15.3 Class Env¶

mosek.Env¶: The MOSEK global environment.

Env.Env¶

Env()

Env(string dbgfile)

Constructor of a new environment.

Parameters: dbgfile (string) – File where the memory debugging log is written. (input)

Env.Dispose¶

void Dispose ()

Free the underlying native allocation.

Env.axpy¶

axpy(int n,
     double alpha,
     double[] x,
     double[] y)

Adds \(\alpha x\) to \(y\), i.e. performs the update

\[y := \alpha x + y.\]

Note that the result is stored overwriting \(y\). It must not overlap with the other input arrays.

Parameters

n (int) – Length of the vectors. (input)
alpha (double) – The scalar that multiplies \(x\). (input)
x (double[]) – The \(x\) vector. (input)
y (double[]) – The \(y\) vector. (input/output)

Groups

Linear algebra

Env.checkinall¶

checkinall()

Check in all unused license features to the license token server.

Groups: License system

Env.checkinlicense¶

checkinlicense(feature feature)

Check in a license feature to the license server. By default all licenses consumed by functions using a single environment are kept checked out for the lifetime of the MOSEK environment. This function checks in a given license feature back to the license server immediately.

If the given license feature is not checked out at all, or it is in use by a call to Task.optimize, calling this function has no effect.

Please note that returning a license to the license server incurs a small overhead, so frequent calls to this function should be avoided.

Parameters: feature (mosek.feature) – Feature to check in to the license system. (input)
Groups: License system

Env.checkoutlicense¶

checkoutlicense(feature feature)

Checks out a license feature from the license server. Normally the required license features will be automatically checked out the first time they are needed by the function Task.optimize. This function can be used to check out one or more features ahead of time.

The feature will remain checked out until the environment is deleted or the function Env.checkinlicense is called.

If a given feature is already checked out when this function is called, the call has no effect.

Parameters: feature (mosek.feature) – Feature to check out from the license system. (input)
Groups: License system

Env.computesparsecholesky¶

computesparsecholesky(int numthreads,
                      int ordermethod,
                      double tolsingular,
                      int[] anzc,
                      long[] aptrc,
                      int[] asubc,
                      double[] avalc,
                      out int[] perm,
                      out double[] diag,
                      out int[] lnzc,
                      out long[] lptrc,
                      out long lensubnval,
                      out int[] lsubc,
                      out double[] lvalc)

computesparsecholesky(int numthreads,
                      int ordermethod,
                      double tolsingular,
                      int[] anzc,
                      long[] aptrc,
                      int[] asubc,
                      double[] avalc) ->
                     (int[] perm,
                      double[] diag,
                      int[] lnzc,
                      long[] lptrc,
                      long lensubnval,
                      int[] lsubc,
                      double[] lvalc)

The function computes a Cholesky factorization of a sparse positive semidefinite matrix. Sparsity is exploited during the computations to reduce the amount of space and work required. Both the input and output matrices are represented using the sparse format.

To be precise, given a symmetric matrix \(A \in \real^{n\times n}\) the function computes a nonsingular lower triangular matrix \(L\), a diagonal matrix \(D\) and a permutation matrix \(P\) such that

\[LL^T - D = P A P^T.\]

If ordermethod is zero then reordering heuristics are not employed and \(P\) is the identity.

If a pivot during the computation of the Cholesky factorization is less than

\[-\rho\cdot\max((PAP^T)_{jj},1.0)\]

then the matrix is declared negative semidefinite. On the hand if a pivot is smaller than

\[\rho\cdot\max((PAP^T)_{jj},1.0),\]

then \(D_{jj}\) is increased from zero to

\[\rho\cdot\max((PAP^T)_{jj},1.0).\]

Therefore, if \(A\) is sufficiently positive definite then \(D\) will be the zero matrix. Here \(\rho\) is set equal to value of tolsingular.

Parameters

numthreads (int) – The number threads that can be used to do the computation. 0 means the code makes the choice. NOTE: API change in version 10: in versions up to 9 the argument in this position indicated whether to use multithreading or not. (input)
ordermethod (int) – If nonzero, then a sparsity preserving ordering will be employed. (input)
tolsingular (double) – A positive parameter controlling when a pivot is declared zero. (input)
anzc (int[]) – anzc[j] is the number of nonzeros in the \(j\)-th column of \(A\). (input)
aptrc (long[]) – aptrc[j] is a pointer to the first element in column \(j\) of \(A\). (input)
asubc (int[]) – Row indexes for each column stored in increasing order. (input)
avalc (double[]) – The value corresponding to row indexed stored in asubc. (input)
perm (int[]) – Permutation array used to specify the permutation matrix \(P\) computed by the function. (output)
diag (double[]) – The diagonal elements of matrix \(D\). (output)
lnzc (int[]) – lnzc[j] is the number of non zero elements in column \(j\) of \(L\). (output)
lptrc (long[]) – lptrc[j] is a pointer to the first row index and value in column \(j\) of \(L\). (output)
lensubnval (long) – Number of elements in lsubc and lvalc. (output)
lsubc (int[]) – Row indexes for each column stored in increasing order. (output)
lvalc (double[]) – The values corresponding to row indexed stored in lsubc. (output)

Return

perm (int[]) – Permutation array used to specify the permutation matrix \(P\) computed by the function.
diag (double[]) – The diagonal elements of matrix \(D\).
lnzc (int[]) – lnzc[j] is the number of non zero elements in column \(j\) of \(L\).
lptrc (long[]) – lptrc[j] is a pointer to the first row index and value in column \(j\) of \(L\).
lensubnval (long) – Number of elements in lsubc and lvalc.
lsubc (int[]) – Row indexes for each column stored in increasing order.
lvalc (double[]) – The values corresponding to row indexed stored in lsubc.

Groups

Linear algebra

Env.dot¶

dot(int n,
    double[] x,
    double[] y,
    out double xty)

dot(int n,
    double[] x,
    double[] y) -> double xty

Computes the inner product of two vectors \(x,y\) of length \(n\geq 0\), i.e

\[x\cdot y= \sum_{i=1}^n x_i y_i.\]

Note that if \(n=0\), then the result of the operation is 0.

Parameters

n (int) – Length of the vectors. (input)
x (double[]) – The \(x\) vector. (input)
y (double[]) – The \(y\) vector. (input)
xty (double) – The result of the inner product between \(x\) and \(y\). (output)

Return

xty (double) – The result of the inner product between \(x\) and \(y\).

Groups

Linear algebra

Env.echointro¶

echointro(int longver)

Prints an intro to message stream.

Parameters: longver (int) – If non-zero, then the intro is slightly longer. (input)
Groups: Logging

Env.expirylicenses¶

expirylicenses(out long expiry)

expirylicenses() -> long expiry

Reports when the first license feature expires. It reports the number of days to the expiry of the first feature of all the features that were ever checked out from the start of the process, or from the last call to Env.resetexpirylicenses, until now.

Parameters: expiry (long) – If nonnegative, then it is the minimum number days to expiry of any feature that has been checked out. (output)
Return: expiry (long) – If nonnegative, then it is the minimum number days to expiry of any feature that has been checked out.
Groups: License system

Env.gemm¶

gemm(transpose transa,
     transpose transb,
     int m,
     int n,
     int k,
     double alpha,
     double[] a,
     double[] b,
     double beta,
     double[] c)

Performs a matrix multiplication plus addition of dense matrices. Given \(A\), \(B\) and \(C\) of compatible dimensions, this function computes

\[C:= \alpha op(A)op(B) + \beta C\]

where \(\alpha,\beta\) are two scalar values. The function \(op(X)\) denotes \(X\) if transX is transpose.no, or \(X^T\) if set to transpose.yes. The matrix \(C\) has \(m\) rows and \(n\) columns, and the other matrices must have compatible dimensions.

The result of this operation is stored in \(C\). It must not overlap with the other input arrays.

Parameters

transa (mosek.transpose) – Indicates whether the matrix \(A\) must be transposed. (input)
transb (mosek.transpose) – Indicates whether the matrix \(B\) must be transposed. (input)
m (int) – Indicates the number of rows of matrix \(C\). (input)
n (int) – Indicates the number of columns of matrix \(C\). (input)
k (int) – Specifies the common dimension along which \(op(A)\) and \(op(B)\) are multiplied. For example, if neither \(A\) nor \(B\) are transposed, then this is the number of columns in \(A\) and also the number of rows in \(B\). (input)
alpha (double) – A scalar value multiplying the result of the matrix multiplication. (input)
a (double[]) – The pointer to the array storing matrix \(A\) in a column-major format. (input)
b (double[]) – The pointer to the array storing matrix \(B\) in a column-major format. (input)
beta (double) – A scalar value that multiplies \(C\). (input)
c (double[]) – The pointer to the array storing matrix \(C\) in a column-major format. (input/output)

Groups

Linear algebra

Env.gemv¶

gemv(transpose transa,
     int m,
     int n,
     double alpha,
     double[] a,
     double[] x,
     double beta,
     double[] y)

Computes the multiplication of a scaled dense matrix times a dense vector, plus a scaled dense vector. Precisely, if trans is transpose.no then the update is

\[y := \alpha A x + \beta y,\]

and if trans is transpose.yes then

\[y := \alpha A^T x + \beta y,\]

where \(\alpha,\beta\) are scalar values and \(A\) is a matrix with \(m\) rows and \(n\) columns.

Note that the result is stored overwriting \(y\). It must not overlap with the other input arrays.

Parameters

transa (mosek.transpose) – Indicates whether the matrix \(A\) must be transposed. (input)
m (int) – Specifies the number of rows of the matrix \(A\). (input)
n (int) – Specifies the number of columns of the matrix \(A\). (input)
alpha (double) – A scalar value multiplying the matrix \(A\). (input)
a (double[]) – A pointer to the array storing matrix \(A\) in a column-major format. (input)
x (double[]) – A pointer to the array storing the vector \(x\). (input)
beta (double) – A scalar value multiplying the vector \(y\). (input)
y (double[]) – A pointer to the array storing the vector \(y\). (input/output)

Groups

Linear algebra

Env.getbuildinfo¶

static getbuildinfo(StringBuilder buildstate,
                    StringBuilder builddate)

static getbuildinfo() ->
                   (string buildstate,
                    string builddate)

Obtains build information.

Parameters

buildstate (StringBuilder) – State of binaries, i.e. a debug, release candidate or final release. (output)
builddate (StringBuilder) – Date when the binaries were built. (output)

Return

buildstate (string) – State of binaries, i.e. a debug, release candidate or final release.
builddate (string) – Date when the binaries were built.

Groups

Versions

Env.getcodedesc¶

static getcodedesc(rescode code,
                   StringBuilder symname,
                   StringBuilder str)

static getcodedesc(rescode code) ->
                  (string symname,
                   string str)

Obtains a short description of the meaning of the response code given by code.

Parameters

code (mosek.rescode) – A valid MOSEK response code. (input)
symname (StringBuilder) – Symbolic name corresponding to code. (output)
str (StringBuilder) – Obtains a short description of a response code. (output)

Return

symname (string) – Symbolic name corresponding to code.
str (string) – Obtains a short description of a response code.

Groups

Names, Responses, errors and warnings

Env.getversion¶

static getversion(out int major,
                  out int minor,
                  out int revision)

static getversion() ->
                 (int major,
                  int minor,
                  int revision)

Obtains MOSEK version information.

Parameters

major (int) – Major version number. (output)
minor (int) – Minor version number. (output)
revision (int) – Revision number. (output)

Return

major (int) – Major version number.
minor (int) – Minor version number.
revision (int) – Revision number.

Groups

Versions

Env.licensecleanup¶

static licensecleanup()

Stops all threads and deletes all handles used by the license system. If this function is called, it must be called as the last MOSEK API call. No other MOSEK API calls are valid after this.

Groups: License system

Env.linkfiletostream¶

linkfiletostream(streamtype whichstream,
                 string filename,
                 int append)

Sends all output from the stream defined by whichstream to the file given by filename.

Parameters

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

Groups

Logging

Env.optimizebatch¶

optimizebatch(bool israce,
              double maxtime,
              int numthreads,
              Task[] task,
              rescode[] trmcode,
              rescode[] rcode)

optimizebatch(bool israce,
              double maxtime,
              int numthreads,
              Task[] task) ->
             (rescode[] trmcode,
              rescode[] rcode)

Optimize a number of tasks in parallel using a specified number of threads. All callbacks and log output streams are disabled.

Assuming that each task takes about same time and there many more tasks than number of threads then a linear speedup can be achieved, also known as strong scaling. A typical application of this method is to solve many small tasks of similar type; in this case it is recommended that each of them is allocated a single thread by setting iparam.num_threads to \(1\).

If the parameters israce or maxtime are used, then the result may not be deterministic, in the sense that the tasks which complete first may vary between runs.

The remaining behavior, including termination and response codes returned for each task, are the same as if each task was optimized separately.

Parameters

israce (bool) – If nonzero, then the function is terminated after the first task has been completed. (input)
maxtime (double) – Time limit for the function: if nonnegative, then the function is terminated after maxtime (seconds) has expired. (input)
numthreads (int) – Number of threads to be employed. (input)
task (Task[]) – An array of tasks to optimize in parallel. (input)
trmcode (mosek.rescode[]) – The termination code for each task. (output)
rcode (mosek.rescode[]) – The response code for each task. (output)

Return

trmcode (mosek.rescode[]) – The termination code for each task.
rcode (mosek.rescode[]) – The response code for each task.

Groups

Optimization

Env.potrf¶

potrf(uplo uplo,
      int n,
      double[] a)

Computes a Cholesky factorization of a real symmetric positive definite dense matrix.

Parameters

uplo (mosek.uplo) – Indicates whether the upper or lower triangular part of the matrix is stored. (input)
n (int) – Dimension of the symmetric matrix. (input)
a (double[]) – A symmetric matrix stored in column-major order. Only the lower or the upper triangular part is used, accordingly with the uplo parameter. It will contain the result on exit. (input/output)

Groups

Linear algebra

Env.putlicensecode¶

putlicensecode(int[] code)

Input a runtime license code. This function has an effect only before the first optimization.

Parameters: code (int[]) – A runtime license code. (input)
Groups: License system

Env.putlicensedebug¶

putlicensedebug(int licdebug)

Enables debug information for the license system. If licdebug is non-zero, then MOSEK will print debug info regarding the license checkout.

Parameters: licdebug (int) – Whether license checkout debug info should be printed. (input)
Groups: License system

Env.putlicensepath¶

putlicensepath(string licensepath)

Set the path to the license file. This function has an effect only before the first optimization.

Parameters: licensepath (string) – A path specifying where to search for the license. (input)
Groups: License system

Env.putlicensewait¶

putlicensewait(int licwait)

Control whether MOSEK should wait for an available license if no license is available. If licwait is non-zero, then MOSEK will wait for licwait-1 milliseconds between each check for an available license.

Parameters: licwait (int) – Whether MOSEK should wait for a license if no license is available. (input)
Groups: License system

Env.resetexpirylicenses¶

resetexpirylicenses()

Reset the license expiry reporting startpoint.

Groups: License system

Env.set_Stream¶

void set_Stream
  (streamtype whichstream,
   Stream callback)

Directs all output from an environment stream to a callback object.

Parameters

whichstream (streamtype) – Index of the stream. (input)
callback (Stream) – The callback object. (input)

Env.sparsetriangularsolvedense¶

sparsetriangularsolvedense(transpose transposed,
                           int[] lnzc,
                           long[] lptrc,
                           int[] lsubc,
                           double[] lvalc,
                           double[] b)

The function solves a triangular system of the form

\[L x = b\]

or

\[L^T x = b\]

where \(L\) is a sparse lower triangular nonsingular matrix. This implies in particular that diagonals in \(L\) are nonzero.

Parameters

transposed (mosek.transpose) – Controls whether to use with \(L\) or \(L^T\). (input)
lnzc (int[]) – lnzc[j] is the number of nonzeros in column j. (input)
lptrc (long[]) – lptrc[j] is a pointer to the first row index and value in column j. (input)
lsubc (int[]) – Row indexes for each column stored sequentially. Must be stored in increasing order for each column. (input)
lvalc (double[]) – The value corresponding to the row index stored in lsubc. (input)
b (double[]) – The right-hand side of linear equation system to be solved as a dense vector. (input/output)

Groups

Linear algebra

Env.syeig¶

syeig(uplo uplo,
      int n,
      double[] a,
      double[] w)

syeig(uplo uplo,
      int n,
      double[] a) -> double[] w

Computes all eigenvalues of a real symmetric matrix \(A\). Given a matrix \(A\in\real^{n\times n}\) it returns a vector \(w\in\real^n\) containing the eigenvalues of \(A\).

Parameters

uplo (mosek.uplo) – Indicates whether the upper or lower triangular part is used. (input)
n (int) – Dimension of the symmetric input matrix. (input)
a (double[]) – A symmetric matrix \(A\) stored in column-major order. Only the part indicated by uplo is used. (input)
w (double[]) – Array of length at least n containing the eigenvalues of \(A\). (output)

Return

w (double[]) – Array of length at least n containing the eigenvalues of \(A\).

Groups

Linear algebra

Env.syevd¶

syevd(uplo uplo,
      int n,
      double[] a,
      double[] w)

syevd(uplo uplo,
      int n,
      double[] a) -> double[] w

Computes all the eigenvalues and eigenvectors a real symmetric matrix. Given the input matrix \(A\in \real^{n\times n}\), this function returns a vector \(w\in \real^n\) containing the eigenvalues of \(A\) and it also computes the eigenvectors of \(A\). Therefore, this function computes the eigenvalue decomposition of \(A\) as

\[A= U V U^T,\]

where \(V=\diag(w)\) and \(U\) contains the eigenvectors of \(A\).

Note that the matrix \(U\) overwrites the input data \(A\).

Parameters

uplo (mosek.uplo) – Indicates whether the upper or lower triangular part is used. (input)
n (int) – Dimension of the symmetric input matrix. (input)
a (double[]) – A symmetric matrix \(A\) stored in column-major order. Only the part indicated by uplo is used. On exit it will be overwritten by the matrix \(U\). (input/output)
w (double[]) – Array of length at least n containing the eigenvalues of \(A\). (output)

Return

w (double[]) – Array of length at least n containing the eigenvalues of \(A\).

Groups

Linear algebra

Env.syrk¶

syrk(uplo uplo,
     transpose trans,
     int n,
     int k,
     double alpha,
     double[] a,
     double beta,
     double[] c)

Performs a symmetric rank-\(k\) update for a symmetric matrix.

Given a symmetric matrix \(C\in \real^{n\times n}\), two scalars \(\alpha,\beta\) and a matrix \(A\) of rank \(k\leq n\), it computes either

\[C := \alpha A A^T + \beta C,\]

when trans is set to transpose.no and \(A\in \real^{n\times k}\), or

\[C := \alpha A^T A + \beta C,\]

when trans is set to transpose.yes and \(A\in \real^{k\times n}\).

Only the part of \(C\) indicated by uplo is used and only that part is updated with the result. It must not overlap with the other input arrays.

Parameters

uplo (mosek.uplo) – Indicates whether the upper or lower triangular part of \(C\) is used. (input)
trans (mosek.transpose) – Indicates whether the matrix \(A\) must be transposed. (input)
n (int) – Specifies the order of \(C\). (input)
k (int) – Indicates the number of rows or columns of \(A\), depending on whether or not it is transposed, and its rank. (input)
alpha (double) – A scalar value multiplying the result of the matrix multiplication. (input)
a (double[]) – The pointer to the array storing matrix \(A\) in a column-major format. (input)
beta (double) – A scalar value that multiplies \(C\). (input)
c (double[]) – The pointer to the array storing matrix \(C\) in a column-major format. (input/output)

Groups

Linear algebra

15.3 Class Env¶

Table of Contents

Download PDF

Modeling Cookbook

Cheatsheet