# 13.3 Parameters (alphabetical list sorted by type)¶

## 13.3.1 Double parameters¶

dparam

The enumeration type containing all double parameters.

MSK_DPAR_ANA_SOL_INFEAS_TOL

If a constraint violates its bound with an amount larger than this value, the constraint name, index and violation will be printed by the solution analyzer.

Default

1e-6

Accepted

[0.0; +inf]

Example

prob$dparam <- list(ANA_SOL_INFEAS_TOL = 1e-6) Groups Analysis MSK_DPAR_BASIS_REL_TOL_S Maximum relative dual bound violation allowed in an optimal basic solution. Default 1.0e-12 Accepted [0.0; +inf] Example prob$dparam <- list(BASIS_REL_TOL_S = 1.0e-12)

Groups
MSK_DPAR_BASIS_TOL_S

Maximum absolute dual bound violation in an optimal basic solution.

Default

1.0e-6

Accepted

[1.0e-9; +inf]

Example

prob$dparam <- list(BASIS_TOL_S = 1.0e-6) Groups MSK_DPAR_BASIS_TOL_X Maximum absolute primal bound violation allowed in an optimal basic solution. Default 1.0e-6 Accepted [1.0e-9; +inf] Example prob$dparam <- list(BASIS_TOL_X = 1.0e-6)

Groups
MSK_DPAR_CHECK_CONVEXITY_REL_TOL

This parameter controls when the full convexity check declares a problem to be non-convex. Increasing this tolerance relaxes the criteria for declaring the problem non-convex.

A problem is declared non-convex if negative (positive) pivot elements are detected in the Cholesky factor of a matrix which is required to be PSD (NSD). This parameter controls how much this non-negativity requirement may be violated.

If $$d_i$$ is the pivot element for column $$i$$, then the matrix $$Q$$ is considered to not be PSD if:

$d_i \leq - |Q_{ii}| \mathtt{check\_convexity\_rel\_tol}$
Default

1e-10

Accepted

[0; +inf]

Example

prob$dparam <- list(CHECK_CONVEXITY_REL_TOL = 1e-10) Groups Interior-point method MSK_DPAR_DATA_SYM_MAT_TOL Absolute zero tolerance for elements in in symmetric matrices. If any value in a symmetric matrix is smaller than this parameter in absolute terms MOSEK will treat the values as zero and generate a warning. Default 1.0e-12 Accepted [1.0e-16; 1.0e-6] Example prob$dparam <- list(DATA_SYM_MAT_TOL = 1.0e-12)

Groups

Data check

MSK_DPAR_DATA_SYM_MAT_TOL_HUGE

An element in a symmetric matrix which is larger than this value in absolute size causes an error.

Default

1.0e20

Accepted

[0.0; +inf]

Example

prob$dparam <- list(DATA_SYM_MAT_TOL_HUGE = 1.0e20) Groups Data check MSK_DPAR_DATA_SYM_MAT_TOL_LARGE An element in a symmetric matrix which is larger than this value in absolute size causes a warning message to be printed. Default 1.0e10 Accepted [0.0; +inf] Example prob$dparam <- list(DATA_SYM_MAT_TOL_LARGE = 1.0e10)

Groups

Data check

MSK_DPAR_DATA_TOL_AIJ_HUGE

An element in $$A$$ which is larger than this value in absolute size causes an error.

Default

1.0e20

Accepted

[0.0; +inf]

Example

prob$dparam <- list(DATA_TOL_AIJ_HUGE = 1.0e20) Groups Data check MSK_DPAR_DATA_TOL_AIJ_LARGE An element in $$A$$ which is larger than this value in absolute size causes a warning message to be printed. Default 1.0e10 Accepted [0.0; +inf] Example prob$dparam <- list(DATA_TOL_AIJ_LARGE = 1.0e10)

Groups

Data check

MSK_DPAR_DATA_TOL_BOUND_INF

Any bound which in absolute value is greater than this parameter is considered infinite.

Default

1.0e16

Accepted

[0.0; +inf]

Example

prob$dparam <- list(DATA_TOL_BOUND_INF = 1.0e16) Groups Data check MSK_DPAR_DATA_TOL_BOUND_WRN If a bound value is larger than this value in absolute size, then a warning message is issued. Default 1.0e8 Accepted [0.0; +inf] Example prob$dparam <- list(DATA_TOL_BOUND_WRN = 1.0e8)

Groups

Data check

MSK_DPAR_DATA_TOL_C_HUGE

An element in $$c$$ which is larger than the value of this parameter in absolute terms is considered to be huge and generates an error.

Default

1.0e16

Accepted

[0.0; +inf]

Example

prob$dparam <- list(DATA_TOL_C_HUGE = 1.0e16) Groups Data check MSK_DPAR_DATA_TOL_CJ_LARGE An element in $$c$$ which is larger than this value in absolute terms causes a warning message to be printed. Default 1.0e8 Accepted [0.0; +inf] Example prob$dparam <- list(DATA_TOL_CJ_LARGE = 1.0e8)

Groups

Data check

MSK_DPAR_DATA_TOL_QIJ

Absolute zero tolerance for elements in $$Q$$ matrices.

Default

1.0e-16

Accepted

[0.0; +inf]

Example

prob$dparam <- list(DATA_TOL_QIJ = 1.0e-16) Groups Data check MSK_DPAR_DATA_TOL_X Zero tolerance for constraints and variables i.e. if the distance between the lower and upper bound is less than this value, then the lower and upper bound is considered identical. Default 1.0e-8 Accepted [0.0; +inf] Example prob$dparam <- list(DATA_TOL_X = 1.0e-8)

Groups

Data check

MSK_DPAR_INTPNT_CO_TOL_DFEAS

Dual feasibility tolerance used by the interior-point optimizer for conic problems.

Default

1.0e-8

Accepted

[0.0; 1.0]

Example

prob$dparam <- list(INTPNT_CO_TOL_DFEAS = 1.0e-8) See also MSK_DPAR_INTPNT_CO_TOL_NEAR_REL Groups MSK_DPAR_INTPNT_CO_TOL_INFEAS Infeasibility tolerance used by the interior-point optimizer for conic problems. Controls when the interior-point optimizer declares the model primal or dual infeasible. A small number means the optimizer gets more conservative about declaring the model infeasible. Default 1.0e-12 Accepted [0.0; 1.0] Example prob$dparam <- list(INTPNT_CO_TOL_INFEAS = 1.0e-12)

Groups
MSK_DPAR_INTPNT_CO_TOL_MU_RED

Relative complementarity gap tolerance used by the interior-point optimizer for conic problems.

Default

1.0e-8

Accepted

[0.0; 1.0]

Example

prob$dparam <- list(INTPNT_CO_TOL_MU_RED = 1.0e-8) Groups MSK_DPAR_INTPNT_CO_TOL_NEAR_REL Optimality tolerance used by the interior-point optimizer for conic problems. If MOSEK cannot compute a solution that has the prescribed accuracy then it will check if the solution found satisfies the termination criteria with all tolerances multiplied by the value of this parameter. If yes, then the solution is also declared optimal. Default 1000 Accepted [1.0; +inf] Example prob$dparam <- list(INTPNT_CO_TOL_NEAR_REL = 1000)

Groups
MSK_DPAR_INTPNT_CO_TOL_PFEAS

Primal feasibility tolerance used by the interior-point optimizer for conic problems.

Default

1.0e-8

Accepted

[0.0; 1.0]

Example

prob$dparam <- list(INTPNT_CO_TOL_PFEAS = 1.0e-8) See also MSK_DPAR_INTPNT_CO_TOL_NEAR_REL Groups MSK_DPAR_INTPNT_CO_TOL_REL_GAP Relative gap termination tolerance used by the interior-point optimizer for conic problems. Default 1.0e-8 Accepted [0.0; 1.0] Example prob$dparam <- list(INTPNT_CO_TOL_REL_GAP = 1.0e-8)

See also

MSK_DPAR_INTPNT_CO_TOL_NEAR_REL

Groups
MSK_DPAR_INTPNT_QO_TOL_DFEAS

Dual feasibility tolerance used by the interior-point optimizer for quadratic problems.

Default

1.0e-8

Accepted

[0.0; 1.0]

Example

prob$dparam <- list(INTPNT_QO_TOL_DFEAS = 1.0e-8) See also MSK_DPAR_INTPNT_QO_TOL_NEAR_REL Groups MSK_DPAR_INTPNT_QO_TOL_INFEAS Infeasibility tolerance used by the interior-point optimizer for quadratic problems. Controls when the interior-point optimizer declares the model primal or dual infeasible. A small number means the optimizer gets more conservative about declaring the model infeasible. Default 1.0e-12 Accepted [0.0; 1.0] Example prob$dparam <- list(INTPNT_QO_TOL_INFEAS = 1.0e-12)

Groups
MSK_DPAR_INTPNT_QO_TOL_MU_RED

Relative complementarity gap tolerance used by the interior-point optimizer for quadratic problems.

Default

1.0e-8

Accepted

[0.0; 1.0]

Example

prob$dparam <- list(INTPNT_QO_TOL_MU_RED = 1.0e-8) Groups MSK_DPAR_INTPNT_QO_TOL_NEAR_REL Optimality tolerance used by the interior-point optimizer for quadratic problems. If MOSEK cannot compute a solution that has the prescribed accuracy then it will check if the solution found satisfies the termination criteria with all tolerances multiplied by the value of this parameter. If yes, then the solution is also declared optimal. Default 1000 Accepted [1.0; +inf] Example prob$dparam <- list(INTPNT_QO_TOL_NEAR_REL = 1000)

Groups
MSK_DPAR_INTPNT_QO_TOL_PFEAS

Primal feasibility tolerance used by the interior-point optimizer for quadratic problems.

Default

1.0e-8

Accepted

[0.0; 1.0]

Example

prob$dparam <- list(INTPNT_QO_TOL_PFEAS = 1.0e-8) See also MSK_DPAR_INTPNT_QO_TOL_NEAR_REL Groups MSK_DPAR_INTPNT_QO_TOL_REL_GAP Relative gap termination tolerance used by the interior-point optimizer for quadratic problems. Default 1.0e-8 Accepted [0.0; 1.0] Example prob$dparam <- list(INTPNT_QO_TOL_REL_GAP = 1.0e-8)

See also

MSK_DPAR_INTPNT_QO_TOL_NEAR_REL

Groups
MSK_DPAR_INTPNT_TOL_DFEAS

Dual feasibility tolerance used by the interior-point optimizer for linear problems.

Default

1.0e-8

Accepted

[0.0; 1.0]

Example

prob$dparam <- list(INTPNT_TOL_DFEAS = 1.0e-8) Groups MSK_DPAR_INTPNT_TOL_DSAFE Controls the initial dual starting point used by the interior-point optimizer. If the interior-point optimizer converges slowly and/or the constraint or variable bounds are very large, then it might be worthwhile to increase this value. Default 1.0 Accepted [1.0e-4; +inf] Example prob$dparam <- list(INTPNT_TOL_DSAFE = 1.0)

Groups

Interior-point method

MSK_DPAR_INTPNT_TOL_INFEAS

Infeasibility tolerance used by the interior-point optimizer for linear problems. Controls when the interior-point optimizer declares the model primal or dual infeasible. A small number means the optimizer gets more conservative about declaring the model infeasible.

Default

1.0e-10

Accepted

[0.0; 1.0]

Example

prob$dparam <- list(INTPNT_TOL_INFEAS = 1.0e-10) Groups MSK_DPAR_INTPNT_TOL_MU_RED Relative complementarity gap tolerance used by the interior-point optimizer for linear problems. Default 1.0e-16 Accepted [0.0; 1.0] Example prob$dparam <- list(INTPNT_TOL_MU_RED = 1.0e-16)

Groups
MSK_DPAR_INTPNT_TOL_PATH

Controls how close the interior-point optimizer follows the central path. A large value of this parameter means the central path is followed very closely. On numerically unstable problems it may be worthwhile to increase this parameter.

Default

1.0e-8

Accepted

[0.0; 0.9999]

Example

prob$dparam <- list(INTPNT_TOL_PATH = 1.0e-8) Groups Interior-point method MSK_DPAR_INTPNT_TOL_PFEAS Primal feasibility tolerance used by the interior-point optimizer for linear problems. Default 1.0e-8 Accepted [0.0; 1.0] Example prob$dparam <- list(INTPNT_TOL_PFEAS = 1.0e-8)

Groups
MSK_DPAR_INTPNT_TOL_PSAFE

Controls the initial primal starting point used by the interior-point optimizer. If the interior-point optimizer converges slowly and/or the constraint or variable bounds are very large, then it may be worthwhile to increase this value.

Default

1.0

Accepted

[1.0e-4; +inf]

Example

prob$dparam <- list(INTPNT_TOL_PSAFE = 1.0) Groups Interior-point method MSK_DPAR_INTPNT_TOL_REL_GAP Relative gap termination tolerance used by the interior-point optimizer for linear problems. Default 1.0e-8 Accepted [1.0e-14; +inf] Example prob$dparam <- list(INTPNT_TOL_REL_GAP = 1.0e-8)

Groups
MSK_DPAR_INTPNT_TOL_REL_STEP

Relative step size to the boundary for linear and quadratic optimization problems.

Default

0.9999

Accepted

[1.0e-4; 0.999999]

Example

prob$dparam <- list(INTPNT_TOL_REL_STEP = 0.9999) Groups Interior-point method MSK_DPAR_INTPNT_TOL_STEP_SIZE Minimal step size tolerance. If the step size falls below the value of this parameter, then the interior-point optimizer assumes that it is stalled. In other words the interior-point optimizer does not make any progress and therefore it is better to stop. Default 1.0e-6 Accepted [0.0; 1.0] Example prob$dparam <- list(INTPNT_TOL_STEP_SIZE = 1.0e-6)

Groups

Interior-point method

MSK_DPAR_LOWER_OBJ_CUT

If either a primal or dual feasible solution is found proving that the optimal objective value is outside the interval $$[$$ MSK_DPAR_LOWER_OBJ_CUT, MSK_DPAR_UPPER_OBJ_CUT $$]$$, then MOSEK is terminated.

Default

-1.0e30

Accepted

[-inf; +inf]

Example

prob$dparam <- list(LOWER_OBJ_CUT = -1.0e30) See also MSK_DPAR_LOWER_OBJ_CUT_FINITE_TRH Groups Termination criteria MSK_DPAR_LOWER_OBJ_CUT_FINITE_TRH If the lower objective cut is less than the value of this parameter value, then the lower objective cut i.e. MSK_DPAR_LOWER_OBJ_CUT is treated as $$-\infty$$. Default -0.5e30 Accepted [-inf; +inf] Example prob$dparam <- list(LOWER_OBJ_CUT_FINITE_TRH = -0.5e30)

Groups

Termination criteria

MSK_DPAR_MIO_DJC_MAX_BIGM

Maximum allowed big-M value when reformulating disjunctive constraints to linear constraints. Higher values make it more likely that a disjunction is reformulated to linear constraints, but also increase the risk of numerical problems.

Default

1.0e6

Accepted

[0; +inf]

Example

prob$dparam <- list(MIO_DJC_MAX_BIGM = 1.0e6) Groups Mixed-integer optimization MSK_DPAR_MIO_MAX_TIME This parameter limits the maximum time spent by the mixed-integer optimizer. A negative number means infinity. Default -1.0 Accepted [-inf; +inf] Example prob$dparam <- list(MIO_MAX_TIME = -1.0)

Groups
MSK_DPAR_MIO_REL_GAP_CONST

This value is used to compute the relative gap for the solution to an integer optimization problem.

Default

1.0e-10

Accepted

[1.0e-15; +inf]

Example

prob$dparam <- list(MIO_REL_GAP_CONST = 1.0e-10) Groups MSK_DPAR_MIO_TOL_ABS_GAP Absolute optimality tolerance employed by the mixed-integer optimizer. Default 0.0 Accepted [0.0; +inf] Example prob$dparam <- list(MIO_TOL_ABS_GAP = 0.0)

Groups

Mixed-integer optimization

MSK_DPAR_MIO_TOL_ABS_RELAX_INT

Absolute integer feasibility tolerance. If the distance to the nearest integer is less than this tolerance then an integer constraint is assumed to be satisfied.

Default

1.0e-5

Accepted

[1e-9; +inf]

Example

prob$dparam <- list(MIO_TOL_ABS_RELAX_INT = 1.0e-5) Groups Mixed-integer optimization MSK_DPAR_MIO_TOL_FEAS Feasibility tolerance for mixed integer solver. Default 1.0e-6 Accepted [1e-9; 1e-3] Example prob$dparam <- list(MIO_TOL_FEAS = 1.0e-6)

Groups

Mixed-integer optimization

MSK_DPAR_MIO_TOL_REL_DUAL_BOUND_IMPROVEMENT

If the relative improvement of the dual bound is smaller than this value, the solver will terminate the root cut generation. A value of 0.0 means that the value is selected automatically.

Default

0.0

Accepted

[0.0; 1.0]

Example

prob$dparam <- list(MIO_TOL_REL_DUAL_BOUND_IMPROVEMENT = 0.0) Groups Mixed-integer optimization MSK_DPAR_MIO_TOL_REL_GAP Relative optimality tolerance employed by the mixed-integer optimizer. Default 1.0e-4 Accepted [0.0; +inf] Example prob$dparam <- list(MIO_TOL_REL_GAP = 1.0e-4)

Groups
MSK_DPAR_OPTIMIZER_MAX_TIME

Maximum amount of time the optimizer is allowed to spent on the optimization. A negative number means infinity.

Default

-1.0

Accepted

[-inf; +inf]

Example

prob$dparam <- list(OPTIMIZER_MAX_TIME = -1.0) Groups Termination criteria MSK_DPAR_PRESOLVE_TOL_ABS_LINDEP Absolute tolerance employed by the linear dependency checker. Default 1.0e-6 Accepted [0.0; +inf] Example prob$dparam <- list(PRESOLVE_TOL_ABS_LINDEP = 1.0e-6)

Groups

Presolve

MSK_DPAR_PRESOLVE_TOL_AIJ

Absolute zero tolerance employed for $$a_{ij}$$ in the presolve.

Default

1.0e-12

Accepted

[1.0e-15; +inf]

Example

prob$dparam <- list(PRESOLVE_TOL_AIJ = 1.0e-12) Groups Presolve MSK_DPAR_PRESOLVE_TOL_PRIMAL_INFEAS_PERTURBATION The presolve is allowed to perturbe a bound on a constraint or variable by this amount if it removes an infeasibility. Default 1.0e-6 Accepted [0.0; +inf] Example prob$dparam <- list(PRESOLVE_TOL_PRIMAL_INFEAS_PERTURBATION = 1.0e-6)

Groups

Presolve

MSK_DPAR_PRESOLVE_TOL_REL_LINDEP

Relative tolerance employed by the linear dependency checker.

Default

1.0e-10

Accepted

[0.0; +inf]

Example

prob$dparam <- list(PRESOLVE_TOL_REL_LINDEP = 1.0e-10) Groups Presolve MSK_DPAR_PRESOLVE_TOL_S Absolute zero tolerance employed for $$s_i$$ in the presolve. Default 1.0e-8 Accepted [0.0; +inf] Example prob$dparam <- list(PRESOLVE_TOL_S = 1.0e-8)

Groups

Presolve

MSK_DPAR_PRESOLVE_TOL_X

Absolute zero tolerance employed for $$x_j$$ in the presolve.

Default

1.0e-8

Accepted

[0.0; +inf]

Example

prob$dparam <- list(PRESOLVE_TOL_X = 1.0e-8) Groups Presolve MSK_DPAR_QCQO_REFORMULATE_REL_DROP_TOL This parameter determines when columns are dropped in incomplete Cholesky factorization during reformulation of quadratic problems. Default 1e-15 Accepted [0; +inf] Example prob$dparam <- list(QCQO_REFORMULATE_REL_DROP_TOL = 1e-15)

Groups

Interior-point method

MSK_DPAR_SEMIDEFINITE_TOL_APPROX

Tolerance to define a matrix to be positive semidefinite.

Default

1.0e-10

Accepted

[1.0e-15; +inf]

Example

prob$dparam <- list(SEMIDEFINITE_TOL_APPROX = 1.0e-10) Groups Data check MSK_DPAR_SIM_LU_TOL_REL_PIV Relative pivot tolerance employed when computing the LU factorization of the basis in the simplex optimizers and in the basis identification procedure. A value closer to 1.0 generally improves numerical stability but typically also implies an increase in the computational work. Default 0.01 Accepted [1.0e-6; 0.999999] Example prob$dparam <- list(SIM_LU_TOL_REL_PIV = 0.01)

Groups
MSK_DPAR_SIMPLEX_ABS_TOL_PIV

Absolute pivot tolerance employed by the simplex optimizers.

Default

1.0e-7

Accepted

[1.0e-12; +inf]

Example

prob$dparam <- list(SIMPLEX_ABS_TOL_PIV = 1.0e-7) Groups Simplex optimizer MSK_DPAR_UPPER_OBJ_CUT If either a primal or dual feasible solution is found proving that the optimal objective value is outside the interval $$[$$ MSK_DPAR_LOWER_OBJ_CUT, MSK_DPAR_UPPER_OBJ_CUT $$]$$, then MOSEK is terminated. Default 1.0e30 Accepted [-inf; +inf] Example prob$dparam <- list(UPPER_OBJ_CUT = 1.0e30)

See also

MSK_DPAR_UPPER_OBJ_CUT_FINITE_TRH

Groups

Termination criteria

MSK_DPAR_UPPER_OBJ_CUT_FINITE_TRH

If the upper objective cut is greater than the value of this parameter, then the upper objective cut MSK_DPAR_UPPER_OBJ_CUT is treated as $$\infty$$.

Default

0.5e30

Accepted

[-inf; +inf]

Example

prob$dparam <- list(UPPER_OBJ_CUT_FINITE_TRH = 0.5e30) Groups Termination criteria ## 13.3.2 Integer parameters¶ iparam The enumeration type containing all integer parameters. MSK_IPAR_ANA_SOL_BASIS Controls whether the basis matrix is analyzed in solution analyzer. Default "ON" Accepted Example prob$iparam <- list(ANA_SOL_BASIS = "ON")

Groups

Analysis

MSK_IPAR_ANA_SOL_PRINT_VIOLATED

A parameter of the problem analyzer. Controls whether a list of violated constraints is printed. All constraints violated by more than the value set by the parameter MSK_DPAR_ANA_SOL_INFEAS_TOL will be printed.

Default

"OFF"

Accepted
Example

prob$iparam <- list(ANA_SOL_PRINT_VIOLATED = "OFF") Groups Analysis MSK_IPAR_AUTO_SORT_A_BEFORE_OPT Controls whether the elements in each column of $$A$$ are sorted before an optimization is performed. This is not required but makes the optimization more deterministic. Default "OFF" Accepted Example prob$iparam <- list(AUTO_SORT_A_BEFORE_OPT = "OFF")

Groups

Debugging

MSK_IPAR_AUTO_UPDATE_SOL_INFO

Controls whether the solution information items are automatically updated after an optimization is performed.

Default

"OFF"

Accepted
Example

prob$iparam <- list(AUTO_UPDATE_SOL_INFO = "OFF") Groups Overall system MSK_IPAR_BASIS_SOLVE_USE_PLUS_ONE If a slack variable is in the basis, then the corresponding column in the basis is a unit vector with -1 in the right position. However, if this parameter is set to "MSK_ON", -1 is replaced by 1. Default "OFF" Accepted Example prob$iparam <- list(BASIS_SOLVE_USE_PLUS_ONE = "OFF")

Groups

Simplex optimizer

MSK_IPAR_BI_CLEAN_OPTIMIZER

Controls which simplex optimizer is used in the clean-up phase. Anything else than "MSK_OPTIMIZER_PRIMAL_SIMPLEX" or "MSK_OPTIMIZER_DUAL_SIMPLEX" is equivalent to "MSK_OPTIMIZER_FREE_SIMPLEX".

Default

"FREE"

Accepted
Example

prob$iparam <- list(BI_CLEAN_OPTIMIZER = "OPTIMIZER_FREE") Groups MSK_IPAR_BI_IGNORE_MAX_ITER If the parameter MSK_IPAR_INTPNT_BASIS has the value "MSK_BI_NO_ERROR" and the interior-point optimizer has terminated due to maximum number of iterations, then basis identification is performed if this parameter has the value "MSK_ON". Default "OFF" Accepted Example prob$iparam <- list(BI_IGNORE_MAX_ITER = "OFF")

Groups
MSK_IPAR_BI_IGNORE_NUM_ERROR

If the parameter MSK_IPAR_INTPNT_BASIS has the value "MSK_BI_NO_ERROR" and the interior-point optimizer has terminated due to a numerical problem, then basis identification is performed if this parameter has the value "MSK_ON".

Default

"OFF"

Accepted
Example

prob$iparam <- list(BI_IGNORE_NUM_ERROR = "OFF") Groups MSK_IPAR_BI_MAX_ITERATIONS Controls the maximum number of simplex iterations allowed to optimize a basis after the basis identification. Default 1000000 Accepted [0; +inf] Example prob$iparam <- list(BI_MAX_ITERATIONS = 1000000)

Groups

Specifies if the license is kept checked out for the lifetime of the MOSEK environment/model/process ("MSK_ON") or returned to the server immediately after the optimization ("MSK_OFF").

By default the license is checked out for the lifetime of the session at the start of first optimization.

Check-in and check-out of licenses have an overhead. Frequent communication with the license server should be avoided.

Default

"ON"

Accepted
Example

prob$iparam <- list(CACHE_LICENSE = "ON") Groups License manager MSK_IPAR_CHECK_CONVEXITY Specify the level of convexity check on quadratic problems. Default "FULL" Accepted Example prob$iparam <- list(CHECK_CONVEXITY = "CHECK_CONVEXITY_FULL")

Groups

Data check

MSK_IPAR_COMPRESS_STATFILE

Control compression of stat files.

Default

"ON"

Accepted
Example

prob$iparam <- list(COMPRESS_STATFILE = "ON") MSK_IPAR_INFEAS_GENERIC_NAMES Controls whether generic names are used when an infeasible subproblem is created. Default "OFF" Accepted Example prob$iparam <- list(INFEAS_GENERIC_NAMES = "OFF")

Groups

Infeasibility report

MSK_IPAR_INFEAS_PREFER_PRIMAL

If both certificates of primal and dual infeasibility are supplied then only the primal is used when this option is turned on.

Default

"ON"

Accepted
Example

prob$iparam <- list(INFEAS_PREFER_PRIMAL = "ON") Groups Overall solver MSK_IPAR_INFEAS_REPORT_AUTO Controls whether an infeasibility report is automatically produced after the optimization if the problem is primal or dual infeasible. Default "OFF" Accepted Example prob$iparam <- list(INFEAS_REPORT_AUTO = "OFF")

Groups
MSK_IPAR_INFEAS_REPORT_LEVEL

Controls the amount of information presented in an infeasibility report. Higher values imply more information.

Default

1

Accepted

[0; +inf]

Example

prob$iparam <- list(INFEAS_REPORT_LEVEL = 1) Groups MSK_IPAR_INTPNT_BASIS Controls whether the interior-point optimizer also computes an optimal basis. Default "ALWAYS" Accepted Example prob$iparam <- list(INTPNT_BASIS = "BI_ALWAYS")

See also
Groups
MSK_IPAR_INTPNT_DIFF_STEP

Controls whether different step sizes are allowed in the primal and dual space.

Default

"ON"

Accepted

Example

prob$iparam <- list(INTPNT_DIFF_STEP = "ON") Groups Interior-point method MSK_IPAR_INTPNT_HOTSTART Currently not in use. Default "NONE" Accepted Example prob$iparam <- list(INTPNT_HOTSTART = "INTPNT_HOTSTART_NONE")

Groups

Interior-point method

MSK_IPAR_INTPNT_MAX_ITERATIONS

Controls the maximum number of iterations allowed in the interior-point optimizer.

Default

400

Accepted

[0; +inf]

Example

prob$iparam <- list(INTPNT_MAX_ITERATIONS = 400) Groups MSK_IPAR_INTPNT_MAX_NUM_COR Controls the maximum number of correctors allowed by the multiple corrector procedure. A negative value means that MOSEK is making the choice. Default -1 Accepted [-1; +inf] Example prob$iparam <- list(INTPNT_MAX_NUM_COR = -1)

Groups

Interior-point method

MSK_IPAR_INTPNT_MAX_NUM_REFINEMENT_STEPS

Maximum number of steps to be used by the iterative refinement of the search direction. A negative value implies that the optimizer chooses the maximum number of iterative refinement steps.

Default

-1

Accepted

[-inf; +inf]

Example

prob$iparam <- list(INTPNT_MAX_NUM_REFINEMENT_STEPS = -1) Groups Interior-point method MSK_IPAR_INTPNT_OFF_COL_TRH Controls how many offending columns are detected in the Jacobian of the constraint matrix.  $$0$$ no detection $$1$$ aggressive detection $$>1$$ higher values mean less aggressive detection Default 40 Accepted [0; +inf] Example prob$iparam <- list(INTPNT_OFF_COL_TRH = 40)

Groups

Interior-point method

MSK_IPAR_INTPNT_ORDER_GP_NUM_SEEDS

The GP ordering is dependent on a random seed. Therefore, trying several random seeds may lead to a better ordering. This parameter controls the number of random seeds tried.

A value of 0 means that MOSEK makes the choice.

Default

0

Accepted

[0; +inf]

Example

prob$iparam <- list(INTPNT_ORDER_GP_NUM_SEEDS = 0) Groups Interior-point method MSK_IPAR_INTPNT_ORDER_METHOD Controls the ordering strategy used by the interior-point optimizer when factorizing the Newton equation system. Default "FREE" Accepted Example prob$iparam <- list(INTPNT_ORDER_METHOD = "ORDER_METHOD_FREE")

Groups

Interior-point method

MSK_IPAR_INTPNT_PURIFY

Currently not in use.

Default

"NONE"

Accepted
Example

prob$iparam <- list(INTPNT_PURIFY = "PURIFY_NONE") Groups Interior-point method MSK_IPAR_INTPNT_REGULARIZATION_USE Controls whether regularization is allowed. Default "ON" Accepted Example prob$iparam <- list(INTPNT_REGULARIZATION_USE = "ON")

Groups

Interior-point method

MSK_IPAR_INTPNT_SCALING

Controls how the problem is scaled before the interior-point optimizer is used.

Default

"FREE"

Accepted
Example

prob$iparam <- list(INTPNT_SCALING = "SCALING_FREE") Groups Interior-point method MSK_IPAR_INTPNT_SOLVE_FORM Controls whether the primal or the dual problem is solved. Default "FREE" Accepted Example prob$iparam <- list(INTPNT_SOLVE_FORM = "SOLVE_FREE")

Groups

Interior-point method

MSK_IPAR_INTPNT_STARTING_POINT

Starting point used by the interior-point optimizer.

Default

"FREE"

Accepted
Example

prob$iparam <- list(INTPNT_STARTING_POINT = "STARTING_POINT_FREE") Groups Interior-point method MSK_IPAR_LICENSE_DEBUG This option is used to turn on debugging of the license manager. Default "OFF" Accepted Example prob$iparam <- list(LICENSE_DEBUG = "OFF")

Groups

If MSK_IPAR_LICENSE_WAIT is "MSK_ON" and no license is available, then MOSEK sleeps a number of milliseconds between each check of whether a license has become free.

Default

100

Accepted

[0; 1000000]

Example

prob$iparam <- list(LICENSE_PAUSE_TIME = 100) Groups License manager MSK_IPAR_LICENSE_SUPPRESS_EXPIRE_WRNS Controls whether license features expire warnings are suppressed. Default "OFF" Accepted Example prob$iparam <- list(LICENSE_SUPPRESS_EXPIRE_WRNS = "OFF")

Groups

If a license feature expires in a numbers of days less than the value of this parameter then a warning will be issued.

Default

7

Accepted

[0; +inf]

Example

prob$iparam <- list(LICENSE_TRH_EXPIRY_WRN = 7) Groups MSK_IPAR_LICENSE_WAIT If all licenses are in use MOSEK returns with an error code. However, by turning on this parameter MOSEK will wait for an available license. Default "OFF" Accepted Example prob$iparam <- list(LICENSE_WAIT = "OFF")

Groups
MSK_IPAR_LOG

Please note that if a task is employed to solve a sequence of optimization problems the value of this parameter is reduced by the value of MSK_IPAR_LOG_CUT_SECOND_OPT for the second and any subsequent optimizations.

Default

10

Accepted

[0; +inf]

Example

prob$iparam <- list(LOG = 10) See also MSK_IPAR_LOG_CUT_SECOND_OPT Groups MSK_IPAR_LOG_ANA_PRO Controls amount of output from the problem analyzer. Default 1 Accepted [0; +inf] Example prob$iparam <- list(LOG_ANA_PRO = 1)

Groups
MSK_IPAR_LOG_BI

Controls the amount of output printed by the basis identification procedure. A higher level implies that more information is logged.

Default

1

Accepted

[0; +inf]

Example

prob$iparam <- list(LOG_BI = 1) Groups MSK_IPAR_LOG_BI_FREQ Controls how frequently the optimizer outputs information about the basis identification and how frequent the user-defined callback function is called. Default 2500 Accepted [0; +inf] Example prob$iparam <- list(LOG_BI_FREQ = 2500)

Groups
MSK_IPAR_LOG_CHECK_CONVEXITY

Controls logging in convexity check on quadratic problems. Set to a positive value to turn logging on. If a quadratic coefficient matrix is found to violate the requirement of PSD (NSD) then a list of negative (positive) pivot elements is printed. The absolute value of the pivot elements is also shown.

Default

0

Accepted

[0; +inf]

Example

prob$iparam <- list(LOG_CHECK_CONVEXITY = 0) Groups Data check MSK_IPAR_LOG_CUT_SECOND_OPT If a task is employed to solve a sequence of optimization problems, then the value of the log levels is reduced by the value of this parameter. E.g MSK_IPAR_LOG and MSK_IPAR_LOG_SIM are reduced by the value of this parameter for the second and any subsequent optimizations. Default 1 Accepted [0; +inf] Example prob$iparam <- list(LOG_CUT_SECOND_OPT = 1)

See also
Groups
MSK_IPAR_LOG_EXPAND

Controls the amount of logging when a data item such as the maximum number constrains is expanded.

Default

0

Accepted

[0; +inf]

Example

prob$iparam <- list(LOG_EXPAND = 0) Groups MSK_IPAR_LOG_FEAS_REPAIR Controls the amount of output printed when performing feasibility repair. A value higher than one means extensive logging. Default 1 Accepted [0; +inf] Example prob$iparam <- list(LOG_FEAS_REPAIR = 1)

Groups
MSK_IPAR_LOG_FILE

Default

1

Accepted

[0; +inf]

Example

prob$iparam <- list(LOG_FILE = 1) Groups MSK_IPAR_LOG_INCLUDE_SUMMARY Not relevant for this API. Default "OFF" Accepted Example prob$iparam <- list(LOG_INCLUDE_SUMMARY = "OFF")

Groups
MSK_IPAR_LOG_INFEAS_ANA

Controls amount of output printed by the infeasibility analyzer procedures. A higher level implies that more information is logged.

Default

1

Accepted

[0; +inf]

Example

prob$iparam <- list(LOG_INFEAS_ANA = 1) Groups MSK_IPAR_LOG_INTPNT Controls amount of output printed by the interior-point optimizer. A higher level implies that more information is logged. Default 1 Accepted [0; +inf] Example prob$iparam <- list(LOG_INTPNT = 1)

Groups
MSK_IPAR_LOG_LOCAL_INFO

Controls whether local identifying information like environment variables, filenames, IP addresses etc. are printed to the log.

Note that this will only affect some functions. Some functions that specifically emit system information will not be affected.

Default

"ON"

Accepted
Example

prob$iparam <- list(LOG_LOCAL_INFO = "ON") Groups MSK_IPAR_LOG_MIO Controls the log level for the mixed-integer optimizer. A higher level implies that more information is logged. Default 4 Accepted [0; +inf] Example prob$iparam <- list(LOG_MIO = 4)

Groups
MSK_IPAR_LOG_MIO_FREQ

Controls how frequent the mixed-integer optimizer prints the log line. It will print line every time MSK_IPAR_LOG_MIO_FREQ relaxations have been solved.

Default

10

Accepted

[-inf; +inf]

Example

prob$iparam <- list(LOG_MIO_FREQ = 10) Groups MSK_IPAR_LOG_ORDER If turned on, then factor lines are added to the log. Default 1 Accepted [0; +inf] Example prob$iparam <- list(LOG_ORDER = 1)

Groups
MSK_IPAR_LOG_PRESOLVE

Controls amount of output printed by the presolve procedure. A higher level implies that more information is logged.

Default

1

Accepted

[0; +inf]

Example

prob$iparam <- list(LOG_PRESOLVE = 1) Groups Logging MSK_IPAR_LOG_RESPONSE Controls amount of output printed when response codes are reported. A higher level implies that more information is logged. Default 0 Accepted [0; +inf] Example prob$iparam <- list(LOG_RESPONSE = 0)

Groups
MSK_IPAR_LOG_SENSITIVITY

Controls the amount of logging during the sensitivity analysis.

• $$0$$. Means no logging information is produced.

• $$1$$. Timing information is printed.

• $$2$$. Sensitivity results are printed.

Default

1

Accepted

[0; +inf]

Example

prob$iparam <- list(LOG_SENSITIVITY = 1) Groups MSK_IPAR_LOG_SENSITIVITY_OPT Controls the amount of logging from the optimizers employed during the sensitivity analysis. 0 means no logging information is produced. Default 0 Accepted [0; +inf] Example prob$iparam <- list(LOG_SENSITIVITY_OPT = 0)

Groups
MSK_IPAR_LOG_SIM

Controls amount of output printed by the simplex optimizer. A higher level implies that more information is logged.

Default

4

Accepted

[0; +inf]

Example

prob$iparam <- list(LOG_SIM = 4) Groups MSK_IPAR_LOG_SIM_FREQ Controls how frequent the simplex optimizer outputs information about the optimization and how frequent the user-defined callback function is called. Default 1000 Accepted [0; +inf] Example prob$iparam <- list(LOG_SIM_FREQ = 1000)

Groups
MSK_IPAR_LOG_SIM_MINOR

Currently not in use.

Default

1

Accepted

[0; +inf]

Example

prob$iparam <- list(LOG_SIM_MINOR = 1) Groups MSK_IPAR_LOG_STORAGE When turned on, MOSEK prints messages regarding the storage usage and allocation. Default 0 Accepted [0; +inf] Example prob$iparam <- list(LOG_STORAGE = 0)

Groups
MSK_IPAR_MAX_NUM_WARNINGS

Each warning is shown a limited number of times controlled by this parameter. A negative value is identical to infinite number of times.

Default

10

Accepted

[-inf; +inf]

Example

prob$iparam <- list(MAX_NUM_WARNINGS = 10) Groups Output information MSK_IPAR_MIO_BRANCH_DIR Controls whether the mixed-integer optimizer is branching up or down by default. Default "FREE" Accepted Example prob$iparam <- list(MIO_BRANCH_DIR = "BRANCH_DIR_FREE")

Groups

Mixed-integer optimization

MSK_IPAR_MIO_CONIC_OUTER_APPROXIMATION

If this option is turned on outer approximation is used when solving relaxations of conic problems; otherwise interior point is used.

Default

"OFF"

Accepted
Example

prob$iparam <- list(MIO_CONIC_OUTER_APPROXIMATION = "OFF") Groups Mixed-integer optimization MSK_IPAR_MIO_CONSTRUCT_SOL If set to "MSK_ON" and all integer variables have been given a value for which a feasible mixed integer solution exists, then MOSEK generates an initial solution to the mixed integer problem by fixing all integer values and solving the remaining problem. Default "OFF" Accepted Example prob$iparam <- list(MIO_CONSTRUCT_SOL = "OFF")

Groups

Mixed-integer optimization

MSK_IPAR_MIO_CUT_CLIQUE

Controls whether clique cuts should be generated.

Default

"ON"

Accepted
Example

prob$iparam <- list(MIO_CUT_CLIQUE = "ON") Groups Mixed-integer optimization MSK_IPAR_MIO_CUT_CMIR Controls whether mixed integer rounding cuts should be generated. Default "ON" Accepted Example prob$iparam <- list(MIO_CUT_CMIR = "ON")

Groups

Mixed-integer optimization

MSK_IPAR_MIO_CUT_GMI

Controls whether GMI cuts should be generated.

Default

"ON"

Accepted
Example

prob$iparam <- list(MIO_CUT_GMI = "ON") Groups Mixed-integer optimization MSK_IPAR_MIO_CUT_IMPLIED_BOUND Controls whether implied bound cuts should be generated. Default "ON" Accepted Example prob$iparam <- list(MIO_CUT_IMPLIED_BOUND = "ON")

Groups

Mixed-integer optimization

MSK_IPAR_MIO_CUT_KNAPSACK_COVER

Controls whether knapsack cover cuts should be generated.

Default

"OFF"

Accepted
Example

prob$iparam <- list(MIO_CUT_KNAPSACK_COVER = "OFF") Groups Mixed-integer optimization MSK_IPAR_MIO_CUT_LIPRO Controls whether lift-and-project cuts should be generated. Default "OFF" Accepted Example prob$iparam <- list(MIO_CUT_LIPRO = "OFF")

Groups

Mixed-integer optimization

MSK_IPAR_MIO_CUT_SELECTION_LEVEL

Controls how aggressively generated cuts are selected to be included in the relaxation.

• $$-1$$. The optimizer chooses the level of cut selection

• $$0$$. Generated cuts less likely to be added to the relaxation

• $$1$$. Cuts are more aggressively selected to be included in the relaxation

Default

-1

Accepted

[-1; +1]

Example

prob$iparam <- list(MIO_CUT_SELECTION_LEVEL = -1) Groups Mixed-integer optimization MSK_IPAR_MIO_DATA_PERMUTATION_METHOD Controls what problem data permutation method is appplied to mixed-integer problems. Default "NONE" Accepted Example prob$iparam <- list(MIO_DATA_PERMUTATION_METHOD = "MIO_DATA_PERMUTATION_METHOD_NONE")

Groups

Mixed-integer optimization

MSK_IPAR_MIO_FEASPUMP_LEVEL

Controls the way the Feasibility Pump heuristic is employed by the mixed-integer optimizer.

• $$-1$$. The optimizer chooses how the Feasibility Pump is used

• $$0$$. The Feasibility Pump is disabled

• $$1$$. The Feasibility Pump is enabled with an effort to improve solution quality

• $$2$$. The Feasibility Pump is enabled with an effort to reach feasibility early

Default

-1

Accepted

[-1; 2]

Example

prob$iparam <- list(MIO_FEASPUMP_LEVEL = -1) Groups Mixed-integer optimization MSK_IPAR_MIO_HEURISTIC_LEVEL Controls the heuristic employed by the mixed-integer optimizer to locate an initial good integer feasible solution. A value of zero means the heuristic is not used at all. A larger value than $$0$$ means that a gradually more sophisticated heuristic is used which is computationally more expensive. A negative value implies that the optimizer chooses the heuristic. Normally a value around $$3$$ to $$5$$ should be optimal. Default -1 Accepted [-inf; +inf] Example prob$iparam <- list(MIO_HEURISTIC_LEVEL = -1)

Groups

Mixed-integer optimization

MSK_IPAR_MIO_MAX_NUM_BRANCHES

Maximum number of branches allowed during the branch and bound search. A negative value means infinite.

Default

-1

Accepted

[-inf; +inf]

Example

prob$iparam <- list(MIO_MAX_NUM_BRANCHES = -1) Groups MSK_IPAR_MIO_MAX_NUM_RELAXS Maximum number of relaxations allowed during the branch and bound search. A negative value means infinite. Default -1 Accepted [-inf; +inf] Example prob$iparam <- list(MIO_MAX_NUM_RELAXS = -1)

Groups

Mixed-integer optimization

MSK_IPAR_MIO_MAX_NUM_ROOT_CUT_ROUNDS

Maximum number of cut separation rounds at the root node.

Default

100

Accepted

[0; +inf]

Example

prob$iparam <- list(MIO_MAX_NUM_ROOT_CUT_ROUNDS = 100) Groups MSK_IPAR_MIO_MAX_NUM_SOLUTIONS The mixed-integer optimizer can be terminated after a certain number of different feasible solutions has been located. If this parameter has the value $$n>0$$, then the mixed-integer optimizer will be terminated when $$n$$ feasible solutions have been located. Default -1 Accepted [-inf; +inf] Example prob$iparam <- list(MIO_MAX_NUM_SOLUTIONS = -1)

Groups
MSK_IPAR_MIO_MEMORY_EMPHASIS_LEVEL

Controls how much emphasis is put on reducing memory usage. Being more conservative about memory usage may come at the cost of decreased solution speed.

• $$0$$. The optimizer chooses

• $$1$$. More emphasis is put on reducing memory usage and less on speed

Default

0

Accepted

[0; +1]

Example

prob$iparam <- list(MIO_MEMORY_EMPHASIS_LEVEL = 0) Groups Mixed-integer optimization MSK_IPAR_MIO_MODE Controls whether the optimizer includes the integer restrictions and disjunctive constraints when solving a (mixed) integer optimization problem. Default "SATISFIED" Accepted Example prob$iparam <- list(MIO_MODE = "MIO_MODE_SATISFIED")

Groups

Overall solver

MSK_IPAR_MIO_NODE_OPTIMIZER

Controls which optimizer is employed at the non-root nodes in the mixed-integer optimizer.

Default

"FREE"

Accepted
Example

prob$iparam <- list(MIO_NODE_OPTIMIZER = "OPTIMIZER_FREE") Groups Mixed-integer optimization MSK_IPAR_MIO_NODE_SELECTION Controls the node selection strategy employed by the mixed-integer optimizer. Default "FREE" Accepted Example prob$iparam <- list(MIO_NODE_SELECTION = "MIO_NODE_SELECTION_FREE")

Groups

Mixed-integer optimization

MSK_IPAR_MIO_NUMERICAL_EMPHASIS_LEVEL

Controls how much emphasis is put on reducing numerical problems possibly at the expense of solution speed.

• $$0$$. The optimizer chooses

• $$1$$. More emphasis is put on reducing numerical problems

• $$2$$. Even more emphasis

Default

0

Accepted

[0; +2]

Example

prob$iparam <- list(MIO_NUMERICAL_EMPHASIS_LEVEL = 0) Groups Mixed-integer optimization MSK_IPAR_MIO_PERSPECTIVE_REFORMULATE Enables or disables perspective reformulation in presolve. Default "ON" Accepted Example prob$iparam <- list(MIO_PERSPECTIVE_REFORMULATE = "ON")

Groups

Mixed-integer optimization

MSK_IPAR_MIO_PRESOLVE_AGGREGATOR_USE

Controls if the aggregator should be used.

Default

"ON"

Accepted
Example

prob$iparam <- list(MIO_PRESOLVE_AGGREGATOR_USE = "ON") Groups Presolve MSK_IPAR_MIO_PROBING_LEVEL Controls the amount of probing employed by the mixed-integer optimizer in presolve. • $$-1$$. The optimizer chooses the level of probing employed • $$0$$. Probing is disabled • $$1$$. A low amount of probing is employed • $$2$$. A medium amount of probing is employed • $$3$$. A high amount of probing is employed Default -1 Accepted [-1; 3] Example prob$iparam <- list(MIO_PROBING_LEVEL = -1)

Groups

Mixed-integer optimization

MSK_IPAR_MIO_PROPAGATE_OBJECTIVE_CONSTRAINT

Use objective domain propagation.

Default

"OFF"

Accepted
Example

prob$iparam <- list(MIO_PROPAGATE_OBJECTIVE_CONSTRAINT = "OFF") Groups Mixed-integer optimization MSK_IPAR_MIO_QCQO_REFORMULATION_METHOD Controls what reformulation method is applied to mixed-integer quadratic problems. Default "FREE" Accepted Example prob$iparam <- list(MIO_QCQO_REFORMULATION_METHOD = "MIO_QCQO_REFORMULATION_METHOD_FREE")

Groups

Mixed-integer optimization

MSK_IPAR_MIO_RINS_MAX_NODES

Controls the maximum number of nodes allowed in each call to the RINS heuristic. The default value of -1 means that the value is determined automatically. A value of zero turns off the heuristic.

Default

-1

Accepted

[-1; +inf]

Example

prob$iparam <- list(MIO_RINS_MAX_NODES = -1) Groups Mixed-integer optimization MSK_IPAR_MIO_ROOT_OPTIMIZER Controls which optimizer is employed at the root node in the mixed-integer optimizer. Default "FREE" Accepted Example prob$iparam <- list(MIO_ROOT_OPTIMIZER = "OPTIMIZER_FREE")

Groups

Mixed-integer optimization

MSK_IPAR_MIO_ROOT_REPEAT_PRESOLVE_LEVEL

Controls whether presolve can be repeated at root node.

• $$-1$$. The optimizer chooses whether presolve is repeated

• $$0$$. Never repeat presolve

• $$1$$. Always repeat presolve

Default

-1

Accepted

[-1; 1]

Example

prob$iparam <- list(MIO_ROOT_REPEAT_PRESOLVE_LEVEL = -1) Groups Mixed-integer optimization MSK_IPAR_MIO_SEED Sets the random seed used for randomization in the mixed integer optimizer. Selecting a different seed can change the path the optimizer takes to the optimal solution. Default 42 Accepted [0; +inf] Example prob$iparam <- list(MIO_SEED = 42)

Groups

Mixed-integer optimization

MSK_IPAR_MIO_SYMMETRY_LEVEL

Controls the amount of symmetry detection and handling employed by the mixed-integer optimizer in presolve.

• $$-1$$. The optimizer chooses the level of symmetry detection and handling employed

• $$0$$. Symmetry detection and handling is disabled

• $$1$$. A low amount of symmetry detection and handling is employed

• $$2$$. A medium amount of symmetry detection and handling is employed

• $$3$$. A high amount of symmetry detection and handling is employed

• $$4$$. An extremely high amount of symmetry detection and handling is employed

Default

-1

Accepted

[-1; 4]

Example

prob$iparam <- list(MIO_SYMMETRY_LEVEL = -1) Groups Mixed-integer optimization MSK_IPAR_MIO_VB_DETECTION_LEVEL Controls how much effort is put into detecting variable bounds. • $$-1$$. The optimizer chooses • $$0$$. No variable bounds are detected • $$1$$. Only detect variable bounds that are directly represented in the problem • $$2$$. Detect variable bounds in probing Default -1 Accepted [-1; +2] Example prob$iparam <- list(MIO_VB_DETECTION_LEVEL = -1)

Groups

Mixed-integer optimization

MSK_IPAR_MT_SPINCOUNT

Set the number of iterations to spin before sleeping.

Default

0

Accepted

[0; 1000000000]

Example

prob$iparam <- list(MT_SPINCOUNT = 0) Groups Overall system MSK_IPAR_NG Not in use. Default "OFF" Accepted Example prob$iparam <- list(NG = "OFF")

Controls the number of threads employed by the optimizer. If set to 0 the number of threads used will be equal to the number of cores detected on the machine.

Default

0

Accepted

[0; +inf]

Example

prob$iparam <- list(NUM_THREADS = 0) Groups Overall system MSK_IPAR_OPF_WRITE_HEADER Write a text header with date and MOSEK version in an OPF file. Default "ON" Accepted Example prob$iparam <- list(OPF_WRITE_HEADER = "ON")

Groups

Data input/output

MSK_IPAR_OPF_WRITE_HINTS

Write a hint section with problem dimensions in the beginning of an OPF file.

Default

"ON"

Accepted
Example

prob$iparam <- list(OPF_WRITE_HINTS = "ON") Groups Data input/output MSK_IPAR_OPF_WRITE_LINE_LENGTH Aim to keep lines in OPF files not much longer than this. Default 80 Accepted [0; +inf] Example prob$iparam <- list(OPF_WRITE_LINE_LENGTH = 80)

Groups

Data input/output

MSK_IPAR_OPF_WRITE_PARAMETERS

Write a parameter section in an OPF file.

Default

"OFF"

Accepted
Example

prob$iparam <- list(OPF_WRITE_PARAMETERS = "OFF") Groups Data input/output MSK_IPAR_OPF_WRITE_PROBLEM Write objective, constraints, bounds etc. to an OPF file. Default "ON" Accepted Example prob$iparam <- list(OPF_WRITE_PROBLEM = "ON")

Groups

Data input/output

MSK_IPAR_OPF_WRITE_SOL_BAS

If MSK_IPAR_OPF_WRITE_SOLUTIONS is "MSK_ON" and a basic solution is defined, include the basic solution in OPF files.

Default

"ON"

Accepted
Example

prob$iparam <- list(OPF_WRITE_SOL_BAS = "ON") Groups Data input/output MSK_IPAR_OPF_WRITE_SOL_ITG If MSK_IPAR_OPF_WRITE_SOLUTIONS is "MSK_ON" and an integer solution is defined, write the integer solution in OPF files. Default "ON" Accepted Example prob$iparam <- list(OPF_WRITE_SOL_ITG = "ON")

Groups

Data input/output

MSK_IPAR_OPF_WRITE_SOL_ITR

If MSK_IPAR_OPF_WRITE_SOLUTIONS is "MSK_ON" and an interior solution is defined, write the interior solution in OPF files.

Default

"ON"

Accepted
Example

prob$iparam <- list(OPF_WRITE_SOL_ITR = "ON") Groups Data input/output MSK_IPAR_OPF_WRITE_SOLUTIONS Enable inclusion of solutions in the OPF files. Default "OFF" Accepted Example prob$iparam <- list(OPF_WRITE_SOLUTIONS = "OFF")

Groups

Data input/output

MSK_IPAR_OPTIMIZER

The parameter controls which optimizer is used to optimize the task.

Default

"FREE"

Accepted
Example

prob$iparam <- list(OPTIMIZER = "OPTIMIZER_FREE") Groups Overall solver MSK_IPAR_PARAM_READ_CASE_NAME If turned on, then names in the parameter file are case sensitive. Default "ON" Accepted Example prob$iparam <- list(PARAM_READ_CASE_NAME = "ON")

Groups

Data input/output

If turned on, then errors in parameter settings is ignored.

Default

"OFF"

Accepted
Example

prob$iparam <- list(PARAM_READ_IGN_ERROR = "OFF") Groups Data input/output MSK_IPAR_PRESOLVE_ELIMINATOR_MAX_FILL Controls the maximum amount of fill-in that can be created by one pivot in the elimination phase of the presolve. A negative value means the parameter value is selected automatically. Default -1 Accepted [-inf; +inf] Example prob$iparam <- list(PRESOLVE_ELIMINATOR_MAX_FILL = -1)

Groups

Presolve

MSK_IPAR_PRESOLVE_ELIMINATOR_MAX_NUM_TRIES

Control the maximum number of times the eliminator is tried. A negative value implies MOSEK decides.

Default

-1

Accepted

[-inf; +inf]

Example

prob$iparam <- list(PRESOLVE_ELIMINATOR_MAX_NUM_TRIES = -1) Groups Presolve MSK_IPAR_PRESOLVE_LEVEL Currently not used. Default -1 Accepted [-inf; +inf] Example prob$iparam <- list(PRESOLVE_LEVEL = -1)

Groups
MSK_IPAR_PRESOLVE_LINDEP_ABS_WORK_TRH

Controls linear dependency check in presolve. The linear dependency check is potentially computationally expensive.

Default

100

Accepted

[-inf; +inf]

Example

prob$iparam <- list(PRESOLVE_LINDEP_ABS_WORK_TRH = 100) Groups Presolve MSK_IPAR_PRESOLVE_LINDEP_REL_WORK_TRH Controls linear dependency check in presolve. The linear dependency check is potentially computationally expensive. Default 100 Accepted [-inf; +inf] Example prob$iparam <- list(PRESOLVE_LINDEP_REL_WORK_TRH = 100)

Groups

Presolve

MSK_IPAR_PRESOLVE_LINDEP_USE

Controls whether the linear constraints are checked for linear dependencies.

Default

"ON"

Accepted
Example

prob$iparam <- list(PRESOLVE_LINDEP_USE = "ON") Groups Presolve MSK_IPAR_PRESOLVE_MAX_NUM_PASS Control the maximum number of times presolve passes over the problem. A negative value implies MOSEK decides. Default -1 Accepted [-inf; +inf] Example prob$iparam <- list(PRESOLVE_MAX_NUM_PASS = -1)

Groups

Presolve

MSK_IPAR_PRESOLVE_MAX_NUM_REDUCTIONS

Controls the maximum number of reductions performed by the presolve. The value of the parameter is normally only changed in connection with debugging. A negative value implies that an infinite number of reductions are allowed.

Default

-1

Accepted

[-inf; +inf]

Example

prob$iparam <- list(PRESOLVE_MAX_NUM_REDUCTIONS = -1) Groups MSK_IPAR_PRESOLVE_USE Controls whether the presolve is applied to a problem before it is optimized. Default "FREE" Accepted Example prob$iparam <- list(PRESOLVE_USE = "PRESOLVE_MODE_FREE")

Groups
MSK_IPAR_PRIMAL_REPAIR_OPTIMIZER

Controls which optimizer that is used to find the optimal repair.

Default

"FREE"

Accepted
Example

prob$iparam <- list(PRIMAL_REPAIR_OPTIMIZER = "OPTIMIZER_FREE") Groups Overall solver MSK_IPAR_PTF_WRITE_TRANSFORM If MSK_IPAR_PTF_WRITE_TRANSFORM is "MSK_ON", constraint blocks with identifiable conic slacks are transformed into conic constraints and the slacks are eliminated. Default "ON" Accepted Example prob$iparam <- list(PTF_WRITE_TRANSFORM = "ON")

Groups

Data input/output

Default

"OFF"

Accepted
Example

prob$iparam <- list(READ_DEBUG = "OFF") Groups Data input/output MSK_IPAR_READ_KEEP_FREE_CON Controls whether the free constraints are included in the problem. Default "OFF" Accepted Example prob$iparam <- list(READ_KEEP_FREE_CON = "OFF")

Groups

Data input/output

If this option is turned on, MOSEK will drop variables that are defined for the first time in the bounds section.

Default

"OFF"

Accepted
Example

prob$iparam <- list(READ_LP_DROP_NEW_VARS_IN_BOU = "OFF") Groups Data input/output MSK_IPAR_READ_LP_QUOTED_NAMES If a name is in quotes when reading an LP file, the quotes will be removed. Default "ON" Accepted Example prob$iparam <- list(READ_LP_QUOTED_NAMES = "ON")

Groups

Data input/output

Controls how strictly the MPS file reader interprets the MPS format.

Default

"FREE"

Accepted
Example

prob$iparam <- list(READ_MPS_FORMAT = "MPS_FORMAT_FREE") Groups Data input/output MSK_IPAR_READ_MPS_WIDTH Controls the maximal number of characters allowed in one line of the MPS file. Default 1024 Accepted [80; +inf] Example prob$iparam <- list(READ_MPS_WIDTH = 1024)

Groups

Data input/output

Controls whether MOSEK should ignore the parameter setting defined in the task file and use the default parameter setting instead.

Default

"OFF"

Accepted
Example

prob$iparam <- list(READ_TASK_IGNORE_PARAM = "OFF") Groups Data input/output MSK_IPAR_REMOTE_USE_COMPRESSION Use compression when sending data to an optimization server. Default "ZSTD" Accepted Example prob$iparam <- list(REMOTE_USE_COMPRESSION = "COMPRESS_ZSTD")

MSK_IPAR_REMOVE_UNUSED_SOLUTIONS

Removes unused solutions before the optimization is performed.

Default

"OFF"

Accepted
Example

prob$iparam <- list(REMOVE_UNUSED_SOLUTIONS = "OFF") Groups Overall system MSK_IPAR_SENSITIVITY_ALL Not applicable. Default "OFF" Accepted Example prob$iparam <- list(SENSITIVITY_ALL = "OFF")

Groups

Overall solver

MSK_IPAR_SENSITIVITY_OPTIMIZER

Controls which optimizer is used for optimal partition sensitivity analysis.

Default

"FREE_SIMPLEX"

Accepted
Example

prob$iparam <- list(SENSITIVITY_OPTIMIZER = "OPTIMIZER_FREE_SIMPLEX") Groups MSK_IPAR_SENSITIVITY_TYPE Controls which type of sensitivity analysis is to be performed. Default "BASIS" Accepted "BASIS" Example prob$iparam <- list(SENSITIVITY_TYPE = "SENSITIVITY_TYPE_BASIS")

Groups

Overall solver

MSK_IPAR_SIM_BASIS_FACTOR_USE

Controls whether an LU factorization of the basis is used in a hot-start. Forcing a refactorization sometimes improves the stability of the simplex optimizers, but in most cases there is a performance penalty.

Default

"ON"

Accepted
Example

prob$iparam <- list(SIM_BASIS_FACTOR_USE = "ON") Groups Simplex optimizer MSK_IPAR_SIM_DEGEN Controls how aggressively degeneration is handled. Default "FREE" Accepted Example prob$iparam <- list(SIM_DEGEN = "SIM_DEGEN_FREE")

Groups

Simplex optimizer

MSK_IPAR_SIM_DETECT_PWL

Not in use.

Default

"ON"

Accepted

Example

prob$iparam <- list(SIM_DETECT_PWL = "ON") Groups Simplex optimizer MSK_IPAR_SIM_DUAL_CRASH Controls whether crashing is performed in the dual simplex optimizer. If this parameter is set to $$x$$, then a crash will be performed if a basis consists of more than $$(100-x)\mod f_v$$ entries, where $$f_v$$ is the number of fixed variables. Default 90 Accepted [0; +inf] Example prob$iparam <- list(SIM_DUAL_CRASH = 90)

Groups

Dual simplex

MSK_IPAR_SIM_DUAL_PHASEONE_METHOD

An experimental feature.

Default

0

Accepted

[0; 10]

Example

prob$iparam <- list(SIM_DUAL_PHASEONE_METHOD = 0) Groups Simplex optimizer MSK_IPAR_SIM_DUAL_RESTRICT_SELECTION The dual simplex optimizer can use a so-called restricted selection/pricing strategy to choose the outgoing variable. Hence, if restricted selection is applied, then the dual simplex optimizer first choose a subset of all the potential outgoing variables. Next, for some time it will choose the outgoing variable only among the subset. From time to time the subset is redefined. A larger value of this parameter implies that the optimizer will be more aggressive in its restriction strategy, i.e. a value of 0 implies that the restriction strategy is not applied at all. Default 50 Accepted [0; 100] Example prob$iparam <- list(SIM_DUAL_RESTRICT_SELECTION = 50)

Groups

Dual simplex

MSK_IPAR_SIM_DUAL_SELECTION

Controls the choice of the incoming variable, known as the selection strategy, in the dual simplex optimizer.

Default

"FREE"

Accepted
Example

prob$iparam <- list(SIM_DUAL_SELECTION = "SIM_SELECTION_FREE") Groups Dual simplex MSK_IPAR_SIM_EXPLOIT_DUPVEC Controls if the simplex optimizers are allowed to exploit duplicated columns. Default "OFF" Accepted Example prob$iparam <- list(SIM_EXPLOIT_DUPVEC = "SIM_EXPLOIT_DUPVEC_OFF")

Groups

Simplex optimizer

MSK_IPAR_SIM_HOTSTART

Controls the type of hot-start that the simplex optimizer perform.

Default

"FREE"

Accepted
Example

prob$iparam <- list(SIM_HOTSTART = "SIM_HOTSTART_FREE") Groups Simplex optimizer MSK_IPAR_SIM_HOTSTART_LU Determines if the simplex optimizer should exploit the initial factorization. Default "ON" Accepted Example prob$iparam <- list(SIM_HOTSTART_LU = "ON")

Groups

Simplex optimizer

MSK_IPAR_SIM_MAX_ITERATIONS

Maximum number of iterations that can be used by a simplex optimizer.

Default

10000000

Accepted

[0; +inf]

Example

prob$iparam <- list(SIM_MAX_ITERATIONS = 10000000) Groups MSK_IPAR_SIM_MAX_NUM_SETBACKS Controls how many set-backs are allowed within a simplex optimizer. A set-back is an event where the optimizer moves in the wrong direction. This is impossible in theory but may happen due to numerical problems. Default 250 Accepted [0; +inf] Example prob$iparam <- list(SIM_MAX_NUM_SETBACKS = 250)

Groups

Simplex optimizer

MSK_IPAR_SIM_NON_SINGULAR

Controls if the simplex optimizer ensures a non-singular basis, if possible.

Default

"ON"

Accepted
Example

prob$iparam <- list(SIM_NON_SINGULAR = "ON") Groups Simplex optimizer MSK_IPAR_SIM_PRIMAL_CRASH Controls whether crashing is performed in the primal simplex optimizer. In general, if a basis consists of more than (100-this parameter value)% fixed variables, then a crash will be performed. Default 90 Accepted [0; +inf] Example prob$iparam <- list(SIM_PRIMAL_CRASH = 90)

Groups

Primal simplex

MSK_IPAR_SIM_PRIMAL_PHASEONE_METHOD

An experimental feature.

Default

0

Accepted

[0; 10]

Example

prob$iparam <- list(SIM_PRIMAL_PHASEONE_METHOD = 0) Groups Simplex optimizer MSK_IPAR_SIM_PRIMAL_RESTRICT_SELECTION The primal simplex optimizer can use a so-called restricted selection/pricing strategy to choose the outgoing variable. Hence, if restricted selection is applied, then the primal simplex optimizer first choose a subset of all the potential incoming variables. Next, for some time it will choose the incoming variable only among the subset. From time to time the subset is redefined. A larger value of this parameter implies that the optimizer will be more aggressive in its restriction strategy, i.e. a value of 0 implies that the restriction strategy is not applied at all. Default 50 Accepted [0; 100] Example prob$iparam <- list(SIM_PRIMAL_RESTRICT_SELECTION = 50)

Groups

Primal simplex

MSK_IPAR_SIM_PRIMAL_SELECTION

Controls the choice of the incoming variable, known as the selection strategy, in the primal simplex optimizer.

Default

"FREE"

Accepted
Example

prob$iparam <- list(SIM_PRIMAL_SELECTION = "SIM_SELECTION_FREE") Groups Primal simplex MSK_IPAR_SIM_REFACTOR_FREQ Controls how frequent the basis is refactorized. The value 0 means that the optimizer determines the best point of refactorization. It is strongly recommended NOT to change this parameter. Default 0 Accepted [0; +inf] Example prob$iparam <- list(SIM_REFACTOR_FREQ = 0)

Groups

Simplex optimizer

MSK_IPAR_SIM_REFORMULATION

Controls if the simplex optimizers are allowed to reformulate the problem.

Default

"OFF"

Accepted
Example

prob$iparam <- list(SIM_REFORMULATION = "SIM_REFORMULATION_OFF") Groups Simplex optimizer MSK_IPAR_SIM_SAVE_LU Controls if the LU factorization stored should be replaced with the LU factorization corresponding to the initial basis. Default "OFF" Accepted Example prob$iparam <- list(SIM_SAVE_LU = "OFF")

Groups

Simplex optimizer

MSK_IPAR_SIM_SCALING

Controls how much effort is used in scaling the problem before a simplex optimizer is used.

Default

"FREE"

Accepted
Example

prob$iparam <- list(SIM_SCALING = "SCALING_FREE") Groups Simplex optimizer MSK_IPAR_SIM_SCALING_METHOD Controls how the problem is scaled before a simplex optimizer is used. Default "POW2" Accepted Example prob$iparam <- list(SIM_SCALING_METHOD = "SCALING_METHOD_POW2")

Groups

Simplex optimizer

MSK_IPAR_SIM_SEED

Sets the random seed used for randomization in the simplex optimizers.

Default

23456

Accepted

[0; 32749]

Example

prob$iparam <- list(SIM_SEED = 23456) Groups Simplex optimizer MSK_IPAR_SIM_SOLVE_FORM Controls whether the primal or the dual problem is solved by the primal-/dual-simplex optimizer. Default "FREE" Accepted Example prob$iparam <- list(SIM_SOLVE_FORM = "SOLVE_FREE")

Groups

Simplex optimizer

MSK_IPAR_SIM_STABILITY_PRIORITY

Controls how high priority the numerical stability should be given.

Default

50

Accepted

[0; 100]

Example

prob$iparam <- list(SIM_STABILITY_PRIORITY = 50) Groups Simplex optimizer MSK_IPAR_SIM_SWITCH_OPTIMIZER The simplex optimizer sometimes chooses to solve the dual problem instead of the primal problem. This implies that if you have chosen to use the dual simplex optimizer and the problem is dualized, then it actually makes sense to use the primal simplex optimizer instead. If this parameter is on and the problem is dualized and furthermore the simplex optimizer is chosen to be the primal (dual) one, then it is switched to the dual (primal). Default "OFF" Accepted Example prob$iparam <- list(SIM_SWITCH_OPTIMIZER = "OFF")

Groups

Simplex optimizer

MSK_IPAR_SOL_FILTER_KEEP_BASIC

If turned on, then basic and super basic constraints and variables are written to the solution file independent of the filter setting.

Default

"OFF"

Accepted
Example

prob$iparam <- list(SOL_FILTER_KEEP_BASIC = "OFF") Groups Solution input/output MSK_IPAR_SOL_FILTER_KEEP_RANGED If turned on, then ranged constraints and variables are written to the solution file independent of the filter setting. Default "OFF" Accepted Example prob$iparam <- list(SOL_FILTER_KEEP_RANGED = "OFF")

Groups

Solution input/output

When a solution is read by MOSEK and some constraint, variable or cone names contain blanks, then a maximum name width much be specified. A negative value implies that no name contain blanks.

Default

-1

Accepted

[-inf; +inf]

Example

prob$iparam <- list(SOL_READ_NAME_WIDTH = -1) Groups MSK_IPAR_SOL_READ_WIDTH Controls the maximal acceptable width of line in the solutions when read by MOSEK. Default 1024 Accepted [80; +inf] Example prob$iparam <- list(SOL_READ_WIDTH = 1024)

Groups
MSK_IPAR_SOLUTION_CALLBACK

Indicates whether solution callbacks will be performed during the optimization.

Default

"OFF"

Accepted
Example

prob$iparam <- list(SOLUTION_CALLBACK = "OFF") Groups MSK_IPAR_TIMING_LEVEL Controls the amount of timing performed inside MOSEK. Default 1 Accepted [0; +inf] Example prob$iparam <- list(TIMING_LEVEL = 1)

Groups

Overall system

MSK_IPAR_WRITE_BAS_CONSTRAINTS

Controls whether the constraint section is written to the basic solution file.

Default

"ON"

Accepted
Example

prob$iparam <- list(WRITE_BAS_CONSTRAINTS = "ON") Groups MSK_IPAR_WRITE_BAS_HEAD Controls whether the header section is written to the basic solution file. Default "ON" Accepted Example prob$iparam <- list(WRITE_BAS_HEAD = "ON")

Groups
MSK_IPAR_WRITE_BAS_VARIABLES

Controls whether the variables section is written to the basic solution file.

Default

"ON"

Accepted
Example

prob$iparam <- list(WRITE_BAS_VARIABLES = "ON") Groups MSK_IPAR_WRITE_COMPRESSION Controls whether the data file is compressed while it is written. 0 means no compression while higher values mean more compression. Default 9 Accepted [0; +inf] Example prob$iparam <- list(WRITE_COMPRESSION = 9)

Groups

Data input/output

MSK_IPAR_WRITE_DATA_PARAM

If this option is turned on the parameter settings are written to the data file as parameters.

Default

"OFF"

Accepted
Example

prob$iparam <- list(WRITE_DATA_PARAM = "OFF") Groups Data input/output MSK_IPAR_WRITE_FREE_CON Controls whether the free constraints are written to the data file. Default "ON" Accepted Example prob$iparam <- list(WRITE_FREE_CON = "ON")

Groups

Data input/output

MSK_IPAR_WRITE_GENERIC_NAMES

Controls whether generic names should be used instead of user-defined names when writing to the data file.

Default

"OFF"

Accepted
Example

prob$iparam <- list(WRITE_GENERIC_NAMES = "OFF") Groups Data input/output MSK_IPAR_WRITE_GENERIC_NAMES_IO Index origin used in generic names. Default 1 Accepted [0; +inf] Example prob$iparam <- list(WRITE_GENERIC_NAMES_IO = 1)

Groups

Data input/output

MSK_IPAR_WRITE_IGNORE_INCOMPATIBLE_ITEMS

Controls if the writer ignores incompatible problem items when writing files.

Default

"OFF"

Accepted

• "ON": Ignore items that cannot be written to the current output file format.

• "OFF": Produce an error if the problem contains items that cannot the written to the current output file format.

Example

prob$iparam <- list(WRITE_IGNORE_INCOMPATIBLE_ITEMS = "OFF") Groups Data input/output MSK_IPAR_WRITE_INT_CONSTRAINTS Controls whether the constraint section is written to the integer solution file. Default "ON" Accepted Example prob$iparam <- list(WRITE_INT_CONSTRAINTS = "ON")

Groups

Controls whether the header section is written to the integer solution file.

Default

"ON"

Accepted
Example

prob$iparam <- list(WRITE_INT_HEAD = "ON") Groups MSK_IPAR_WRITE_INT_VARIABLES Controls whether the variables section is written to the integer solution file. Default "ON" Accepted Example prob$iparam <- list(WRITE_INT_VARIABLES = "ON")

Groups
MSK_IPAR_WRITE_JSON_INDENTATION

When set, the JSON task and solution files are written with indentation for better readability.

Default

"OFF"

Accepted
Example

prob$iparam <- list(WRITE_JSON_INDENTATION = "OFF") Groups Data input/output MSK_IPAR_WRITE_LP_FULL_OBJ Write all variables, including the ones with 0-coefficients, in the objective. Default "ON" Accepted Example prob$iparam <- list(WRITE_LP_FULL_OBJ = "ON")

Groups

Data input/output

MSK_IPAR_WRITE_LP_LINE_WIDTH

Maximum width of line in an LP file written by MOSEK.

Default

80

Accepted

[40; +inf]

Example

prob$iparam <- list(WRITE_LP_LINE_WIDTH = 80) Groups Data input/output MSK_IPAR_WRITE_LP_QUOTED_NAMES If this option is turned on, then MOSEK will quote invalid LP names when writing an LP file. Default "ON" Accepted Example prob$iparam <- list(WRITE_LP_QUOTED_NAMES = "ON")

Groups

Data input/output

MSK_IPAR_WRITE_LP_STRICT_FORMAT

Controls whether LP output files satisfy the LP format strictly.

Default

"OFF"

Accepted
Example

prob$iparam <- list(WRITE_LP_STRICT_FORMAT = "OFF") Groups Data input/output MSK_IPAR_WRITE_LP_TERMS_PER_LINE Maximum number of terms on a single line in an LP file written by MOSEK. 0 means unlimited. Default 10 Accepted [0; +inf] Example prob$iparam <- list(WRITE_LP_TERMS_PER_LINE = 10)

Groups

Data input/output

MSK_IPAR_WRITE_MPS_FORMAT

Controls in which format the MPS is written.

Default

"FREE"

Accepted
Example

prob$iparam <- list(WRITE_MPS_FORMAT = "MPS_FORMAT_FREE") Groups Data input/output MSK_IPAR_WRITE_MPS_INT Controls if marker records are written to the MPS file to indicate whether variables are integer restricted. Default "ON" Accepted Example prob$iparam <- list(WRITE_MPS_INT = "ON")

Groups

Data input/output

MSK_IPAR_WRITE_SOL_BARVARIABLES

Controls whether the symmetric matrix variables section is written to the solution file.

Default

"ON"

Accepted
Example

prob$iparam <- list(WRITE_SOL_BARVARIABLES = "ON") Groups MSK_IPAR_WRITE_SOL_CONSTRAINTS Controls whether the constraint section is written to the solution file. Default "ON" Accepted Example prob$iparam <- list(WRITE_SOL_CONSTRAINTS = "ON")

Groups

Controls whether the header section is written to the solution file.

Default

"ON"

Accepted
Example

prob$iparam <- list(WRITE_SOL_HEAD = "ON") Groups MSK_IPAR_WRITE_SOL_IGNORE_INVALID_NAMES Even if the names are invalid MPS names, then they are employed when writing the solution file. Default "OFF" Accepted Example prob$iparam <- list(WRITE_SOL_IGNORE_INVALID_NAMES = "OFF")

Groups
MSK_IPAR_WRITE_SOL_VARIABLES

Controls whether the variables section is written to the solution file.

Default

"ON"

Accepted
Example

prob$iparam <- list(WRITE_SOL_VARIABLES = "ON") Groups MSK_IPAR_WRITE_TASK_INC_SOL Controls whether the solutions are stored in the task file too. Default "ON" Accepted Example prob$iparam <- list(WRITE_TASK_INC_SOL = "ON")

Groups

Data input/output

MSK_IPAR_WRITE_XML_MODE

Controls if linear coefficients should be written by row or column when writing in the XML file format.

Default

"ROW"

Accepted
Example

prob$iparam <- list(WRITE_XML_MODE = "WRITE_XML_MODE_ROW") Groups Data input/output ## 13.3.3 String parameters¶ sparam The enumeration type containing all string parameters. MSK_SPAR_BAS_SOL_FILE_NAME Name of the bas solution file. Accepted Any valid file name. Example prob$sparam <- list(BAS_SOL_FILE_NAME = "somevalue")

Groups
MSK_SPAR_DATA_FILE_NAME

Data are read and written to this file.

Accepted

Any valid file name.

Example

prob$sparam <- list(DATA_FILE_NAME = "somevalue") Groups Data input/output MSK_SPAR_DEBUG_FILE_NAME MOSEK debug file. Accepted Any valid file name. Example prob$sparam <- list(DEBUG_FILE_NAME = "somevalue")

Groups

Data input/output

MSK_SPAR_INT_SOL_FILE_NAME

Name of the int solution file.

Accepted

Any valid file name.

Example

prob$sparam <- list(INT_SOL_FILE_NAME = "somevalue") Groups MSK_SPAR_ITR_SOL_FILE_NAME Name of the itr solution file. Accepted Any valid file name. Example prob$sparam <- list(ITR_SOL_FILE_NAME = "somevalue")

Groups
MSK_SPAR_MIO_DEBUG_STRING

For internal debugging purposes.

Accepted

Any valid string.

Example

prob$sparam <- list(MIO_DEBUG_STRING = "somevalue") Groups Data input/output MSK_SPAR_PARAM_COMMENT_SIGN Only the first character in this string is used. It is considered as a start of comment sign in the MOSEK parameter file. Spaces are ignored in the string. Default %% Accepted Any valid string. Example prob$sparam <- list(PARAM_COMMENT_SIGN = "%%")

Groups

Data input/output

Modifications to the parameter database is read from this file.

Accepted

Any valid file name.

Example

prob$sparam <- list(PARAM_READ_FILE_NAME = "somevalue") Groups Data input/output MSK_SPAR_PARAM_WRITE_FILE_NAME The parameter database is written to this file. Accepted Any valid file name. Example prob$sparam <- list(PARAM_WRITE_FILE_NAME = "somevalue")

Groups

Data input/output

Name of the BOUNDS vector used. An empty name means that the first BOUNDS vector is used.

Accepted

Any valid MPS name.

Example

prob$sparam <- list(READ_MPS_BOU_NAME = "somevalue") Groups Data input/output MSK_SPAR_READ_MPS_OBJ_NAME Name of the free constraint used as objective function. An empty name means that the first constraint is used as objective function. Accepted Any valid MPS name. Example prob$sparam <- list(READ_MPS_OBJ_NAME = "somevalue")

Groups

Data input/output

Name of the RANGE vector used. An empty name means that the first RANGE vector is used.

Accepted

Any valid MPS name.

Example

prob$sparam <- list(READ_MPS_RAN_NAME = "somevalue") Groups Data input/output MSK_SPAR_READ_MPS_RHS_NAME Name of the RHS used. An empty name means that the first RHS vector is used. Accepted Any valid MPS name. Example prob$sparam <- list(READ_MPS_RHS_NAME = "somevalue")

Groups

Data input/output

MSK_SPAR_REMOTE_OPTSERVER_HOST

URL of the remote optimization server in the format (http|https)://server:port. If set, all subsequent calls to any MOSEK function that involves synchronous optimization will be sent to the specified OptServer instead of being executed locally. Passing empty string deactivates this redirection.

Accepted

Any valid URL.

Example

prob$sparam <- list(REMOTE_OPTSERVER_HOST = "somevalue") Groups Overall system MSK_SPAR_REMOTE_TLS_CERT List of known server certificates in PEM format. Accepted PEM files separated by new-lines. Example prob$sparam <- list(REMOTE_TLS_CERT = "somevalue")

Groups

Overall system

MSK_SPAR_REMOTE_TLS_CERT_PATH

Path to known server certificates in PEM format.

Accepted

Any valid path.

Example

prob$sparam <- list(REMOTE_TLS_CERT_PATH = "somevalue") Groups Overall system MSK_SPAR_SENSITIVITY_FILE_NAME If defined, MOSEK reads this file as a sensitivity analysis data file specifying the type of analysis to be done. Accepted Any valid string. Example prob$sparam <- list(SENSITIVITY_FILE_NAME = "somevalue")

Groups

Data input/output

MSK_SPAR_SENSITIVITY_RES_FILE_NAME
Accepted

Any valid string.

Example

prob$sparam <- list(SENSITIVITY_RES_FILE_NAME = "somevalue") Groups Data input/output MSK_SPAR_SOL_FILTER_XC_LOW A filter used to determine which constraints should be listed in the solution file. A value of $$0.5$$ means that all constraints having xc[i]>0.5 should be listed, whereas +0.5 means that all constraints having xc[i]>=blc[i]+0.5 should be listed. An empty filter means that no filter is applied. Accepted Any valid filter. Example prob$sparam <- list(SOL_FILTER_XC_LOW = "somevalue")

Groups
MSK_SPAR_SOL_FILTER_XC_UPR

A filter used to determine which constraints should be listed in the solution file. A value of 0.5 means that all constraints having xc[i]<0.5 should be listed, whereas -0.5 means all constraints having xc[i]<=buc[i]-0.5 should be listed. An empty filter means that no filter is applied.

Accepted

Any valid filter.

Example

prob$sparam <- list(SOL_FILTER_XC_UPR = "somevalue") Groups MSK_SPAR_SOL_FILTER_XX_LOW A filter used to determine which variables should be listed in the solution file. A value of “0.5” means that all constraints having xx[j]>=0.5 should be listed, whereas “+0.5” means that all constraints having xx[j]>=blx[j]+0.5 should be listed. An empty filter means no filter is applied. Accepted Any valid filter. Example prob$sparam <- list(SOL_FILTER_XX_LOW = "somevalue")

Groups
MSK_SPAR_SOL_FILTER_XX_UPR

A filter used to determine which variables should be listed in the solution file. A value of “0.5” means that all constraints having xx[j]<0.5 should be printed, whereas “-0.5” means all constraints having xx[j]<=bux[j]-0.5 should be listed. An empty filter means no filter is applied.

Accepted

Any valid file name.

Example

prob$sparam <- list(SOL_FILTER_XX_UPR = "somevalue") Groups MSK_SPAR_STAT_KEY Key used when writing the summary file. Accepted Any valid string. Example prob$sparam <- list(STAT_KEY = "somevalue")

Groups

Data input/output

MSK_SPAR_STAT_NAME

Name used when writing the statistics file.

Accepted

Any valid XML string.

Example

prob$sparam <- list(STAT_NAME = "somevalue") Groups Data input/output MSK_SPAR_WRITE_LP_GEN_VAR_NAME Sometimes when an LP file is written additional variables must be inserted. They will have the prefix denoted by this parameter. Default xmskgen Accepted Any valid string. Example prob$sparam <- list(WRITE_LP_GEN_VAR_NAME = "xmskgen")

Groups

Data input/output