right.\end{split}$

Both when the solution is a certificate of primal infeasibility or when it is a dual feasible solution the violation should be small.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• sub (int[]) – An array of indexes of conic constraints. (input)
• viol (float[]) – viol[k] is the violation of the dual solution associated with the conic constraint sub[k]. (output)
Groups:

Solution information

Task.getdviolvar
def getdviolvar (whichsol, sub, viol)


The violation of the dual solution associated with the $$j$$-th variable is computed as follows

$\max \left(\rho((s_l^x)_j^*,(b_l^x)_j),\ \rho((s_u^x)_j^*,-(b_u^x)_j),\ |\sum_{i=\idxbeg}^{\idxend{numcon}} a_{ij} y_i+(s_l^x)_j^*-(s_u^x)_j^* - \tau c_j| \right)$

where

$\begin{split}\rho(x,l) = \left\{ \begin{array}{ll} -x, & l > -\infty , \\ |x|, & \mbox{otherwise} \end{array} \right.\end{split}$

and $$\tau=0$$ if the solution is a certificate of primal infeasibility and $$\tau=1$$ otherwise. The formula for computing the violation is only shown for the linear case but is generalized appropriately for the more general problems. Both when the solution is a certificate of primal infeasibility or when it is a dual feasible solution the violation should be small.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• sub (int[]) – An array of indexes of $$x$$ variables. (input)
• viol (float[]) – viol[k] is the violation of dual solution associated with the variable sub[k]. (output)
Groups:

Solution information

Task.getintinf
def getintinf (whichiinf) -> ivalue


Obtains an integer information item from the task information database.

Parameters:
whichiinf (mosek.iinfitem) – Specifies an integer information item. (input)
Return:
ivalue (int) – The value of the required integer information item.
Groups:
Optimizer statistics
Task.getintparam
def getintparam (param) -> parvalue


Obtains the value of an integer parameter.

Parameters:
param (mosek.iparam) – Which parameter. (input)
Return:
parvalue (int) – Parameter value.
Groups:
Parameters (get)
Task.getlenbarvarj
def getlenbarvarj (j) -> lenbarvarj


Obtains the length of the $$j$$-th semidefinite variable i.e. the number of elements in the lower triangular part.

Parameters:
j (int) – Index of the semidefinite variable whose length if requested. (input)
Return:
lenbarvarj (int) – Number of scalar elements in the lower triangular part of the semidefinite variable.
Groups:
Scalar variable data
Task.getlintinf
def getlintinf (whichliinf) -> ivalue


Obtains a long integer information item from the task information database.

Parameters:
whichliinf (mosek.liinfitem) – Specifies a long information item. (input)
Return:
ivalue (int) – The value of the required long integer information item.
Groups:
Optimizer statistics
Task.getmaxnumanz
def getmaxnumanz () -> maxnumanz


Obtains number of preallocated non-zeros in $$A$$. When this number of non-zeros is reached MOSEK will automatically allocate more space for $$A$$.

Return:
maxnumanz (int) – Number of preallocated non-zero linear matrix elements.
Groups:
Scalar variable data
Task.getmaxnumbarvar
def getmaxnumbarvar () -> maxnumbarvar


Obtains maximum number of symmetric matrix variables for which space is currently preallocated.

Return:
maxnumbarvar (int) – Maximum number of symmetric matrix variables for which space is currently preallocated.
Groups:
Symmetric matrix variable data
Task.getmaxnumcon
def getmaxnumcon () -> maxnumcon


Obtains the number of preallocated constraints in the optimization task. When this number of constraints is reached MOSEK will automatically allocate more space for constraints.

Return:
maxnumcon (int) – Number of preallocated constraints in the optimization task.
Groups:
Linear constraint data
Task.getmaxnumcone
def getmaxnumcone () -> maxnumcone


Obtains the number of preallocated cones in the optimization task. When this number of cones is reached MOSEK will automatically allocate space for more cones.

Return:
maxnumcone (int) – Number of preallocated conic constraints in the optimization task.
Groups:
Task.getmaxnumqnz
def getmaxnumqnz () -> maxnumqnz


Obtains the number of preallocated non-zeros for $$Q$$ (both objective and constraints). When this number of non-zeros is reached MOSEK will automatically allocate more space for $$Q$$.

Return:
maxnumqnz (int) – Number of non-zero elements preallocated in quadratic coefficient matrices.
Groups:
Scalar variable data
Task.getmaxnumvar
def getmaxnumvar () -> maxnumvar


Obtains the number of preallocated variables in the optimization task. When this number of variables is reached MOSEK will automatically allocate more space for variables.

Return:
maxnumvar (int) – Number of preallocated variables in the optimization task.
Groups:
Scalar variable data
Task.getmemusage
def getmemusage () -> meminuse, maxmemuse


Return:

• meminuse (int) – Amount of memory currently used by the task.
• maxmemuse (int) – Maximum amount of memory used by the task until now.
Groups:

Memory

Task.getnumanz
def getnumanz () -> numanz


Obtains the number of non-zeros in $$A$$.

Return:
numanz (int) – Number of non-zero elements in the linear constraint matrix.
Groups:
Scalar variable data
Task.getnumanz64
def getnumanz64 () -> numanz


Obtains the number of non-zeros in $$A$$.

Return:
numanz (int) – Number of non-zero elements in the linear constraint matrix.
Groups:
Scalar variable data
Task.getnumbarablocktriplets
def getnumbarablocktriplets () -> num


Obtains an upper bound on the number of elements in the block triplet form of $$\barA$$.

Return:
num (int) – An upper bound on the number of elements in the block triplet form of $$\barA.$$
Groups:
Symmetric matrix variable data
Task.getnumbaranz
def getnumbaranz () -> nz


Get the number of nonzero elements in $$\barA$$.

Return:
nz (int) – The number of nonzero block elements in $$\barA$$ i.e. the number of $$\barA_{ij}$$ elements that are nonzero.
Groups:
Symmetric matrix variable data
Task.getnumbarcblocktriplets
def getnumbarcblocktriplets () -> num


Obtains an upper bound on the number of elements in the block triplet form of $$\barC$$.

Return:
num (int) – An upper bound on the number of elements in the block triplet form of $$\barC.$$
Groups:
Symmetric matrix variable data
Task.getnumbarcnz
def getnumbarcnz () -> nz


Obtains the number of nonzero elements in $$\barC$$.

Return:
nz (int) – The number of nonzeros in $$\barC$$ i.e. the number of elements $$\barC_j$$ that are nonzero.
Groups:
Symmetric matrix variable data
Task.getnumbarvar
def getnumbarvar () -> numbarvar


Obtains the number of semidefinite variables.

Return:
numbarvar (int) – Number of semidefinite variables in the problem.
Groups:
Symmetric matrix variable data
Task.getnumcon
def getnumcon () -> numcon


Obtains the number of constraints.

Return:
numcon (int) – Number of constraints.
Groups:
Linear constraint data
Task.getnumcone
def getnumcone () -> numcone


Obtains the number of cones.

Return:
numcone (int) – Number of conic constraints.
Groups:
Conic constraint data
Task.getnumconemem
def getnumconemem (k) -> nummem


Obtains the number of members in a cone.

Parameters:
k (int) – Index of the cone. (input)
Return:
nummem (int) – Number of member variables in the cone.
Groups:
Conic constraint data
Task.getnumintvar
def getnumintvar () -> numintvar


Obtains the number of integer-constrained variables.

Return:
numintvar (int) – Number of integer variables.
Groups:
Scalar variable data
Task.getnumparam
def getnumparam (partype) -> numparam


Obtains the number of parameters of a given type.

Parameters:
partype (mosek.parametertype) – Parameter type. (input)
Return:
numparam (int) – The number of parameters of type partype.
Groups:
Parameter management
Task.getnumqconknz
def getnumqconknz (k) -> numqcnz


Obtains the number of non-zero quadratic terms in a constraint.

Parameters:
k (int) – Index of the constraint for which the number quadratic terms should be obtained. (input)
Return:
numqcnz (int) – Number of quadratic terms.
Groups:
Scalar variable data
Task.getnumqobjnz
def getnumqobjnz () -> numqonz


Obtains the number of non-zero quadratic terms in the objective.

Return:
numqonz (int) – Number of non-zero elements in the quadratic objective terms.
Groups:
Scalar variable data
Task.getnumsymmat
def getnumsymmat () -> num


Obtains the number of symmetric matrices stored in the vector $$E$$.

Return:
num (int) – The number of symmetric sparse matrices.
Groups:
Scalar variable data
Task.getnumvar
def getnumvar () -> numvar


Obtains the number of variables.

Return:
numvar (int) – Number of variables.
Groups:
Scalar variable data
Task.getobjname
def getobjname () -> objname


Obtains the name assigned to the objective function.

Return:
objname (str) – Assigned the objective name.
Groups:
Naming
Task.getobjnamelen
def getobjnamelen () -> len


Obtains the length of the name assigned to the objective function.

Return:
len (int) – Assigned the length of the objective name.
Groups:
Naming
Task.getobjsense
def getobjsense () -> sense


Gets the objective sense of the task.

Return:
sense (mosek.objsense) – The returned objective sense.
Groups:
Objective data
Task.getprimalobj
def getprimalobj (whichsol) -> primalobj


Computes the primal objective value for the desired solution. Note that if the solution is an infeasibility certificate, then the fixed term in the objective is not included.

Parameters:
whichsol (mosek.soltype) – Selects a solution. (input)
Return:
primalobj (float) – Objective value corresponding to the primal solution.
Groups:
Solution information
Task.getprimalsolutionnorms
def getprimalsolutionnorms (whichsol) -> nrmxc, nrmxx, nrmbarx


Compute norms of the primal solution.

Parameters:

whichsol (mosek.soltype) – Selects a solution. (input)

Return:

• nrmxc (float) – The norm of the $$x^c$$ vector.
• nrmxx (float) – The norm of the $$x$$ vector.
• nrmbarx (float) – The norm of the $$\barX$$ vector.
Groups:

Solution information

Task.getprobtype
def getprobtype () -> probtype


Obtains the problem type.

Return:
probtype (mosek.problemtype) – The problem type.
Groups:
Task.getprosta
def getprosta (whichsol) -> prosta


Obtains the problem status.

Parameters:
whichsol (mosek.soltype) – Selects a solution. (input)
Return:
prosta (mosek.prosta) – Problem status.
Groups:
Solution information
Task.getpviolbarvar
def getpviolbarvar (whichsol, sub, viol)


Computes the primal solution violation for a set of semidefinite variables. Let $$(\barX_j)^*$$ be the value of the variable $$\barX_j$$ for the specified solution. Then the primal violation of the solution associated with variable $$\barX_j$$ is given by

$\max(-\lambda_{\min}(\barX_j),\ 0.0).$

Both when the solution is a certificate of dual infeasibility or when it is primal feasible the violation should be small.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• sub (int[]) – An array of indexes of $$\barX$$ variables. (input)
• viol (float[]) – viol[k] is how much the solution violates the constraint $$\barX_{\mathtt{sub}[k]} \in \PSD$$. (output)
Groups:

Solution information

Task.getpviolcon
def getpviolcon (whichsol, sub, viol)


Computes the primal solution violation for a set of constraints. The primal violation of the solution associated with the $$i$$-th constraint is given by

$\max(\tau l_i^c - (x_i^c)^*,\ (x_i^c)^* - \tau u_i^c),\ |\sum_{j=\idxbeg}^{\idxend{numvar}} a_{ij} x_j^* - x_i^c|)$

where $$\tau=0$$ if the solution is a certificate of dual infeasibility and $$\tau=1$$ otherwise. Both when the solution is a certificate of dual infeasibility and when it is primal feasible the violation should be small. The above formula applies for the linear case but is appropriately generalized in other cases.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• sub (int[]) – An array of indexes of constraints. (input)
• viol (float[]) – viol[k] is the violation associated with the solution for the constraint sub[k]. (output)
Groups:

Solution information

Task.getpviolcones
def getpviolcones (whichsol, sub, viol)


Computes the primal solution violation for a set of conic constraints. Let $$x^*$$ be the value of the variable $$x$$ for the specified solution. For simplicity let us assume that $$x$$ is a member of a quadratic cone, then the violation is computed as follows

$\begin{split}\left\{ \begin{array}{ll} \max(0,\|x_{2:n}\|-x_1) / \sqrt{2}, & x_1 \geq -\|x_{2:n}\|, \\ \|x\|, & \mbox{otherwise.} \end{array} \right.\end{split}$

Both when the solution is a certificate of dual infeasibility or when it is primal feasible the violation should be small.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• sub (int[]) – An array of indexes of conic constraints. (input)
• viol (float[]) – viol[k] is the violation of the solution associated with the conic constraint number sub[k]. (output)
Groups:

Solution information

Task.getpviolvar
def getpviolvar (whichsol, sub, viol)


Computes the primal solution violation associated to a set of variables. Let $$x_j^*$$ be the value of $$x_j$$ for the specified solution. Then the primal violation of the solution associated with variable $$x_j$$ is given by

$\max( \tau l_j^x - x_j^*,\ x_j^* - \tau u_j^x,\ 0).$

where $$\tau=0$$ if the solution is a certificate of dual infeasibility and $$\tau=1$$ otherwise. Both when the solution is a certificate of dual infeasibility and when it is primal feasible the violation should be small.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• sub (int[]) – An array of indexes of $$x$$ variables. (input)
• viol (float[]) – viol[k] is the violation associated with the solution for the variable $$x_\mathtt{sub[k]}$$. (output)
Groups:

Solution information

Task.getqconk
def getqconk (k, qcsubi, qcsubj, qcval) -> numqcnz


Obtains all the quadratic terms in a constraint. The quadratic terms are stored sequentially in qcsubi, qcsubj, and qcval.

Parameters:

• k (int) – Which constraint. (input)
• qcsubi (int[]) – Row subscripts for quadratic constraint matrix. (output)
• qcsubj (int[]) – Column subscripts for quadratic constraint matrix. (output)
• qcval (float[]) – Quadratic constraint coefficient values. (output)
Return:

numqcnz (int) – Number of quadratic terms.

Groups:

Scalar variable data

Task.getqobj
def getqobj (qosubi, qosubj, qoval) -> numqonz


Obtains the quadratic terms in the objective. The required quadratic terms are stored sequentially in qosubi, qosubj, and qoval.

Parameters:

• qosubi (int[]) – Row subscripts for quadratic objective coefficients. (output)
• qosubj (int[]) – Column subscripts for quadratic objective coefficients. (output)
• qoval (float[]) – Quadratic objective coefficient values. (output)
Return:

numqonz (int) – Number of non-zero elements in the quadratic objective terms.

Groups:

Scalar variable data

Task.getqobjij
def getqobjij (i, j) -> qoij


Obtains one coefficient $$q_{ij}^o$$ in the quadratic term of the objective.

Parameters:

• i (int) – Row index of the coefficient. (input)
• j (int) – Column index of coefficient. (input)
Return:

qoij (float) – The required coefficient.

Groups:

Scalar variable data

Task.getreducedcosts
def getreducedcosts (whichsol, first, last, redcosts)


Computes the reduced costs for a slice of variables and returns them in the array redcosts i.e.

(1)$\mathtt{redcosts}[j-\mathtt{first}] = (s_l^x)_j-(s_u^x)_j, ~j=\mathtt{first},\ldots,\mathtt{last}-1$
Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – The index of the first variable in the sequence. (input)
• last (int) – The index of the last variable in the sequence plus 1. (input)
• redcosts (float[]) – The reduced costs for the required slice of variables. (output)
Groups:

Solution (get)

Task.getskc
def getskc (whichsol, skc)


Obtains the status keys for the constraints.

Parameters:

Groups:

Solution (get)

Task.getskcslice
def getskcslice (whichsol, first, last, skc)


Obtains the status keys for a slice of the constraints.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• skc (mosek.stakey[]) – Status keys for the constraints. (output)
Groups:

Solution (get)

Task.getskx
def getskx (whichsol, skx)


Obtains the status keys for the scalar variables.

Parameters:

Groups:

Solution (get)

Task.getskxslice
def getskxslice (whichsol, first, last, skx)


Obtains the status keys for a slice of the scalar variables.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• skx (mosek.stakey[]) – Status keys for the variables. (output)
Groups:

Solution (get)

Task.getslc
def getslc (whichsol, slc)


Obtains the $$s_l^c$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• slc (float[]) – Dual variables corresponding to the lower bounds on the constraints. (output)
Groups:

Solution (get)

Task.getslcslice
def getslcslice (whichsol, first, last, slc)


Obtains a slice of the $$s_l^c$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• slc (float[]) – Dual variables corresponding to the lower bounds on the constraints. (output)
Groups:

Solution (get)

Task.getslx
def getslx (whichsol, slx)


Obtains the $$s_l^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• slx (float[]) – Dual variables corresponding to the lower bounds on the variables. (output)
Groups:

Solution (get)

Task.getslxslice
def getslxslice (whichsol, first, last, slx)


Obtains a slice of the $$s_l^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• slx (float[]) – Dual variables corresponding to the lower bounds on the variables. (output)
Groups:

Solution (get)

Task.getsnx
def getsnx (whichsol, snx)


Obtains the $$s_n^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• snx (float[]) – Dual variables corresponding to the conic constraints on the variables. (output)
Groups:

Solution (get)

Task.getsnxslice
def getsnxslice (whichsol, first, last, snx)


Obtains a slice of the $$s_n^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• snx (float[]) – Dual variables corresponding to the conic constraints on the variables. (output)
Groups:

Solution (get)

Task.getsolsta
def getsolsta (whichsol) -> solsta


Obtains the solution status.

Parameters:
whichsol (mosek.soltype) – Selects a solution. (input)
Return:
solsta (mosek.solsta) – Solution status.
Groups:
Solution information
Task.getsolution
def getsolution (whichsol, skc, skx, skn, xc, xx, y, slc, suc, slx, sux, snx) -> prosta, solsta


Obtains the complete solution.

Consider the case of linear programming. The primal problem is given by

$\begin{split}\begin{array}{lccccl} \mbox{minimize} & & & c^T x+c^f & & \\ \mbox{subject to} & l^c & \leq & A x & \leq & u^c, \\ & l^x & \leq & x & \leq & u^x. \\ \end{array}\end{split}$

and the corresponding dual problem is

$\begin{split}\begin{array}{lccl} \mbox{maximize} & (l^c)^T s_l^c - (u^c)^T s_u^c & \\ & + (l^x)^T s_l^x - (u^x)^T s_u^x + c^f & \\ \mbox{subject to} & A^T y + s_l^x - s_u^x & = & c, \\ & -y + s_l^c - s_u^c & = & 0, \\ & s_l^c,s_u^c,s_l^x,s_u^x \geq 0. & & \\ \end{array}\end{split}$

A conic optimization problem has the same primal variables as in the linear case. Recall that the dual of a conic optimization problem is given by:

$\begin{split}\begin{array}{lccccc} \mbox{maximize} & (l^c)^T s_l^c - (u^c)^T s_u^c & & \\ & +(l^x)^T s_l^x - (u^x)^T s_u^x + c^f & & \\ \mbox{subject to} & A^T y + s_l^x - s_u^x + s_n^x & = & c, \\ & -y + s_l^c - s_u^c & = & 0, \\ & s_l^c,s_u^c,s_l^x,s_u^x & \geq & 0, \\ & s_n^x \in \K^* & & \\ \end{array}\end{split}$

The mapping between variables and arguments to the function is as follows:

• xx : Corresponds to variable $$x$$ (also denoted $$x^x$$).
• xc : Corresponds to $$x^c:=Ax$$.
• y : Corresponds to variable $$y$$.
• slc: Corresponds to variable $$s_l^c$$.
• suc: Corresponds to variable $$s_u^c$$.
• slx: Corresponds to variable $$s_l^x$$.
• sux: Corresponds to variable $$s_u^x$$.
• snx: Corresponds to variable $$s_n^x$$.

The meaning of the values returned by this function depend on the solution status returned in the argument solsta. The most important possible values of solsta are:

In order to retrieve the primal and dual values of semidefinite variables see Task.getbarxj and Task.getbarsj.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• skc (mosek.stakey[]) – Status keys for the constraints. (output)
• skx (mosek.stakey[]) – Status keys for the variables. (output)
• skn (mosek.stakey[]) – Status keys for the conic constraints. (output)
• xc (float[]) – Primal constraint solution. (output)
• xx (float[]) – Primal variable solution. (output)
• y (float[]) – Vector of dual variables corresponding to the constraints. (output)
• slc (float[]) – Dual variables corresponding to the lower bounds on the constraints. (output)
• suc (float[]) – Dual variables corresponding to the upper bounds on the constraints. (output)
• slx (float[]) – Dual variables corresponding to the lower bounds on the variables. (output)
• sux (float[]) – Dual variables corresponding to the upper bounds on the variables. (output)
• snx (float[]) – Dual variables corresponding to the conic constraints on the variables. (output)
Return:

Groups:

Solution (get)

Task.getsolutioni Deprecated
def getsolutioni (accmode, i, whichsol) -> sk, x, sl, su, sn


Obtains the primal and dual solution information for a single constraint or variable.

Parameters:

• accmode (mosek.accmode) – Defines whether solution information for a constraint or for a variable is retrieved. (input)
• i (int) – Index of the constraint or variable. (input)
• whichsol (mosek.soltype) – Selects a solution. (input)
Return:

• sk (mosek.stakey) – Status key of the constraint of variable.
• x (float) – Solution value of the primal variable.
• sl (float) – Solution value of the dual variable associated with the lower bound.
• su (float) – Solution value of the dual variable associated with the upper bound.
• sn (float) – Solution value of the dual variable associated with the cone constraint.
Groups:

Solution (get)

Task.getsolutioninfo
def getsolutioninfo (whichsol) -> pobj, pviolcon, pviolvar, pviolbarvar, pviolcone, pviolitg, dobj, dviolcon, dviolvar, dviolbarvar, dviolcone


Parameters:

whichsol (mosek.soltype) – Selects a solution. (input)

Return:

• pobj (float) – The primal objective value as computed by Task.getprimalobj.
• pviolcon (float) – Maximal primal violation of the solution associated with the $$x^c$$ variables where the violations are computed by Task.getpviolcon.
• pviolvar (float) – Maximal primal violation of the solution for the $$x$$ variables where the violations are computed by Task.getpviolvar.
• pviolbarvar (float) – Maximal primal violation of solution for the $$\barX$$ variables where the violations are computed by Task.getpviolbarvar.
• pviolcone (float) – Maximal primal violation of solution for the conic constraints where the violations are computed by Task.getpviolcones.
• pviolitg (float) – Maximal violation in the integer constraints. The violation for an integer variable $$x_j$$ is given by $$\min(x_j-\lfloor x_j \rfloor,\lceil x_j \rceil - x_j)$$. This number is always zero for the interior-point and basic solutions.
• dobj (float) – Dual objective value as computed by Task.getdualobj.
• dviolcon (float) – Maximal violation of the dual solution associated with the $$x^c$$ variable as computed by Task.getdviolcon.
• dviolvar (float) – Maximal violation of the dual solution associated with the $$x$$ variable as computed by Task.getdviolvar.
• dviolbarvar (float) – Maximal violation of the dual solution associated with the $$\barS$$ variable as computed by Task.getdviolbarvar.
• dviolcone (float) – Maximal violation of the dual solution associated with the dual conic constraints as computed by Task.getdviolcones.
Groups:

Solution information

Task.getsolutionslice
def getsolutionslice (whichsol, solitem, first, last, values)


Obtains a slice of one item from the solution. The format of the solution is exactly as in Task.getsolution. The parameter solitem determines which of the solution vectors should be returned.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• solitem (mosek.solitem) – Which part of the solution is required. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• values (float[]) – The values in the required sequence are stored sequentially in values. (output)
Groups:

Solution (get)

Task.getsparsesymmat
def getsparsesymmat (idx, subi, subj, valij)


Get a single symmetric matrix from the matrix store.

Parameters:

• idx (int) – Index of the matrix to retrieve. (input)
• subi (int[]) – Row subscripts of the matrix non-zero elements. (output)
• subj (int[]) – Column subscripts of the matrix non-zero elements. (output)
• valij (float[]) – Coefficients of the matrix non-zero elements. (output)
Groups:

Scalar variable data

Task.getstrparam
def getstrparam (param) -> len, parvalue


Obtains the value of a string parameter.

Parameters:

param (mosek.sparam) – Which parameter. (input)

Return:

• len (int) – The length of the parameter value.
• parvalue (str) – Parameter value.
Groups:

Parameters (get)

Task.getstrparamlen
def getstrparamlen (param) -> len


Obtains the length of a string parameter.

Parameters:
param (mosek.sparam) – Which parameter. (input)
Return:
len (int) – The length of the parameter value.
Groups:
Parameters (get)
Task.getsuc
def getsuc (whichsol, suc)


Obtains the $$s_u^c$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• suc (float[]) – Dual variables corresponding to the upper bounds on the constraints. (output)
Groups:

Solution (get)

Task.getsucslice
def getsucslice (whichsol, first, last, suc)


Obtains a slice of the $$s_u^c$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• suc (float[]) – Dual variables corresponding to the upper bounds on the constraints. (output)
Groups:

Solution (get)

Task.getsux
def getsux (whichsol, sux)


Obtains the $$s_u^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• sux (float[]) – Dual variables corresponding to the upper bounds on the variables. (output)
Groups:

Solution (get)

Task.getsuxslice
def getsuxslice (whichsol, first, last, sux)


Obtains a slice of the $$s_u^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• sux (float[]) – Dual variables corresponding to the upper bounds on the variables. (output)
Groups:

Solution (get)

Task.getsymmatinfo
def getsymmatinfo (idx) -> dim, nz, type


MOSEK maintains a vector denoted by $$E$$ of symmetric data matrices. This function makes it possible to obtain important information about a single matrix in $$E$$.

Parameters:

idx (int) – Index of the matrix for which information is requested. (input)

Return:

• dim (int) – Returns the dimension of the requested matrix.
• nz (int) – Returns the number of non-zeros in the requested matrix.
• type (mosek.symmattype) – Returns the type of the requested matrix.
Groups:

Scalar variable data

Task.gettaskname
def gettaskname () -> taskname


Obtains the name assigned to the task.

Return:
taskname (str) – Returns the task name.
Groups:
Naming
Task.gettasknamelen
def gettasknamelen () -> len


Obtains the length the task name.

Return:
len (int) – Returns the length of the task name.
Groups:
Naming
Task.getvarbound
def getvarbound (i) -> bk, bl, bu


Obtains bound information for one variable.

Parameters:

i (int) – Index of the variable for which the bound information should be obtained. (input)

Return:

• bk (mosek.boundkey) – Bound keys.
• bl (float) – Values for lower bounds.
• bu (float) – Values for upper bounds.
Groups:

Bound data

Task.getvarboundslice
def getvarboundslice (first, last, bk, bl, bu)


Obtains bounds information for a slice of the variables.

Parameters:

• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• bk (mosek.boundkey[]) – Bound keys. (output)
• bl (float[]) – Values for lower bounds. (output)
• bu (float[]) – Values for upper bounds. (output)
Groups:

Bound data

Task.getvarname
def getvarname (j) -> name


Obtains the name of a variable.

Parameters:
j (int) – Index of a variable. (input)
Return:
name (str) – Returns the required name.
Groups:
Naming
Task.getvarnameindex
def getvarnameindex (somename) -> asgn, index


Checks whether the name somename has been assigned to any variable. If so, the index of the variable is reported.

Parameters:

somename (str) – The name which should be checked. (input)

Return:

• asgn (int) – Is non-zero if the name somename is assigned to a variable.
• index (int) – If the name somename is assigned to a variable, then index is the index of the variable.
Groups:

Naming

Task.getvarnamelen
def getvarnamelen (i) -> len


Obtains the length of the name of a variable.

Parameters:
i (int) – Index of a variable. (input)
Return:
len (int) – Returns the length of the indicated name.
Groups:
Naming
Task.getvartype
def getvartype (j) -> vartype


Gets the variable type of one variable.

Parameters:
j (int) – Index of the variable. (input)
Return:
vartype (mosek.variabletype) – Variable type of the $$j$$-th variable.
Groups:
Scalar variable data
Task.getvartypelist
def getvartypelist (subj, vartype)


Obtains the variable type of one or more variables. Upon return vartype[k] is the variable type of variable subj[k].

Parameters:

• subj (int[]) – A list of variable indexes. (input)
• vartype (mosek.variabletype[]) – The variables types corresponding to the variables specified by subj. (output)
Groups:

Scalar variable data

Task.getxc
def getxc (whichsol, xc)


Obtains the $$x^c$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• xc (float[]) – Primal constraint solution. (output)
Groups:

Solution (get)

Task.getxcslice
def getxcslice (whichsol, first, last, xc)


Obtains a slice of the $$x^c$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• xc (float[]) – Primal constraint solution. (output)
Groups:

Solution (get)

Task.getxx
def getxx (whichsol, xx)


Obtains the $$x^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• xx (float[]) – Primal variable solution. (output)
Groups:

Solution (get)

Task.getxxslice
def getxxslice (whichsol, first, last, xx)


Obtains a slice of the $$x^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• xx (float[]) – Primal variable solution. (output)
Groups:

Solution (get)

Task.gety
def gety (whichsol, y)


Obtains the $$y$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• y (float[]) – Vector of dual variables corresponding to the constraints. (output)
Groups:

Solution (get)

Task.getyslice
def getyslice (whichsol, first, last, y)


Obtains a slice of the $$y$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• y (float[]) – Vector of dual variables corresponding to the constraints. (output)
Groups:

Solution (get)

Task.initbasissolve
def initbasissolve (basis)


Prepare a task for use with the Task.solvewithbasis function.

This function should be called

If the basis is singular i.e. not invertible, then the error rescode.err_basis_singular is reported.

Parameters:
basis (int[]) – The array of basis indexes to use. The array is interpreted as follows: If $$\mathtt{basis}[i] \leq \idxend{numcon}$$, then $$x_{\mathtt{basis}[i]}^c$$ is in the basis at position $$i$$, otherwise $$x_{\mathtt{basis}[i]-\mathtt{numcon}}$$ is in the basis at position $$i$$. (output)
Groups:
Basis matrix
Task.inputdata
def inputdata (maxnumcon, maxnumvar, c, cfix, aptrb, aptre, asub, aval, bkc, blc, buc, bkx, blx, bux)


Input the linear part of an optimization problem.

The non-zeros of $$A$$ are inputted column-wise in the format described in Section Column or Row Ordered Sparse Matrix.

For an explained code example see Section Linear Optimization and Section Matrix Formats.

Parameters:

• maxnumcon (int) – Number of preallocated constraints in the optimization task. (input)
• maxnumvar (int) – Number of preallocated variables in the optimization task. (input)
• c (float[]) – Linear terms of the objective as a dense vector. The length is the number of variables. (input)
• cfix (float) – Fixed term in the objective. (input)
• aptrb (int[]) – Row or column start pointers. (input)
• aptre (int[]) – Row or column end pointers. (input)
• asub (int[]) – Coefficient subscripts. (input)
• aval (float[]) – Coefficient values. (input)
• bkc (mosek.boundkey[]) – Bound keys for the constraints. (input)
• blc (float[]) – Lower bounds for the constraints. (input)
• buc (float[]) – Upper bounds for the constraints. (input)
• bkx (mosek.boundkey[]) – Bound keys for the variables. (input)
• blx (float[]) – Lower bounds for the variables. (input)
• bux (float[]) – Upper bounds for the variables. (input)
Groups:

Task.isdouparname
def isdouparname (parname) -> param


Checks whether parname is a valid double parameter name.

Parameters:
parname (str) – Parameter name. (input)
Return:
param (mosek.dparam) – Returns the parameter corresponding to the name, if one exists.
Groups:
Parameter management
Task.isintparname
def isintparname (parname) -> param


Checks whether parname is a valid integer parameter name.

Parameters:
parname (str) – Parameter name. (input)
Return:
param (mosek.iparam) – Returns the parameter corresponding to the name, if one exists.
Groups:
Parameter management
Task.isstrparname
def isstrparname (parname) -> param


Checks whether parname is a valid string parameter name.

Parameters:
parname (str) – Parameter name. (input)
Return:
param (mosek.sparam) – Returns the parameter corresponding to the name, if one exists.
Groups:
Parameter management
Task.linkfiletostream
def linkfiletostream (whichstream, filename, append)


Directs all output from a task stream whichstream to a file filename.

Parameters:

• whichstream (mosek.streamtype) – Index of the stream. (input)
• filename (str) – A valid file name. (input)
• append (int) – If this argument is 0 the output file will be overwritten, otherwise it will be appended to. (input)
Groups:

Logging

Task.onesolutionsummary
def onesolutionsummary (whichstream, whichsol)


Prints a short summary of a specified solution.

Parameters:

Groups:

Task.optimize
def optimize () -> trmcode


Calls the optimizer. Depending on the problem type and the selected optimizer this will call one of the optimizers in MOSEK. By default the interior point optimizer will be selected for continuous problems. The optimizer may be selected manually by setting the parameter iparam.optimizer.

Return:
trmcode (mosek.rescode) – Is either rescode.ok or a termination response code.
Groups:
Optimization
Task.optimizermt
def optimizermt (server, port) -> trmcode


Offload the optimization task to a solver server defined by server:port. The call will block until a result is available or the connection closes.

If the string parameter sparam.remote_access_token is not blank, it will be passed to the server as authentication.

Parameters:

• server (str) – Name or IP address of the solver server. (input)
• port (str) – Network port of the solver server. (input)
Return:

trmcode (mosek.rescode) – Is either rescode.ok or a termination response code.

Task.optimizersummary
def optimizersummary (whichstream)


Prints a short summary with optimizer statistics from last optimization.

Parameters:
whichstream (mosek.streamtype) – Index of the stream. (input)
Groups:
Task.primalrepair
def primalrepair (wlc, wuc, wlx, wux)


The function repairs a primal infeasible optimization problem by adjusting the bounds on the constraints and variables where the adjustment is computed as the minimal weighted sum of relaxations to the bounds on the constraints and variables. Observe the function only repairs the problem but does not solve it. If an optimal solution is required the problem should be optimized after the repair.

The function is applicable to linear and conic problems possibly with integer variables.

Observe that when computing the minimal weighted relaxation the termination tolerance specified by the parameters of the task is employed. For instance the parameter iparam.mio_mode can be used to make MOSEK ignore the integer constraints during the repair which usually leads to a much faster repair. However, the drawback is of course that the repaired problem may not have an integer feasible solution.

Note the function modifies the task in place. If this is not desired, then apply the function to a cloned task.

Parameters:

• wlc (float[]) – $$(w_l^c)_i$$ is the weight associated with relaxing the lower bound on constraint $$i$$. If the weight is negative, then the lower bound is not relaxed. Moreover, if the argument is NULL, then all the weights are assumed to be $$1$$. (input)
• wuc (float[]) – $$(w_u^c)_i$$ is the weight associated with relaxing the upper bound on constraint $$i$$. If the weight is negative, then the upper bound is not relaxed. Moreover, if the argument is NULL, then all the weights are assumed to be $$1$$. (input)
• wlx (float[]) – $$(w_l^x)_j$$ is the weight associated with relaxing the lower bound on variable $$j$$. If the weight is negative, then the lower bound is not relaxed. Moreover, if the argument is NULL, then all the weights are assumed to be $$1$$. (input)
• wux (float[]) – $$(w_l^x)_i$$ is the weight associated with relaxing the upper bound on variable $$j$$. If the weight is negative, then the upper bound is not relaxed. Moreover, if the argument is NULL, then all the weights are assumed to be $$1$$. (input)
Groups:

Infeasibility diagnostics

Task.primalsensitivity
def primalsensitivity (subi, marki, subj, markj, leftpricei, rightpricei, leftrangei, rightrangei, leftpricej, rightpricej, leftrangej, rightrangej)


Calculates sensitivity information for bounds on variables and constraints. For details on sensitivity analysis, the definitions of shadow price and linearity interval and an example see Section Sensitivity Analysis.

The type of sensitivity analysis to be performed (basis or optimal partition) is controlled by the parameter iparam.sensitivity_type.

Parameters:

• subi (int[]) – Indexes of constraints to analyze. (input)
• marki (mosek.mark[]) – The value of marki[i] indicates for which bound of constraint subi[i] sensitivity analysis is performed. If marki[i] = mark.up the upper bound of constraint subi[i] is analyzed, and if marki[i] = mark.lo the lower bound is analyzed. If subi[i] is an equality constraint, either mark.lo or mark.up can be used to select the constraint for sensitivity analysis. (input)
• subj (int[]) – Indexes of variables to analyze. (input)
• markj (mosek.mark[]) – The value of markj[j] indicates for which bound of variable subj[j] sensitivity analysis is performed. If markj[j] = mark.up the upper bound of variable subj[j] is analyzed, and if markj[j] = mark.lo the lower bound is analyzed. If subj[j] is a fixed variable, either mark.lo or mark.up can be used to select the bound for sensitivity analysis. (input)
• leftpricei (float[]) – leftpricei[i] is the left shadow price for the bound marki[i] of constraint subi[i]. (output)
• rightpricei (float[]) – rightpricei[i] is the right shadow price for the bound marki[i] of constraint subi[i]. (output)
• leftrangei (float[]) – leftrangei[i] is the left range $$\beta_1$$ for the bound marki[i] of constraint subi[i]. (output)
• rightrangei (float[]) – rightrangei[i] is the right range $$\beta_2$$ for the bound marki[i] of constraint subi[i]. (output)
• leftpricej (float[]) – leftpricej[j] is the left shadow price for the bound markj[j] of variable subj[j]. (output)
• rightpricej (float[]) – rightpricej[j] is the right shadow price for the bound markj[j] of variable subj[j]. (output)
• leftrangej (float[]) – leftrangej[j] is the left range $$\beta_1$$ for the bound markj[j] of variable subj[j]. (output)
• rightrangej (float[]) – rightrangej[j] is the right range $$\beta_2$$ for the bound markj[j] of variable subj[j]. (output)
Groups:

Sensitivity analysis

Task.printdata
def printdata (whichstream, firsti, lasti, firstj, lastj, firstk, lastk, c, qo, a, qc, bc, bx, vartype, cones)


Prints a part of the problem data to a stream. This function is normally used for debugging purposes only, e.g. to verify that the correct data has been inputted.

Parameters:

• whichstream (mosek.streamtype) – Index of the stream. (input)
• firsti (int) – Index of first constraint for which data should be printed. (input)
• lasti (int) – Index of last constraint plus 1 for which data should be printed. (input)
• firstj (int) – Index of first variable for which data should be printed. (input)
• lastj (int) – Index of last variable plus 1 for which data should be printed. (input)
• firstk (int) – Index of first cone for which data should be printed. (input)
• lastk (int) – Index of last cone plus 1 for which data should be printed. (input)
• c (int) – If non-zero $$c$$ is printed. (input)
• qo (int) – If non-zero $$Q^o$$ is printed. (input)
• a (int) – If non-zero $$A$$ is printed. (input)
• qc (int) – If non-zero $$Q^k$$ is printed for the relevant constraints. (input)
• bc (int) – If non-zero the constraint bounds are printed. (input)
• bx (int) – If non-zero the variable bounds are printed. (input)
• vartype (int) – If non-zero the variable types are printed. (input)
• cones (int) – If non-zero the conic data is printed. (input)
Groups:

Task.putacol
def putacol (j, subj, valj)


Change one column of the linear constraint matrix $$A$$. Resets all the elements in column $$j$$ to zero and then sets

$a_{\mathtt{subj}[k],\mathtt{j}} = \mathtt{valj}[k], \quad k=0,\ldots,\mathtt{nzj}-1.$
Parameters:

• j (int) – Index of a column in $$A$$. (input)
• subj (int[]) – Row indexes of non-zero values in column $$j$$ of $$A$$. (input)
• valj (float[]) – New non-zero values of column $$j$$ in $$A$$. (input)
Groups:

Scalar variable data

Task.putacollist
def putacollist (sub, ptrb, ptre, asub, aval)


Change a set of columns in the linear constraint matrix $$A$$ with data in sparse triplet format. The requested columns are set to zero and then updated with:

$\begin{split}\begin{array}{rl} \mathtt{for} & i=\idxbeg,\ldots,\idxend{num}\\ & a_{\mathtt{asub}[k],\mathtt{sub}[i]} = \mathtt{aval}[k],\quad k=\mathtt{ptrb}[i],\ldots,\mathtt{ptre}[i]-1. \end{array}\end{split}$
Parameters:

• sub (int[]) – Indexes of columns that should be replaced, no duplicates. (input)
• ptrb (int[]) – Array of pointers to the first element in each column. (input)
• ptre (int[]) – Array of pointers to the last element plus one in each column. (input)
• asub (int[]) – Row indexes of new elements. (input)
• aval (float[]) – Coefficient values. (input)
Groups:

Scalar variable data

Task.putacolslice
def putacolslice (first, last, ptrb, ptre, asub, aval)


Change a slice of columns in the linear constraint matrix $$A$$ with data in sparse triplet format. The requested columns are set to zero and then updated with:

$\begin{split}\begin{array}{rl} \mathtt{for} & i=\mathtt{first},\ldots,\mathtt{last}-1\\ & a_{\mathtt{asub}[k],i} = \mathtt{aval}[k],\quad k=\mathtt{ptrb}[i],\ldots,\mathtt{ptre}[i]-1. \end{array}\end{split}$
Parameters:

• first (int) – First column in the slice. (input)
• last (int) – Last column plus one in the slice. (input)
• ptrb (int[]) – Array of pointers to the first element in each column. (input)
• ptre (int[]) – Array of pointers to the last element plus one in each column. (input)
• asub (int[]) – Row indexes of new elements. (input)
• aval (float[]) – Coefficient values. (input)
Groups:

Scalar variable data

Task.putaij
def putaij (i, j, aij)


Changes a coefficient in the linear coefficient matrix $$A$$ using the method

$a_{i,j} = \mathtt{aij}.$
Parameters:

• i (int) – Constraint (row) index. (input)
• j (int) – Variable (column) index. (input)
• aij (float) – New coefficient for $$a_{i,j}$$. (input)
Groups:

Scalar variable data

Task.putaijlist
def putaijlist (subi, subj, valij)


Changes one or more coefficients in $$A$$ using the method

$a_{\mathtt{subi[k]},\mathtt{subj[k]}} = \mathtt{valij[k]}, \quad k=\idxbeg,\ldots,\idxend{num}.$

Duplicates are not allowed.

Parameters:

• subi (int[]) – Constraint (row) indices. (input)
• subj (int[]) – Variable (column) indices. (input)
• valij (float[]) – New coefficient values for $$a_{i,j}$$. (input)
Groups:

Scalar variable data

Task.putarow
def putarow (i, subi, vali)


Change one row of the linear constraint matrix $$A$$. Resets all the elements in row $$i$$ to zero and then sets

$a_{\mathtt{i},\mathtt{subi}[k]} = \mathtt{vali}[k], \quad k=0,\ldots,\mathtt{nzi}-1.$
Parameters:

• i (int) – Index of a row in $$A$$. (input)
• subi (int[]) – Column indexes of non-zero values in row $$i$$ of $$A$$. (input)
• vali (float[]) – New non-zero values of row $$i$$ in $$A$$. (input)
Groups:

Scalar variable data

Task.putarowlist
def putarowlist (sub, ptrb, ptre, asub, aval)


Change a set of rows in the linear constraint matrix $$A$$ with data in sparse triplet format. The requested rows are set to zero and then updated with:

$\begin{split}\begin{array}{rl} \mathtt{for} & i=\idxbeg,\ldots,\idxend{num} \\ & a_{\mathtt{sub}[i],\mathtt{asub}[k]} = \mathtt{aval}[k],\quad k=\mathtt{ptrb}[i],\ldots,\mathtt{ptre}[i]-1. \end{array}\end{split}$
Parameters:

• sub (int[]) – Indexes of rows that should be replaced, no duplicates. (input)
• ptrb (int[]) – Array of pointers to the first element in each row. (input)
• ptre (int[]) – Array of pointers to the last element plus one in each row. (input)
• asub (int[]) – Column indexes of new elements. (input)
• aval (float[]) – Coefficient values. (input)
Groups:

Scalar variable data

Task.putarowslice
def putarowslice (first, last, ptrb, ptre, asub, aval)


Change a slice of rows in the linear constraint matrix $$A$$ with data in sparse triplet format. The requested columns are set to zero and then updated with:

$\begin{split}\begin{array}{rl} \mathtt{for} & i=\mathtt{first},\ldots,\mathtt{last}-1 \\ & a_{\mathtt{sub}[i],\mathtt{asub}[k]} = \mathtt{aval}[k],\quad k=\mathtt{ptrb}[i],\ldots,\mathtt{ptre}[i]-1. \end{array}\end{split}$
Parameters:

• first (int) – First row in the slice. (input)
• last (int) – Last row plus one in the slice. (input)
• ptrb (int[]) – Array of pointers to the first element in each row. (input)
• ptre (int[]) – Array of pointers to the last element plus one in each row. (input)
• asub (int[]) – Column indexes of new elements. (input)
• aval (float[]) – Coefficient values. (input)
Groups:

Scalar variable data

Task.putbarablocktriplet
def putbarablocktriplet (num, subi, subj, subk, subl, valijkl)


Inputs the $$\barA$$ matrix in block triplet form.

Parameters:

• num (int) – Number of elements in the block triplet form. (input)
• subi (int[]) – Constraint index. (input)
• subj (int[]) – Symmetric matrix variable index. (input)
• subk (int[]) – Block row index. (input)
• subl (int[]) – Block column index. (input)
• valijkl (float[]) – The numerical value associated with each block triplet. (input)
Groups:

Symmetric matrix variable data

Task.putbaraij
def putbaraij (i, j, sub, weights)


This function sets one element in the $$\barA$$ matrix.

Each element in the $$\barA$$ matrix is a weighted sum of symmetric matrices from the symmetric matrix storage $$E$$, so $$\barA_{ij}$$ is a symmetric matrix. By default all elements in $$\barA$$ are 0, so only non-zero elements need be added. Setting the same element again will overwrite the earlier entry.

The symmetric matrices from $$E$$ are defined separately using the function Task.appendsparsesymmat.

Parameters:

• i (int) – Row index of $$\barA$$. (input)
• j (int) – Column index of $$\barA$$. (input)
• sub (int[]) – Indices in $$E$$ of the matrices appearing in the weighted sum for $$\barA_{ij}$$. (input)
• weights (float[]) – weights[k] is the coefficient of the sub[k]-th element of $$E$$ in the weighted sum forming $$\barA_{ij}$$. (input)
Groups:

Symmetric matrix variable data

Task.putbarcblocktriplet
def putbarcblocktriplet (num, subj, subk, subl, valjkl)


Inputs the $$\barC$$ matrix in block triplet form.

Parameters:

• num (int) – Number of elements in the block triplet form. (input)
• subj (int[]) – Symmetric matrix variable index. (input)
• subk (int[]) – Block row index. (input)
• subl (int[]) – Block column index. (input)
• valjkl (float[]) – The numerical value associated with each block triplet. (input)
Groups:

Symmetric matrix variable data

Task.putbarcj
def putbarcj (j, sub, weights)


This function sets one entry in the $$\barC$$ vector.

Each element in the $$\barC$$ vector is a weighted sum of symmetric matrices from the symmetric matrix storage $$E$$, so $$\barC_{j}$$ is a symmetric matrix. By default all elements in $$\barC$$ are 0, so only non-zero elements need be added. Setting the same element again will overwrite the earlier entry.

The symmetric matrices from $$E$$ are defined separately using the function Task.appendsparsesymmat.

Parameters:

• j (int) – Index of the element in $$\barC$$ that should be changed. (input)
• sub (int[]) – Indices in $$E$$ of matrices appearing in the weighted sum for $$\barC_j$$ (input)
• weights (float[]) – weights[k] is the coefficient of the sub[k]-th element of $$E$$ in the weighted sum forming $$\barC_j$$. (input)
Groups:

Symmetric matrix variable data

Task.putbarsj
def putbarsj (whichsol, j, barsj)


Sets the dual solution for a semidefinite variable.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• j (int) – Index of the semidefinite variable. (input)
• barsj (float[]) – Value of $$\barS_j$$. Format as in Task.getbarsj. (input)
Groups:

Solution (put)

Task.putbarvarname
def putbarvarname (j, name)


Sets the name of a semidefinite variable.

Parameters:

• j (int) – Index of the variable. (input)
• name (str) – The variable name. (input)
Groups:

Naming

Task.putbarxj
def putbarxj (whichsol, j, barxj)


Sets the primal solution for a semidefinite variable.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• j (int) – Index of the semidefinite variable. (input)
• barxj (float[]) – Value of $$\barX_j$$. Format as in Task.getbarxj. (input)
Groups:

Solution (put)

Task.putbound Deprecated
def putbound (accmode, i, bk, bl, bu)


Changes the bound for either one constraint or one variable.

Parameters:

Groups:

Bound data

Task.putboundlist Deprecated
def putboundlist (accmode, sub, bk, bl, bu)


Changes the bounds of constraints or variables.

Parameters:

• accmode (mosek.accmode) – Defines whether bounds for constraints (accmode.con) or variables (accmode.var) are changed. (input)
• sub (int[]) – Subscripts of the constraints or variables that should be changed. (input)
• bk (mosek.boundkey[]) – Bound keys. (input)
• bl (float[]) – Values for lower bounds. (input)
• bu (float[]) – Values for upper bounds. (input)
Groups:

Bound data

Task.putboundslice Deprecated
def putboundslice (con, first, last, bk, bl, bu)


Changes the bounds for a slice of constraints or variables.

Parameters:

• con (mosek.accmode) – Defines whether bounds for constraints (accmode.con) or variables (accmode.var) are changed. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• bk (mosek.boundkey[]) – Bound keys. (input)
• bl (float[]) – Values for lower bounds. (input)
• bu (float[]) – Values for upper bounds. (input)
Groups:

Bound data

Task.putcfix
def putcfix (cfix)


Replaces the fixed term in the objective by a new one.

Parameters:
cfix (float) – Fixed term in the objective. (input)
Groups:
Objective data
Task.putcj
def putcj (j, cj)


Modifies one coefficient in the linear objective vector $$c$$, i.e.

$c_{\mathtt{j}} = \mathtt{cj}.$

If the absolute value exceeds dparam.data_tol_c_huge an error is generated. If the absolute value exceeds dparam.data_tol_cj_large, a warning is generated, but the coefficient is inputted as specified.

Parameters:

• j (int) – Index of the variable for which $$c$$ should be changed. (input)
• cj (float) – New value of $$c_j$$. (input)
Groups:

Scalar variable data

Task.putclist
def putclist (subj, val)


Modifies the coefficients in the linear term $$c$$ in the objective using the principle

$c_{\mathtt{subj[t]}} = \mathtt{val[t]}, \quad t=\idxbeg,\ldots,\idxend{num}.$

If a variable index is specified multiple times in subj only the last entry is used. Data checks are performed as in Task.putcj.

Parameters:

• subj (int[]) – Indices of variables for which the coefficient in $$c$$ should be changed. (input)
• val (float[]) – New numerical values for coefficients in $$c$$ that should be modified. (input)
Groups:

Scalar variable data

Task.putconbound
def putconbound (i, bk, bl, bu)


Changes the bounds for one constraint.

If the bound value specified is numerically larger than dparam.data_tol_bound_inf it is considered infinite and the bound key is changed accordingly. If a bound value is numerically larger than dparam.data_tol_bound_wrn, a warning will be displayed, but the bound is inputted as specified.

Parameters:

• i (int) – Index of the constraint. (input)
• bk (mosek.boundkey) – New bound key. (input)
• bl (float) – New lower bound. (input)
• bu (float) – New upper bound. (input)
Groups:

Bound data

Task.putconboundlist
def putconboundlist (sub, bk, bl, bu)


Changes the bounds for a list of constraints. If multiple bound changes are specified for a constraint, then only the last change takes effect. Data checks are performed as in Task.putconbound.

Parameters:

• sub (int[]) – List of constraint indexes. (input)
• bk (mosek.boundkey[]) – Bound keys. (input)
• bl (float[]) – Values for lower bounds. (input)
• bu (float[]) – Values for upper bounds. (input)
Groups:

Bound data

Task.putconboundslice
def putconboundslice (first, last, bk, bl, bu)


Changes the bounds for a slice of the constraints. Data checks are performed as in Task.putconbound.

Parameters:

• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• bk (mosek.boundkey[]) – Bound keys. (input)
• bl (float[]) – Values for lower bounds. (input)
• bu (float[]) – Values for upper bounds. (input)
Groups:

Task.putcone
def putcone (k, ct, conepar, submem)


Replaces a conic constraint.

Parameters:

• k (int) – Index of the cone. (input)
• ct (mosek.conetype) – Specifies the type of the cone. (input)
• conepar (float) – This argument is currently not used. It can be set to 0 (input)
• submem (int[]) – Variable subscripts of the members in the cone. (input)
Groups:

Conic constraint data

Task.putconename
def putconename (j, name)


Sets the name of a cone.

Parameters:

• j (int) – Index of the cone. (input)
• name (str) – The name of the cone. (input)
Groups:

Naming

Task.putconname
def putconname (i, name)


Sets the name of a constraint.

Parameters:

• i (int) – Index of the constraint. (input)
• name (str) – The name of the constraint. (input)
Groups:

Naming

Task.putcslice
def putcslice (first, last, slice)


Modifies a slice in the linear term $$c$$ in the objective using the principle

$c_{\mathtt{j}} = \mathtt{slice[j-first]}, \quad j=first,..,\idxend{last}$

Data checks are performed as in Task.putcj.

Parameters:

• first (int) – First element in the slice of $$c$$. (input)
• last (int) – Last element plus 1 of the slice in $$c$$ to be changed. (input)
• slice (float[]) – New numerical values for coefficients in $$c$$ that should be modified. (input)
Groups:

Scalar variable data

Task.putdouparam
def putdouparam (param, parvalue)


Sets the value of a double parameter.

Parameters:

• param (mosek.dparam) – Which parameter. (input)
• parvalue (float) – Parameter value. (input)
Groups:

Parameters (put)

Task.putintparam
def putintparam (param, parvalue)


Sets the value of an integer parameter.

Parameters:

• param (mosek.iparam) – Which parameter. (input)
• parvalue (int) – Parameter value. (input)
Groups:

Parameters (put)

Task.putmaxnumanz
def putmaxnumanz (maxnumanz)


Sets the number of preallocated non-zero entries in $$A$$.

MOSEK stores only the non-zero elements in the linear coefficient matrix $$A$$ and it cannot predict how much storage is required to store $$A$$. Using this function it is possible to specify the number of non-zeros to preallocate for storing $$A$$.

If the number of non-zeros in the problem is known, it is a good idea to set maxnumanz slightly larger than this number, otherwise a rough estimate can be used. In general, if $$A$$ is inputted in many small chunks, setting this value may speed up the data input phase.

It is not mandatory to call this function, since MOSEK will reallocate internal structures whenever it is necessary.

The function call has no effect if both maxnumcon and maxnumvar are zero.

Parameters:
maxnumanz (int) – Number of preallocated non-zeros in $$A$$. (input)
Groups:
Scalar variable data
Task.putmaxnumbarvar
def putmaxnumbarvar (maxnumbarvar)


Sets the number of preallocated symmetric matrix variables in the optimization task. When this number of variables is reached MOSEK will automatically allocate more space for variables.

It is not mandatory to call this function. It only gives a hint about the amount of data to preallocate for efficiency reasons.

Please note that maxnumbarvar must be larger than the current number of symmetric matrix variables in the task.

Parameters:
maxnumbarvar (int) – Number of preallocated symmetric matrix variables. (input)
Groups:
Symmetric matrix variable data
Task.putmaxnumcon
def putmaxnumcon (maxnumcon)


Sets the number of preallocated constraints in the optimization task. When this number of constraints is reached MOSEK will automatically allocate more space for constraints.

It is never mandatory to call this function, since MOSEK will reallocate any internal structures whenever it is required.

Please note that maxnumcon must be larger than the current number of constraints in the task.

Parameters:
maxnumcon (int) – Number of preallocated constraints in the optimization task. (input)
Groups:
Task.putmaxnumcone
def putmaxnumcone (maxnumcone)


Sets the number of preallocated conic constraints in the optimization task. When this number of conic constraints is reached MOSEK will automatically allocate more space for conic constraints.

It is not mandatory to call this function, since MOSEK will reallocate any internal structures whenever it is required.

Please note that maxnumcon must be larger than the current number of conic constraints in the task.

Parameters:
maxnumcone (int) – Number of preallocated conic constraints in the optimization task. (input)
Groups:
Task.putmaxnumqnz
def putmaxnumqnz (maxnumqnz)


Sets the number of preallocated non-zero entries in quadratic terms.

MOSEK stores only the non-zero elements in $$Q$$. Therefore, MOSEK cannot predict how much storage is required to store $$Q$$. Using this function it is possible to specify the number non-zeros to preallocate for storing $$Q$$ (both objective and constraints).

It may be advantageous to reserve more non-zeros for $$Q$$ than actually needed since it may improve the internal efficiency of MOSEK, however, it is never worthwhile to specify more than the double of the anticipated number of non-zeros in $$Q$$.

It is not mandatory to call this function, since MOSEK will reallocate internal structures whenever it is necessary.

Parameters:
maxnumqnz (int) – Number of non-zero elements preallocated in quadratic coefficient matrices. (input)
Groups:
Scalar variable data
Task.putmaxnumvar
def putmaxnumvar (maxnumvar)


Sets the number of preallocated variables in the optimization task. When this number of variables is reached MOSEK will automatically allocate more space for variables.

It is not mandatory to call this function. It only gives a hint about the amount of data to preallocate for efficiency reasons.

Please note that maxnumvar must be larger than the current number of variables in the task.

Parameters:
maxnumvar (int) – Number of preallocated variables in the optimization task. (input)
Groups:
Scalar variable data
Task.putnadouparam
def putnadouparam (paramname, parvalue)


Sets the value of a named double parameter.

Parameters:

• paramname (str) – Name of a parameter. (input)
• parvalue (float) – Parameter value. (input)
Groups:

Parameters (put)

Task.putnaintparam
def putnaintparam (paramname, parvalue)


Sets the value of a named integer parameter.

Parameters:

• paramname (str) – Name of a parameter. (input)
• parvalue (int) – Parameter value. (input)
Groups:

Parameters (put)

Task.putnastrparam
def putnastrparam (paramname, parvalue)


Sets the value of a named string parameter.

Parameters:

• paramname (str) – Name of a parameter. (input)
• parvalue (str) – Parameter value. (input)
Groups:

Parameters (put)

Task.putobjname
def putobjname (objname)


Assigns a new name to the objective.

Parameters:
objname (str) – Name of the objective. (input)
Groups:
Naming
Task.putobjsense
def putobjsense (sense)


Sets the objective sense of the task.

Parameters:
sense (mosek.objsense) – The objective sense of the task. The values objsense.maximize and objsense.minimize mean that the problem is maximized or minimized respectively. (input)
Groups:
Objective data
Task.putparam
def putparam (parname, parvalue)


Checks if parname is valid parameter name. If it is, the parameter is assigned the value specified by parvalue.

Parameters:

• parname (str) – Parameter name. (input)
• parvalue (str) – Parameter value. (input)
Groups:

Parameters (put)

Task.putqcon
def putqcon (qcsubk, qcsubi, qcsubj, qcval)


Replace all quadratic entries in the constraints. The list of constraints has the form

$l_k^c \leq \half \sum_{i=0}^{\idxend{numvar}} \sum_{j=0}^{\idxend{numvar}} q_{ij}^k x_i x_j + \sum_{j=0}^{\idxend{numvar}} a_{kj} x_j \leq u_k^c, ~\ k=0,\ldots,m-1.$

This function sets all the quadratic terms to zero and then performs the update:

$q_{\mathtt{qcsubi[t]},\mathtt{qcsubj[t]}}^{\mathtt{qcsubk[t]}} = q_{\mathtt{\mathtt{qcsubj[t]},qcsubi[t]}}^{\mathtt{qcsubk[t]}} = q_{\mathtt{\mathtt{qcsubj[t]},qcsubi[t]}}^{\mathtt{qcsubk[t]}} + \mathtt{qcval[t]},$

for $$t=\idxbeg,\ldots,\idxend{numqcnz}$$.

• For large problems it is essential for the efficiency that the function Task.putmaxnumqnz is employed to pre-allocate space.
• Only the lower triangular parts should be specified because the $$Q$$ matrices are symmetric. Specifying entries where $$i < j$$ will result in an error.
• Only non-zero elements should be specified.
• The order in which the non-zero elements are specified is insignificant.
• Duplicate elements are added together as shown above. Hence, it is usually not recommended to specify the same entry multiple times.

For a code example see Section Quadratic Optimization

Parameters:

• qcsubk (int[]) – Constraint subscripts for quadratic coefficients. (input)
• qcsubi (int[]) – Row subscripts for quadratic constraint matrix. (input)
• qcsubj (int[]) – Column subscripts for quadratic constraint matrix. (input)
• qcval (float[]) – Quadratic constraint coefficient values. (input)
Groups:

Scalar variable data

Task.putqconk
def putqconk (k, qcsubi, qcsubj, qcval)


Replaces all the quadratic entries in one constraint. This function performs the same operations as Task.putqcon but only with respect to constraint number k and it does not modify the other constraints. See the description of Task.putqcon for definitions and important remarks.

Parameters:

• k (int) – The constraint in which the new $$Q$$ elements are inserted. (input)
• qcsubi (int[]) – Row subscripts for quadratic constraint matrix. (input)
• qcsubj (int[]) – Column subscripts for quadratic constraint matrix. (input)
• qcval (float[]) – Quadratic constraint coefficient values. (input)
Groups:

Scalar variable data

Task.putqobj
def putqobj (qosubi, qosubj, qoval)


Replace all quadratic terms in the objective. If the objective has the form

$\half \sum_{i=0}^{\idxend{numvar}} \sum_{j=0}^{\idxend{numvar}} q_{ij}^o x_i x_j + \sum_{j=0}^{\idxend{numvar}} c_{j} x_j + c^f$

then this function sets all the quadratic terms to zero and then performs the update:

$q_{\mathtt{qosubi[t]},\mathtt{qosubj[t]}}^{o} = q_{\mathtt{\mathtt{qosubj[t]},qosubi[t]}}^{o} = q_{\mathtt{\mathtt{qosubj[t]},qosubi[t]}}^{o} + \mathtt{qoval[t]},$

for $$t=\idxbeg,\ldots,\idxend{numqonz}$$.

See the description of Task.putqcon for important remarks and example.

Parameters:

• qosubi (int[]) – Row subscripts for quadratic objective coefficients. (input)
• qosubj (int[]) – Column subscripts for quadratic objective coefficients. (input)
• qoval (float[]) – Quadratic objective coefficient values. (input)
Groups:

Scalar variable data

Task.putqobjij
def putqobjij (i, j, qoij)


Replaces one coefficient in the quadratic term in the objective. The function performs the assignment

$q_{ij}^o = q_{ji}^o = \mathtt{qoij}.$

Only the elements in the lower triangular part are accepted. Setting $$q_{ij}$$ with $$j>i$$ will cause an error.

Please note that replacing all quadratic elements one by one is more computationally expensive than replacing them all at once. Use Task.putqobj instead whenever possible.

Parameters:

• i (int) – Row index for the coefficient to be replaced. (input)
• j (int) – Column index for the coefficient to be replaced. (input)
• qoij (float) – The new value for $$q_{ij}^o$$. (input)
Groups:

Scalar variable data

Task.putskc
def putskc (whichsol, skc)


Sets the status keys for the constraints.

Parameters:

Groups:

Solution (put)

Task.putskcslice
def putskcslice (whichsol, first, last, skc)


Sets the status keys for a slice of the constraints.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• skc (mosek.stakey[]) – Status keys for the constraints. (input)
Groups:

Solution (put)

Task.putskx
def putskx (whichsol, skx)


Sets the status keys for the scalar variables.

Parameters:

Groups:

Solution (put)

Task.putskxslice
def putskxslice (whichsol, first, last, skx)


Sets the status keys for a slice of the variables.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• skx (mosek.stakey[]) – Status keys for the variables. (input)
Groups:

Solution (put)

Task.putslc
def putslc (whichsol, slc)


Sets the $$s_l^c$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• slc (float[]) – Dual variables corresponding to the lower bounds on the constraints. (input)
Groups:

Solution (put)

Task.putslcslice
def putslcslice (whichsol, first, last, slc)


Sets a slice of the $$s_l^c$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• slc (float[]) – Dual variables corresponding to the lower bounds on the constraints. (input)
Groups:

Solution (put)

Task.putslx
def putslx (whichsol, slx)


Sets the $$s_l^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• slx (float[]) – Dual variables corresponding to the lower bounds on the variables. (input)
Groups:

Solution (put)

Task.putslxslice
def putslxslice (whichsol, first, last, slx)


Sets a slice of the $$s_l^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• slx (float[]) – Dual variables corresponding to the lower bounds on the variables. (input)
Groups:

Solution (put)

Task.putsnx
def putsnx (whichsol, sux)


Sets the $$s_n^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• sux (float[]) – Dual variables corresponding to the upper bounds on the variables. (input)
Groups:

Solution (put)

Task.putsnxslice
def putsnxslice (whichsol, first, last, snx)


Sets a slice of the $$s_n^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• snx (float[]) – Dual variables corresponding to the conic constraints on the variables. (input)
Groups:

Solution (put)

Task.putsolution
def putsolution (whichsol, skc, skx, skn, xc, xx, y, slc, suc, slx, sux, snx)


Inserts a solution into the task.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• skc (mosek.stakey[]) – Status keys for the constraints. (input)
• skx (mosek.stakey[]) – Status keys for the variables. (input)
• skn (mosek.stakey[]) – Status keys for the conic constraints. (input)
• xc (float[]) – Primal constraint solution. (input)
• xx (float[]) – Primal variable solution. (input)
• y (float[]) – Vector of dual variables corresponding to the constraints. (input)
• slc (float[]) – Dual variables corresponding to the lower bounds on the constraints. (input)
• suc (float[]) – Dual variables corresponding to the upper bounds on the constraints. (input)
• slx (float[]) – Dual variables corresponding to the lower bounds on the variables. (input)
• sux (float[]) – Dual variables corresponding to the upper bounds on the variables. (input)
• snx (float[]) – Dual variables corresponding to the conic constraints on the variables. (input)
Groups:

Solution (put)

Task.putsolutioni Deprecated
def putsolutioni (accmode, i, whichsol, sk, x, sl, su, sn)


Sets the primal and dual solution information for a single constraint or variable.

Parameters:

• accmode (mosek.accmode) – Defines whether solution information for a constraint (accmode.con) or for a variable (accmode.var) is modified. (input)
• i (int) – Index of the constraint or variable. (input)
• whichsol (mosek.soltype) – Selects a solution. (input)
• sk (mosek.stakey) – Status key of the constraint or variable. (input)
• x (float) – Solution value of the primal constraint or variable. (input)
• sl (float) – Solution value of the dual variable associated with the lower bound. (input)
• su (float) – Solution value of the dual variable associated with the upper bound. (input)
• sn (float) – Solution value of the dual variable associated with the conic constraint. (input)
Groups:

Solution (put)

Task.putsolutionyi
def putsolutionyi (i, whichsol, y)


Inputs the dual variable of a solution.

Parameters:

• i (int) – Index of the dual variable. (input)
• whichsol (mosek.soltype) – Selects a solution. (input)
• y (float) – Solution value of the dual variable. (input)
Task.putstrparam
def putstrparam (param, parvalue)


Sets the value of a string parameter.

Parameters:

• param (mosek.sparam) – Which parameter. (input)
• parvalue (str) – Parameter value. (input)
Groups:

Parameters (put)

Task.putsuc
def putsuc (whichsol, suc)


Sets the $$s_u^c$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• suc (float[]) – Dual variables corresponding to the upper bounds on the constraints. (input)
Groups:

Solution (put)

Task.putsucslice
def putsucslice (whichsol, first, last, suc)


Sets a slice of the $$s_u^c$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• suc (float[]) – Dual variables corresponding to the upper bounds on the constraints. (input)
Groups:

Solution (put)

Task.putsux
def putsux (whichsol, sux)


Sets the $$s_u^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• sux (float[]) – Dual variables corresponding to the upper bounds on the variables. (input)
Groups:

Solution (put)

Task.putsuxslice
def putsuxslice (whichsol, first, last, sux)


Sets a slice of the $$s_u^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• sux (float[]) – Dual variables corresponding to the upper bounds on the variables. (input)
Groups:

Solution (put)

Task.puttaskname
def puttaskname (taskname)


Assigns a new name to the task.

Parameters:
taskname (str) – Name assigned to the task. (input)
Groups:
Naming
Task.putvarbound
def putvarbound (j, bk, bl, bu)


Changes the bounds for one variable.

If the bound value specified is numerically larger than dparam.data_tol_bound_inf it is considered infinite and the bound key is changed accordingly. If a bound value is numerically larger than dparam.data_tol_bound_wrn, a warning will be displayed, but the bound is inputted as specified.

Parameters:

• j (int) – Index of the variable. (input)
• bk (mosek.boundkey) – New bound key. (input)
• bl (float) – New lower bound. (input)
• bu (float) – New upper bound. (input)
Groups:

Bound data

Task.putvarboundlist
def putvarboundlist (sub, bkx, blx, bux)


Changes the bounds for one or more variables. If multiple bound changes are specified for a variable, then only the last change takes effect. Data checks are performed as in Task.putvarbound.

Parameters:

• sub (int[]) – List of variable indexes. (input)
• bkx (mosek.boundkey[]) – Bound keys for the variables. (input)
• blx (float[]) – Lower bounds for the variables. (input)
• bux (float[]) – Upper bounds for the variables. (input)
Groups:

Bound data

Task.putvarboundslice
def putvarboundslice (first, last, bk, bl, bu)


Changes the bounds for a slice of the variables. Data checks are performed as in Task.putvarbound.

Parameters:

• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• bk (mosek.boundkey[]) – Bound keys. (input)
• bl (float[]) – Values for lower bounds. (input)
• bu (float[]) – Values for upper bounds. (input)
Groups:

Scalar variable data

Task.putvarname
def putvarname (j, name)


Sets the name of a variable.

Parameters:

• j (int) – Index of the variable. (input)
• name (str) – The variable name. (input)
Groups:

Naming

Task.putvartype
def putvartype (j, vartype)


Sets the variable type of one variable.

Parameters:

• j (int) – Index of the variable. (input)
• vartype (mosek.variabletype) – The new variable type. (input)
Groups:

Scalar variable data

Task.putvartypelist
def putvartypelist (subj, vartype)


Sets the variable type for one or more variables. If the same index is specified multiple times in subj only the last entry takes effect.

Parameters:

• subj (int[]) – A list of variable indexes for which the variable type should be changed. (input)
• vartype (mosek.variabletype[]) – A list of variable types that should be assigned to the variables specified by subj. (input)
Groups:

Scalar variable data

Task.putxc
def putxc (whichsol, xc)


Sets the $$x^c$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• xc (float[]) – Primal constraint solution. (output)
Groups:

Solution (put)

Task.putxcslice
def putxcslice (whichsol, first, last, xc)


Sets a slice of the $$x^c$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• xc (float[]) – Primal constraint solution. (input)
Groups:

Solution (put)

Task.putxx
def putxx (whichsol, xx)


Sets the $$x^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• xx (float[]) – Primal variable solution. (input)
Groups:

Solution (put)

Task.putxxslice
def putxxslice (whichsol, first, last, xx)


Obtains a slice of the $$x^x$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• xx (float[]) – Primal variable solution. (input)
Groups:

Solution (put)

Task.puty
def puty (whichsol, y)


Sets the $$y$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• y (float[]) – Vector of dual variables corresponding to the constraints. (input)
Groups:

Solution (put)

Task.putyslice
def putyslice (whichsol, first, last, y)


Sets a slice of the $$y$$ vector for a solution.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• first (int) – First index in the sequence. (input)
• last (int) – Last index plus 1 in the sequence. (input)
• y (float[]) – Vector of dual variables corresponding to the constraints. (input)
Groups:

Solution (put)

Task.readdata
def readdata (filename)


Reads an optimization problem and associated data from a file.

Parameters:
filename (str) – A valid file name. (input)
Groups:
Data file
Task.readdataformat
def readdataformat (filename, format, compress)


Reads an optimization problem and associated data from a file.

Parameters:

Groups:

Data file

Task.readparamfile
def readparamfile (filename)


Reads MOSEK parameters from a file. Data is read from the file filename if it is a nonempty string. Otherwise data is read from the file specified by sparam.param_read_file_name.

Parameters:
filename (str) – A valid file name. (input)
Groups:
Data file
Task.readsolution
def readsolution (whichsol, filename)


Reads a solution file and inserts it as a specified solution in the task. Data is read from the file filename if it is a nonempty string. Otherwise data is read from one of the files specified by sparam.bas_sol_file_name, sparam.itr_sol_file_name or sparam.int_sol_file_name depending on which solution is chosen.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• filename (str) – A valid file name. (input)
Groups:

Data file

Task.readsummary
def readsummary (whichstream)


Prints a short summary of last file that was read.

Parameters:
whichstream (mosek.streamtype) – Index of the stream. (input)
Groups:
Task.readtask
def readtask (filename)


Load task data from a file, replacing any data that already exists in the task object. All problem data, parameters and other settings are resorted, but if the file contains solutions, the solution status after loading a file is set to unknown, even if it was optimal or otherwise well-defined when the file was dumped.

See section The Task Format for a description of the Task format.

Parameters:
filename (str) – A valid file name. (input)
Task.removebarvars
def removebarvars (subset)


The function removes a subset of the symmetric matrices from the optimization task. This implies that the remaining symmetric matrices are renumbered.

Parameters:
subset (int[]) – Indexes of symmetric matrices which should be removed. (input)
Groups:
Symmetric matrix variable data
Task.removecones
def removecones (subset)


Removes a number of conic constraints from the problem. This implies that the remaining conic constraints are renumbered. In general, it is much more efficient to remove a cone with a high index than a low index.

Parameters:
subset (int[]) – Indexes of cones which should be removed. (input)
Groups:
Conic constraint data
Task.removecons
def removecons (subset)


The function removes a subset of the constraints from the optimization task. This implies that the remaining constraints are renumbered.

Parameters:
subset (int[]) – Indexes of constraints which should be removed. (input)
Groups:
Linear constraint data
Task.removevars
def removevars (subset)


The function removes a subset of the variables from the optimization task. This implies that the remaining variables are renumbered.

Parameters:
subset (int[]) – Indexes of variables which should be removed. (input)
Groups:
Scalar variable data
Task.resizetask
def resizetask (maxnumcon, maxnumvar, maxnumcone, maxnumanz, maxnumqnz)


Sets the amount of preallocated space assigned for each type of data in an optimization task.

It is never mandatory to call this function, since it only gives a hint about the amount of data to preallocate for efficiency reasons.

Please note that the procedure is destructive in the sense that all existing data stored in the task is destroyed.

Parameters:

• maxnumcon (int) – New maximum number of constraints. (input)
• maxnumvar (int) – New maximum number of variables. (input)
• maxnumcone (int) – New maximum number of cones. (input)
• maxnumanz (int) – New maximum number of non-zeros in $$A$$. (input)
• maxnumqnz (int) – New maximum number of non-zeros in all $$Q$$ matrices. (input)
Task.sensitivityreport
def sensitivityreport (whichstream)


Reads a sensitivity format file from a location given by sparam.sensitivity_file_name and writes the result to the stream whichstream. If sparam.sensitivity_res_file_name is set to a non-empty string, then the sensitivity report is also written to a file of this name.

Parameters:
whichstream (mosek.streamtype) – Index of the stream. (input)
Groups:
Sensitivity analysis
Task.set_InfoCallback
def set_InfoCallback (callback)


Receive callbacks with solver status and information during optimization.

For example:

task.set_Progress(lambda code,dinf,iinf,liinf: print("Called from: {0}".format(code)))

Parameters:
callback (callbackfunc) – The callback function. (input)
Task.set_Progress
def set_Progress (callback)


For example:

task.set_Progress(lambda code: print("Called from: {0}".format(code)))

Parameters:
callback (progresscallbackfunc) – The callback function. (input)
Task.set_Stream
def set_Stream (whichstream, callback)


Directs all output from a task stream to a callback function.

Parameters:

Task.setdefaults
def setdefaults ()


Resets all the parameters to their default values.

Groups:
Parameter management
Task.solutiondef
def solutiondef (whichsol) -> isdef


Checks whether a solution is defined.

Parameters:
whichsol (mosek.soltype) – Selects a solution. (input)
Return:
isdef (int) – Is non-zero if the requested solution is defined.
Groups:
Solution information
Task.solutionsummary
def solutionsummary (whichstream)


Prints a short summary of the current solutions.

Parameters:
whichstream (mosek.streamtype) – Index of the stream. (input)
Groups:
Task.solvewithbasis
def solvewithbasis (transp, numnz, sub, val) -> numnz


If a basic solution is available, then exactly $$numcon$$ basis variables are defined. These $$numcon$$ basis variables are denoted the basis. Associated with the basis is a basis matrix denoted $$B$$. This function solves either the linear equation system

(2)$B \barX = b$

or the system

(3)$B^T \barX = b$

for the unknowns $$\barX$$, with $$b$$ being a user-defined vector. In order to make sense of the solution $$\barX$$ it is important to know the ordering of the variables in the basis because the ordering specifies how $$B$$ is constructed. When calling Task.initbasissolve an ordering of the basis variables is obtained, which can be used to deduce how MOSEK has constructed $$B$$. Indeed if the $$k$$-th basis variable is variable $$x_j$$ it implies that

$B_{i,k} = A_{i,j}, ~i=\idxbeg,\ldots,\idxend{numcon}.$

Otherwise if the $$k$$-th basis variable is variable $$x_j^c$$ it implies that

$\begin{split}B_{i,k} = \left\{ \begin{array}{ll} -1, & i = j, \\ 0 , & i \neq j. \\ \end{array} \right.\end{split}$

The function Task.initbasissolve must be called before a call to this function. Please note that this function exploits the sparsity in the vector $$b$$ to speed up the computations.

Parameters:

• transp (int) – If this argument is zero, then (2) is solved, if non-zero then (3) is solved. (input)
• numnz (int) – As input it is the number of non-zeros in $$b$$. As output it is the number of non-zeros in $$\barX$$. (input/output)
• sub (int[]) – As input it contains the positions of non-zeros in $$b$$. As output it contains the positions of the non-zeros in $$\barX$$. It must have room for $$numcon$$ elements. (input/output)
• val (float[]) – As input it is the vector $$b$$ as a dense vector (although the positions of non-zeros are specified in sub it is required that $$\mathtt{val}[i] = 0$$ when $$b[i] = 0$$). As output val is the vector $$\barX$$ as a dense vector. It must have length $$numcon$$. (input/output)
Return:

numnz (int) – As input it is the number of non-zeros in $$b$$. As output it is the number of non-zeros in $$\barX$$.

Groups:

Basis matrix

Task.strtoconetype
def strtoconetype (str) -> conetype


Obtains cone type code corresponding to a cone type string.

Parameters:
str (str) – String corresponding to the cone type code conetype. (input)
Return:
conetype (mosek.conetype) – The cone type corresponding to the string str.
Task.strtosk
def strtosk (str) -> sk


Obtains the status key corresponding to an explanatory string.

Parameters:
str (str) – Status key string. (input)
Return:
sk (int) – Status key corresponding to the string.
Task.toconic
def toconic ()


This function tries to reformulate a given Quadratically Constrained Quadratic Optimization problem (QCQP) as a Conic Quadratic Optimization problem (CQO). The first step of the reformulation is to convert the quadratic term of the objective function, if any, into a constraint. Then the following steps are repeated for each quadratic constraint:

• a conic constraint is added along with a suitable number of auxiliary variables and constraints;
• the original quadratic constraint is not removed, but all its coefficients are zeroed out.

Note that the reformulation preserves all the original variables.

The conversion is performed in-place, i.e. the task passed as argument is modified on exit. That also means that if the reformulation fails, i.e. the given QCQP is not representable as a CQO, then the task has an undefined state. In some cases, users may want to clone the task to ensure a clean copy is preserved.

Task.updatesolutioninfo
def updatesolutioninfo (whichsol)


Update the information items related to the solution.

Parameters:
whichsol (mosek.soltype) – Selects a solution. (input)
Groups:
Task.writedata
def writedata (filename)


Writes problem data associated with the optimization task to a file in one of the supported formats. See Section Supported File Formats for the complete list.

By default the data file format is determined by the file name extension. This behaviour can be overridden by setting the iparam.write_data_format parameter. To write in compressed format append the extension .gz. E.g to write a gzip compressed MPS file use the extension mps.gz.

Please note that MPS, LP and OPF files require all variables to have unique names. If a task contains no names, it is possible to write the file with automatically generated anonymous names by setting the iparam.write_generic_names parameter to onoffkey.on.

Data is written to the file filename if it is a nonempty string. Otherwise data is written to the file specified by sparam.data_file_name.

Please note that if a general nonlinear function appears in the problem then such function cannot be written to file and MOSEK will issue a warning.

Parameters:
filename (str) – A valid file name. (input)
Groups:
Data file
Task.writejsonsol
def writejsonsol (filename)


Saves the current solutions and solver information items in a JSON file.

Parameters:
filename (str) – A valid file name. (input)
Groups:
Data file
Task.writeparamfile
def writeparamfile (filename)


Writes all the parameters to a parameter file.

Parameters:
filename (str) – A valid file name. (input)
Groups:
Data file
Task.writesolution
def writesolution (whichsol, filename)


Saves the current basic, interior-point, or integer solution to a file.

Parameters:

• whichsol (mosek.soltype) – Selects a solution. (input)
• filename (str) – A valid file name. (input)
Groups:

Data file

Task.writetask
def writetask (filename)


Write a binary dump of the task data. This format saves all problem data, coefficients and parameter settings but does not save callback functions and general non-linear terms.

See section The Task Format for a description of the Task format.

Parameters:
filename (str) – A valid file name. (input)
Task.writetasksolverresult_file
def writetasksolverresult_file (filename)


Internal

Parameters:
filename (str) – A valid file name. (input)