# 14.4 Parameters (alphabetical list sorted by type)¶

## 14.4.1 Double parameters¶

- "basisRelTolS"¶
Maximum relative dual bound violation allowed in an optimal basic solution.

- Default:
1.0e-12

- Accepted:
[0.0; +inf]

- Example:
`M.setSolverParam("basisRelTolS", 1.0e-12)`

- Generic name:
MSK_DPAR_BASIS_REL_TOL_S

- Groups:

- "basisTolS"¶
Maximum absolute dual bound violation in an optimal basic solution.

- Default:
1.0e-6

- Accepted:
[1.0e-9; +inf]

- Example:
`M.setSolverParam("basisTolS", 1.0e-6)`

- Generic name:
MSK_DPAR_BASIS_TOL_S

- Groups:

- "basisTolX"¶
Maximum absolute primal bound violation allowed in an optimal basic solution.

- Default:
1.0e-6

- Accepted:
[1.0e-9; +inf]

- Example:
`M.setSolverParam("basisTolX", 1.0e-6)`

- Generic name:
MSK_DPAR_BASIS_TOL_X

- Groups:

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

- Default:
1.0e-8

- Accepted:
[0.0; 1.0]

- Example:
`M.setSolverParam("intpntCoTolDfeas", 1.0e-8)`

- See also:
- Generic name:
MSK_DPAR_INTPNT_CO_TOL_DFEAS

- Groups:
Interior-point method, Termination criteria, Conic interior-point method

- "intpntCoTolInfeas"¶
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:
`M.setSolverParam("intpntCoTolInfeas", 1.0e-12)`

- Generic name:
MSK_DPAR_INTPNT_CO_TOL_INFEAS

- Groups:
Interior-point method, Termination criteria, Conic interior-point method

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

- Default:
1.0e-8

- Accepted:
[0.0; 1.0]

- Example:
`M.setSolverParam("intpntCoTolMuRed", 1.0e-8)`

- Generic name:
MSK_DPAR_INTPNT_CO_TOL_MU_RED

- Groups:
Interior-point method, Termination criteria, Conic interior-point method

- "intpntCoTolNearRel"¶
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:
`M.setSolverParam("intpntCoTolNearRel", 1000)`

- Generic name:
MSK_DPAR_INTPNT_CO_TOL_NEAR_REL

- Groups:
Interior-point method, Termination criteria, Conic interior-point method

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

- Default:
1.0e-8

- Accepted:
[0.0; 1.0]

- Example:
`M.setSolverParam("intpntCoTolPfeas", 1.0e-8)`

- See also:
- Generic name:
MSK_DPAR_INTPNT_CO_TOL_PFEAS

- Groups:
Interior-point method, Termination criteria, Conic interior-point method

- "intpntCoTolRelGap"¶
Relative gap termination tolerance used by the interior-point optimizer for conic problems.

- Default:
1.0e-8

- Accepted:
[0.0; 1.0]

- Example:
`M.setSolverParam("intpntCoTolRelGap", 1.0e-8)`

- See also:
- Generic name:
MSK_DPAR_INTPNT_CO_TOL_REL_GAP

- Groups:
Interior-point method, Termination criteria, Conic interior-point method

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

- Default:
1.0e-8

- Accepted:
[0.0; 1.0]

- Example:
`M.setSolverParam("intpntTolDfeas", 1.0e-8)`

- Generic name:
MSK_DPAR_INTPNT_TOL_DFEAS

- Groups:

- "intpntTolDsafe"¶
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:
`M.setSolverParam("intpntTolDsafe", 1.0)`

- Generic name:
MSK_DPAR_INTPNT_TOL_DSAFE

- Groups:

- "intpntTolInfeas"¶
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:
`M.setSolverParam("intpntTolInfeas", 1.0e-10)`

- Generic name:
MSK_DPAR_INTPNT_TOL_INFEAS

- Groups:

- "intpntTolMuRed"¶
Relative complementarity gap tolerance used by the interior-point optimizer for linear problems.

- Default:
1.0e-16

- Accepted:
[0.0; 1.0]

- Example:
`M.setSolverParam("intpntTolMuRed", 1.0e-16)`

- Generic name:
MSK_DPAR_INTPNT_TOL_MU_RED

- Groups:

- "intpntTolPath"¶
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:
`M.setSolverParam("intpntTolPath", 1.0e-8)`

- Generic name:
MSK_DPAR_INTPNT_TOL_PATH

- Groups:

- "intpntTolPfeas"¶
Primal feasibility tolerance used by the interior-point optimizer for linear problems.

- Default:
1.0e-8

- Accepted:
[0.0; 1.0]

- Example:
`M.setSolverParam("intpntTolPfeas", 1.0e-8)`

- Generic name:
MSK_DPAR_INTPNT_TOL_PFEAS

- Groups:

- "intpntTolPsafe"¶
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:
`M.setSolverParam("intpntTolPsafe", 1.0)`

- Generic name:
MSK_DPAR_INTPNT_TOL_PSAFE

- Groups:

- "intpntTolRelGap"¶
Relative gap termination tolerance used by the interior-point optimizer for linear problems.

- Default:
1.0e-8

- Accepted:
[1.0e-14; +inf]

- Example:
`M.setSolverParam("intpntTolRelGap", 1.0e-8)`

- Generic name:
MSK_DPAR_INTPNT_TOL_REL_GAP

- Groups:

- "intpntTolRelStep"¶
Relative step size to the boundary for linear and quadratic optimization problems.

- Default:
0.9999

- Accepted:
[1.0e-4; 0.999999]

- Example:
`M.setSolverParam("intpntTolRelStep", 0.9999)`

- Generic name:
MSK_DPAR_INTPNT_TOL_REL_STEP

- Groups:

- "intpntTolStepSize"¶
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:
`M.setSolverParam("intpntTolStepSize", 1.0e-6)`

- Generic name:
MSK_DPAR_INTPNT_TOL_STEP_SIZE

- Groups:

- "lowerObjCut"¶
If either a primal or dual feasible solution is found proving that the optimal objective value is outside the interval \([\)

`lowerObjCut`

,`upperObjCut`

\(]\), then**MOSEK**is terminated.- Default:
-1.0e30

- Accepted:
[-inf; +inf]

- Example:
`M.setSolverParam("lowerObjCut", -1.0e30)`

- See also:
- Generic name:
MSK_DPAR_LOWER_OBJ_CUT

- Groups:

- "lowerObjCutFiniteTrh"¶
If the lower objective cut is less than the value of this parameter value, then the lower objective cut i.e.

`lowerObjCut`

is treated as \(-\infty\).- Default:
-0.5e30

- Accepted:
[-inf; +inf]

- Example:
`M.setSolverParam("lowerObjCutFiniteTrh", -0.5e30)`

- Generic name:
MSK_DPAR_LOWER_OBJ_CUT_FINITE_TRH

- Groups:

- "mioDjcMaxBigm"¶
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:
`M.setSolverParam("mioDjcMaxBigm", 1.0e6)`

- Generic name:
MSK_DPAR_MIO_DJC_MAX_BIGM

- Groups:

- "mioMaxTime"¶
This parameter limits the maximum time spent by the mixed-integer optimizer. A negative number means infinity.

- Default:
-1.0

- Accepted:
[-inf; +inf]

- Example:
`M.setSolverParam("mioMaxTime", -1.0)`

- Generic name:
MSK_DPAR_MIO_MAX_TIME

- Groups:

- "mioRelGapConst"¶
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:
`M.setSolverParam("mioRelGapConst", 1.0e-10)`

- Generic name:
MSK_DPAR_MIO_REL_GAP_CONST

- Groups:

- "mioTolAbsGap"¶
Absolute optimality tolerance employed by the mixed-integer optimizer.

- Default:
0.0

- Accepted:
[0.0; +inf]

- Example:
`M.setSolverParam("mioTolAbsGap", 0.0)`

- Generic name:
MSK_DPAR_MIO_TOL_ABS_GAP

- Groups:

- "mioTolAbsRelaxInt"¶
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:
`M.setSolverParam("mioTolAbsRelaxInt", 1.0e-5)`

- Generic name:
MSK_DPAR_MIO_TOL_ABS_RELAX_INT

- Groups:

- "mioTolFeas"¶
Feasibility tolerance for mixed integer solver.

- Default:
1.0e-6

- Accepted:
[1e-9; 1e-3]

- Example:
`M.setSolverParam("mioTolFeas", 1.0e-6)`

- Generic name:
MSK_DPAR_MIO_TOL_FEAS

- Groups:

- "mioTolRelDualBoundImprovement"¶
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:
`M.setSolverParam("mioTolRelDualBoundImprovement", 0.0)`

- Generic name:
MSK_DPAR_MIO_TOL_REL_DUAL_BOUND_IMPROVEMENT

- Groups:

- "mioTolRelGap"¶
Relative optimality tolerance employed by the mixed-integer optimizer.

- Default:
1.0e-4

- Accepted:
[0.0; +inf]

- Example:
`M.setSolverParam("mioTolRelGap", 1.0e-4)`

- Generic name:
MSK_DPAR_MIO_TOL_REL_GAP

- Groups:

- "optimizerMaxTicks"¶
CURRENTLY NOT IN USE.

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

- Default:
-1.0

- Accepted:
[-inf; +inf]

- Example:
`M.setSolverParam("optimizerMaxTicks", -1.0)`

- Generic name:
MSK_DPAR_OPTIMIZER_MAX_TICKS

- Groups:

- "optimizerMaxTime"¶
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:
`M.setSolverParam("optimizerMaxTime", -1.0)`

- Generic name:
MSK_DPAR_OPTIMIZER_MAX_TIME

- Groups:

- "presolveTolAbsLindep"¶
Absolute tolerance employed by the linear dependency checker.

- Default:
1.0e-6

- Accepted:
[0.0; +inf]

- Example:
`M.setSolverParam("presolveTolAbsLindep", 1.0e-6)`

- Generic name:
MSK_DPAR_PRESOLVE_TOL_ABS_LINDEP

- Groups:

- "presolveTolAij"¶
Absolute zero tolerance employed for \(a_{ij}\) in the presolve.

- Default:
1.0e-12

- Accepted:
[1.0e-15; +inf]

- Example:
`M.setSolverParam("presolveTolAij", 1.0e-12)`

- Generic name:
MSK_DPAR_PRESOLVE_TOL_AIJ

- Groups:

- "presolveTolPrimalInfeasPerturbation"¶
The presolve is allowed to perturb a bound on a constraint or variable by this amount if it removes an infeasibility.

- Default:
1.0e-6

- Accepted:
[0.0; +inf]

- Example:
`M.setSolverParam("presolveTolPrimalInfeasPerturbation", 1.0e-6)`

- Generic name:
MSK_DPAR_PRESOLVE_TOL_PRIMAL_INFEAS_PERTURBATION

- Groups:

- "presolveTolRelLindep"¶
Relative tolerance employed by the linear dependency checker.

- Default:
1.0e-10

- Accepted:
[0.0; +inf]

- Example:
`M.setSolverParam("presolveTolRelLindep", 1.0e-10)`

- Generic name:
MSK_DPAR_PRESOLVE_TOL_REL_LINDEP

- Groups:

- "presolveTolS"¶
Absolute zero tolerance employed for \(s_i\) in the presolve.

- Default:
1.0e-8

- Accepted:
[0.0; +inf]

- Example:
`M.setSolverParam("presolveTolS", 1.0e-8)`

- Generic name:
MSK_DPAR_PRESOLVE_TOL_S

- Groups:

- "presolveTolX"¶
Absolute zero tolerance employed for \(x_j\) in the presolve.

- Default:
1.0e-8

- Accepted:
[0.0; +inf]

- Example:
`M.setSolverParam("presolveTolX", 1.0e-8)`

- Generic name:
MSK_DPAR_PRESOLVE_TOL_X

- Groups:

- "simLuTolRelPiv"¶
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:
`M.setSolverParam("simLuTolRelPiv", 0.01)`

- Generic name:
MSK_DPAR_SIM_LU_TOL_REL_PIV

- Groups:

- "simplexAbsTolPiv"¶
Absolute pivot tolerance employed by the simplex optimizers.

- Default:
1.0e-7

- Accepted:
[1.0e-12; +inf]

- Example:
`M.setSolverParam("simplexAbsTolPiv", 1.0e-7)`

- Generic name:
MSK_DPAR_SIMPLEX_ABS_TOL_PIV

- Groups:

- "upperObjCut"¶
If either a primal or dual feasible solution is found proving that the optimal objective value is outside the interval \([\)

`lowerObjCut`

,`upperObjCut`

\(]\), then**MOSEK**is terminated.- Default:
1.0e30

- Accepted:
[-inf; +inf]

- Example:
`M.setSolverParam("upperObjCut", 1.0e30)`

- See also:
- Generic name:
MSK_DPAR_UPPER_OBJ_CUT

- Groups:

- "upperObjCutFiniteTrh"¶
If the upper objective cut is greater than the value of this parameter, then the upper objective cut

`upperObjCut`

is treated as \(\infty\).- Default:
0.5e30

- Accepted:
[-inf; +inf]

- Example:
`M.setSolverParam("upperObjCutFiniteTrh", 0.5e30)`

- Generic name:
MSK_DPAR_UPPER_OBJ_CUT_FINITE_TRH

- Groups:

## 14.4.2 Integer parameters¶

- "autoUpdateSolInfo"¶
Controls whether the solution information items are automatically updated after an optimization is performed.

- Default:
- Accepted:
- Example:
`M.setSolverParam("autoUpdateSolInfo", "off")`

- Generic name:
MSK_IPAR_AUTO_UPDATE_SOL_INFO

- Groups:

- "biCleanOptimizer"¶
Controls which simplex optimizer is used in the clean-up phase. Anything else than

`"primalSimplex"`

or`"dualSimplex"`

is equivalent to`"freeSimplex"`

.- Default:
- Accepted:
`"free"`

,`"intpnt"`

,`"conic"`

,`"primalSimplex"`

,`"dualSimplex"`

,`"freeSimplex"`

,`"mixedInt"`

- Example:
`M.setSolverParam("biCleanOptimizer", "free")`

- Generic name:
MSK_IPAR_BI_CLEAN_OPTIMIZER

- Groups:

- "biIgnoreMaxIter"¶
If the parameter

`intpntBasis`

has the value`"noError"`

and the interior-point optimizer has terminated due to maximum number of iterations, then basis identification is performed if this parameter has the value`"on"`

.- Default:
- Accepted:
- Example:
`M.setSolverParam("biIgnoreMaxIter", "off")`

- Generic name:
MSK_IPAR_BI_IGNORE_MAX_ITER

- Groups:

- "biIgnoreNumError"¶
If the parameter

`intpntBasis`

has the value`"noError"`

and the interior-point optimizer has terminated due to a numerical problem, then basis identification is performed if this parameter has the value`"on"`

.- Default:
- Accepted:
- Example:
`M.setSolverParam("biIgnoreNumError", "off")`

- Generic name:
MSK_IPAR_BI_IGNORE_NUM_ERROR

- Groups:

- "biMaxIterations"¶
Controls the maximum number of simplex iterations allowed to optimize a basis after the basis identification.

- Default:
1000000

- Accepted:
[0; +inf]

- Example:
`M.setSolverParam("biMaxIterations", 1000000)`

- Generic name:
MSK_IPAR_BI_MAX_ITERATIONS

- Groups:

- "cacheLicense"¶
Specifies if the license is kept checked out for the lifetime of the

**MOSEK**environment/model/process (`"on"`

) or returned to the server immediately after the optimization (`"off"`

).By default the license is checked out for the lifetime of the process by the first call to

`Model.solve`

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

- Default:
- Accepted:
- Example:
`M.setSolverParam("cacheLicense", "on")`

- Generic name:
MSK_IPAR_CACHE_LICENSE

- Groups:

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

- Default:
- Accepted:
- Example:
`M.setSolverParam("infeasPreferPrimal", "on")`

- Generic name:
MSK_IPAR_INFEAS_PREFER_PRIMAL

- Groups:

- "infeasReportAuto"¶
Controls whether an infeasibility report is automatically produced after the optimization if the problem is primal or dual infeasible.

- Default:
- Accepted:
- Example:
`M.setSolverParam("infeasReportAuto", "off")`

- Generic name:
MSK_IPAR_INFEAS_REPORT_AUTO

- Groups:

- "intpntBasis"¶
Controls whether the interior-point optimizer also computes an optimal basis.

- Default:
- Accepted:
- Example:
`M.setSolverParam("intpntBasis", "always")`

- See also:
`biIgnoreMaxIter`

,`biIgnoreNumError`

,`biMaxIterations`

,`biCleanOptimizer`

- Generic name:
MSK_IPAR_INTPNT_BASIS

- Groups:

- "intpntDiffStep"¶
Controls whether different step sizes are allowed in the primal and dual space.

- Default:
- Accepted:
- Example:
`M.setSolverParam("intpntDiffStep", "on")`

- Generic name:
MSK_IPAR_INTPNT_DIFF_STEP

- Groups:

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

- Default:
400

- Accepted:
[0; +inf]

- Example:
`M.setSolverParam("intpntMaxIterations", 400)`

- Generic name:
MSK_IPAR_INTPNT_MAX_ITERATIONS

- Groups:

- "intpntMaxNumCor"¶
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:
`M.setSolverParam("intpntMaxNumCor", -1)`

- Generic name:
MSK_IPAR_INTPNT_MAX_NUM_COR

- Groups:

- "intpntOffColTrh"¶
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:
`M.setSolverParam("intpntOffColTrh", 40)`

- Generic name:
MSK_IPAR_INTPNT_OFF_COL_TRH

- Groups:

- "intpntOrderGpNumSeeds"¶
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:
`M.setSolverParam("intpntOrderGpNumSeeds", 0)`

- Generic name:
MSK_IPAR_INTPNT_ORDER_GP_NUM_SEEDS

- Groups:

- "intpntOrderMethod"¶
Controls the ordering strategy used by the interior-point optimizer when factorizing the Newton equation system.

- Default:
- Accepted:
`"free"`

,`"appminloc"`

,`"experimental"`

,`"tryGraphpar"`

,`"forceGraphpar"`

,`"none"`

- Example:
`M.setSolverParam("intpntOrderMethod", "free")`

- Generic name:
MSK_IPAR_INTPNT_ORDER_METHOD

- Groups:

- "intpntRegularizationUse"¶
Controls whether regularization is allowed.

- Default:
- Accepted:
- Example:
`M.setSolverParam("intpntRegularizationUse", "on")`

- Generic name:
MSK_IPAR_INTPNT_REGULARIZATION_USE

- Groups:

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

- Default:
- Accepted:
- Example:
`M.setSolverParam("intpntScaling", "free")`

- Generic name:
MSK_IPAR_INTPNT_SCALING

- Groups:

- "intpntSolveForm"¶
Controls whether the primal or the dual problem is solved.

- Default:
- Accepted:
- Example:
`M.setSolverParam("intpntSolveForm", "free")`

- Generic name:
MSK_IPAR_INTPNT_SOLVE_FORM

- Groups:

- "intpntStartingPoint"¶
Starting point used by the interior-point optimizer.

- Default:
- Accepted:
- Example:
`M.setSolverParam("intpntStartingPoint", "free")`

- Generic name:
MSK_IPAR_INTPNT_STARTING_POINT

- Groups:

- "licenseDebug"¶
This option is used to turn on debugging of the license manager.

- Default:
- Accepted:
- Example:
`M.setSolverParam("licenseDebug", "off")`

- Generic name:
MSK_IPAR_LICENSE_DEBUG

- Groups:

- "licensePauseTime"¶
If

`licenseWait`

is`"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:
`M.setSolverParam("licensePauseTime", 100)`

- Generic name:
MSK_IPAR_LICENSE_PAUSE_TIME

- Groups:

- "licenseSuppressExpireWrns"¶
Controls whether license features expire warnings are suppressed.

- Default:
- Accepted:
- Example:
`M.setSolverParam("licenseSuppressExpireWrns", "off")`

- Generic name:
MSK_IPAR_LICENSE_SUPPRESS_EXPIRE_WRNS

- Groups:

- "licenseTrhExpiryWrn"¶
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:
`M.setSolverParam("licenseTrhExpiryWrn", 7)`

- Generic name:
MSK_IPAR_LICENSE_TRH_EXPIRY_WRN

- Groups:

- "licenseWait"¶
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:
- Accepted:
- Example:
`M.setSolverParam("licenseWait", "off")`

- Generic name:
MSK_IPAR_LICENSE_WAIT

- Groups:

- "log"¶
Controls the amount of log information. The value 0 implies that all log information is suppressed. A higher level implies that more information is logged.

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

`logCutSecondOpt`

for the second and any subsequent optimizations.- Default:
10

- Accepted:
[0; +inf]

- Example:
`M.setSolverParam("log", 10)`

- See also:
- Generic name:
MSK_IPAR_LOG

- Groups:

- "logBi"¶
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:
`M.setSolverParam("logBi", 1)`

- Generic name:
MSK_IPAR_LOG_BI

- Groups:

- "logBiFreq"¶
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:
`M.setSolverParam("logBiFreq", 2500)`

- Generic name:
MSK_IPAR_LOG_BI_FREQ

- Groups:

- "logCutSecondOpt"¶
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

`log`

and`logSim`

are reduced by the value of this parameter for the second and any subsequent optimizations.

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

- Default:
1

- Accepted:
[0; +inf]

- Example:
`M.setSolverParam("logExpand", 1)`

- Generic name:
MSK_IPAR_LOG_EXPAND

- Groups:

- "logFile"¶
If turned on, then some log info is printed when a file is written or read.

- Default:
1

- Accepted:
[0; +inf]

- Example:
`M.setSolverParam("logFile", 1)`

- Generic name:
MSK_IPAR_LOG_FILE

- Groups:

- "logInfeasAna"¶
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:
`M.setSolverParam("logInfeasAna", 1)`

- Generic name:
MSK_IPAR_LOG_INFEAS_ANA

- Groups:

- "logIntpnt"¶
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:
`M.setSolverParam("logIntpnt", 1)`

- Generic name:
MSK_IPAR_LOG_INTPNT

- Groups:

- "logLocalInfo"¶
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:
- Accepted:
- Example:
`M.setSolverParam("logLocalInfo", "on")`

- Generic name:
MSK_IPAR_LOG_LOCAL_INFO

- Groups:

- "logMio"¶
Controls the log level for the mixed-integer optimizer. A higher level implies that more information is logged.

- Default:
4

- Accepted:
[0; +inf]

- Example:
`M.setSolverParam("logMio", 4)`

- Generic name:
MSK_IPAR_LOG_MIO

- Groups:

- "logMioFreq"¶
Controls how frequent the mixed-integer optimizer prints the log line. It will print line every time

`logMioFreq`

relaxations have been solved.- Default:
10

- Accepted:
[-inf; +inf]

- Example:
`M.setSolverParam("logMioFreq", 10)`

- Generic name:
MSK_IPAR_LOG_MIO_FREQ

- Groups:

- "logOrder"¶
If turned on, then factor lines are added to the log.

- Default:
1

- Accepted:
[0; +inf]

- Example:
`M.setSolverParam("logOrder", 1)`

- Generic name:
MSK_IPAR_LOG_ORDER

- Groups:

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

- Default:
1

- Accepted:
[0; +inf]

- Example:
`M.setSolverParam("logPresolve", 1)`

- Generic name:
MSK_IPAR_LOG_PRESOLVE

- Groups:

- "logResponse"¶
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:
`M.setSolverParam("logResponse", 0)`

- Generic name:
MSK_IPAR_LOG_RESPONSE

- Groups:

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

- Default:
4

- Accepted:
[0; +inf]

- Example:
`M.setSolverParam("logSim", 4)`

- Generic name:
MSK_IPAR_LOG_SIM

- Groups:

- "logSimFreq"¶
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:
`M.setSolverParam("logSimFreq", 1000)`

- Generic name:
MSK_IPAR_LOG_SIM_FREQ

- Groups:

- "logSimMinor"¶
Currently not in use.

- Default:
1

- Accepted:
[0; +inf]

- Example:
`M.setSolverParam("logSimMinor", 1)`

- Generic name:
MSK_IPAR_LOG_SIM_MINOR

- Groups:

- "mioBranchDir"¶
Controls whether the mixed-integer optimizer is branching up or down by default.

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

- Default:
- Accepted:
- Example:
`M.setSolverParam("mioConicOuterApproximation", "off")`

- Generic name:
MSK_IPAR_MIO_CONIC_OUTER_APPROXIMATION

- Groups:

- "mioConstructSol"¶
If set to

`"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:
- Accepted:
- Example:
`M.setSolverParam("mioConstructSol", "off")`

- Generic name:
MSK_IPAR_MIO_CONSTRUCT_SOL

- Groups:

- "mioCutClique"¶
Controls whether clique cuts should be generated.

- Default:
- Accepted:
- Example:
`M.setSolverParam("mioCutClique", "on")`

- Generic name:
MSK_IPAR_MIO_CUT_CLIQUE

- Groups:

- "mioCutCmir"¶
Controls whether mixed integer rounding cuts should be generated.

- Default:
- Accepted:
- Example:
`M.setSolverParam("mioCutCmir", "on")`

- Generic name:
MSK_IPAR_MIO_CUT_CMIR

- Groups:

- "mioCutGmi"¶
Controls whether GMI cuts should be generated.

- Default:
- Accepted:
- Example:
`M.setSolverParam("mioCutGmi", "on")`

- Generic name:
MSK_IPAR_MIO_CUT_GMI

- Groups:

- "mioCutImpliedBound"¶
Controls whether implied bound cuts should be generated.

- Default:
- Accepted:
- Example:
`M.setSolverParam("mioCutImpliedBound", "on")`

- Generic name:
MSK_IPAR_MIO_CUT_IMPLIED_BOUND

- Groups:

- "mioCutKnapsackCover"¶
Controls whether knapsack cover cuts should be generated.

- Default:
- Accepted:
- Example:
`M.setSolverParam("mioCutKnapsackCover", "on")`

- Generic name:
MSK_IPAR_MIO_CUT_KNAPSACK_COVER

- Groups:

- "mioCutLipro"¶
Controls whether lift-and-project cuts should be generated.

- Default:
- Accepted:
- Example:
`M.setSolverParam("mioCutLipro", "off")`

- Generic name:
MSK_IPAR_MIO_CUT_LIPRO

- Groups:

- "mioCutSelectionLevel"¶
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:
`M.setSolverParam("mioCutSelectionLevel", -1)`

- Generic name:
MSK_IPAR_MIO_CUT_SELECTION_LEVEL

- Groups:

- "mioDataPermutationMethod"¶
Controls what problem data permutation method is appplied to mixed-integer problems.

- Default:
- Accepted:
- Example:
`M.setSolverParam("mioDataPermutationMethod", "none")`

- Generic name:
MSK_IPAR_MIO_DATA_PERMUTATION_METHOD

- Groups:

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

\(-1\). The optimizer chooses the level of dual ray analysis employed

\(0\). Dual ray analysis is disabled

\(1\). A lower amount of dual ray analysis is employed

\(2\). A higher amount of dual ray analysis is employed

- Default:
-1

- Accepted:
[-1; 2]

- Example:
`M.setSolverParam("mioDualRayAnalysisLevel", -1)`

- Generic name:
MSK_IPAR_MIO_DUAL_RAY_ANALYSIS_LEVEL

- Groups:

- "mioFeaspumpLevel"¶
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:
`M.setSolverParam("mioFeaspumpLevel", -1)`

- Generic name:
MSK_IPAR_MIO_FEASPUMP_LEVEL

- Groups:

- "mioHeuristicLevel"¶
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:
`M.setSolverParam("mioHeuristicLevel", -1)`

- Generic name:
MSK_IPAR_MIO_HEURISTIC_LEVEL

- Groups:

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

- Default:
-1

- Accepted:
[-inf; +inf]

- Example:
`M.setSolverParam("mioMaxNumBranches", -1)`

- Generic name:
MSK_IPAR_MIO_MAX_NUM_BRANCHES

- Groups:

- "mioMaxNumRelaxs"¶
Maximum number of relaxations allowed during the branch and bound search. A negative value means infinite.

- Default:
-1

- Accepted:
[-inf; +inf]

- Example:
`M.setSolverParam("mioMaxNumRelaxs", -1)`

- Generic name:
MSK_IPAR_MIO_MAX_NUM_RELAXS

- Groups:

- "mioMaxNumRestarts"¶
Maximum number of restarts allowed during the branch and bound search.

- Default:
0

- Accepted:
[0; +inf]

- Example:
`M.setSolverParam("mioMaxNumRestarts", 0)`

- Generic name:
MSK_IPAR_MIO_MAX_NUM_RESTARTS

- Groups:

- "mioMaxNumRootCutRounds"¶
Maximum number of cut separation rounds at the root node.

- Default:
100

- Accepted:
[0; +inf]

- Example:
`M.setSolverParam("mioMaxNumRootCutRounds", 100)`

- Generic name:
MSK_IPAR_MIO_MAX_NUM_ROOT_CUT_ROUNDS

- Groups:

- "mioMaxNumSolutions"¶
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:
`M.setSolverParam("mioMaxNumSolutions", -1)`

- Generic name:
MSK_IPAR_MIO_MAX_NUM_SOLUTIONS

- Groups:

- "mioMemoryEmphasisLevel"¶
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:
`M.setSolverParam("mioMemoryEmphasisLevel", 0)`

- Generic name:
MSK_IPAR_MIO_MEMORY_EMPHASIS_LEVEL

- Groups:

- "mioMinRel"¶
Number of times a variable must have been branched on for its pseudocost to be considered reliable.

- Default:
5

- Accepted:
[0; +inf]

- Example:
`M.setSolverParam("mioMinRel", 5)`

- Generic name:
MSK_IPAR_MIO_MIN_REL

- Groups:

- "mioMode"¶
Controls whether the optimizer includes the integer restrictions and disjunctive constraints when solving a (mixed) integer optimization problem.

- Default:
- Accepted:
- Example:
`M.setSolverParam("mioMode", "satisfied")`

- Generic name:
MSK_IPAR_MIO_MODE

- Groups:

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

- Default:
- Accepted:
`"free"`

,`"intpnt"`

,`"conic"`

,`"primalSimplex"`

,`"dualSimplex"`

,`"freeSimplex"`

,`"mixedInt"`

- Example:
`M.setSolverParam("mioNodeOptimizer", "free")`

- Generic name:
MSK_IPAR_MIO_NODE_OPTIMIZER

- Groups:

- "mioNodeSelection"¶
Controls the node selection strategy employed by the mixed-integer optimizer.

- "mioNumericalEmphasisLevel"¶
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:
`M.setSolverParam("mioNumericalEmphasisLevel", 0)`

- Generic name:
MSK_IPAR_MIO_NUMERICAL_EMPHASIS_LEVEL

- Groups:

- "mioPerspectiveReformulate"¶
Enables or disables perspective reformulation in presolve.

- Default:
- Accepted:
- Example:
`M.setSolverParam("mioPerspectiveReformulate", "on")`

- Generic name:
MSK_IPAR_MIO_PERSPECTIVE_REFORMULATE

- Groups:

- "mioPresolveAggregatorUse"¶
Controls if the aggregator should be used.

- "mioProbingLevel"¶
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:
`M.setSolverParam("mioProbingLevel", -1)`

- Generic name:
MSK_IPAR_MIO_PROBING_LEVEL

- Groups:

- "mioPropagateObjectiveConstraint"¶
Use objective domain propagation.

- Default:
- Accepted:
- Example:
`M.setSolverParam("mioPropagateObjectiveConstraint", "off")`

- Generic name:
MSK_IPAR_MIO_PROPAGATE_OBJECTIVE_CONSTRAINT

- Groups:

- "mioQcqoReformulationMethod"¶
Controls what reformulation method is applied to mixed-integer quadratic problems.

- Default:
- Accepted:
`"free"`

,`"none"`

,`"linearization"`

,`"eigenValMethod"`

,`"diagSdp"`

,`"relaxSdp"`

- Example:
`M.setSolverParam("mioQcqoReformulationMethod", "free")`

- Generic name:
MSK_IPAR_MIO_QCQO_REFORMULATION_METHOD

- Groups:

- "mioRinsMaxNodes"¶
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:
`M.setSolverParam("mioRinsMaxNodes", -1)`

- Generic name:
MSK_IPAR_MIO_RINS_MAX_NODES

- Groups:

- "mioRootOptimizer"¶
Controls which optimizer is employed at the root node in the mixed-integer optimizer.

- Default:
- Accepted:
`"free"`

,`"intpnt"`

,`"conic"`

,`"primalSimplex"`

,`"dualSimplex"`

,`"freeSimplex"`

,`"mixedInt"`

- Example:
`M.setSolverParam("mioRootOptimizer", "free")`

- Generic name:
MSK_IPAR_MIO_ROOT_OPTIMIZER

- Groups:

- "mioRootRepeatPresolveLevel"¶
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:
`M.setSolverParam("mioRootRepeatPresolveLevel", -1)`

- Generic name:
MSK_IPAR_MIO_ROOT_REPEAT_PRESOLVE_LEVEL

- Groups:

- "mioSeed"¶
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:
`M.setSolverParam("mioSeed", 42)`

- Generic name:
MSK_IPAR_MIO_SEED

- Groups:

- "mioSymmetryLevel"¶
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:
`M.setSolverParam("mioSymmetryLevel", -1)`

- Generic name:
MSK_IPAR_MIO_SYMMETRY_LEVEL

- Groups:

- "mioVarSelection"¶
Controls the variable selection strategy employed by the mixed-integer optimizer.

- Default:
- Accepted:
- Example:
`M.setSolverParam("mioVarSelection", "free")`

- Generic name:
MSK_IPAR_MIO_VAR_SELECTION

- Groups:

- "mioVbDetectionLevel"¶
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:
`M.setSolverParam("mioVbDetectionLevel", -1)`

- Generic name:
MSK_IPAR_MIO_VB_DETECTION_LEVEL

- Groups:

- "mtSpincount"¶
Set the number of iterations to spin before sleeping.

- Default:
0

- Accepted:
[0; 1000000000]

- Example:
`M.setSolverParam("mtSpincount", 0)`

- Generic name:
MSK_IPAR_MT_SPINCOUNT

- Groups:

- "numThreads"¶
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:
`M.setSolverParam("numThreads", 0)`

- Generic name:
MSK_IPAR_NUM_THREADS

- Groups:

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

- Default:
- Accepted:
`"free"`

,`"intpnt"`

,`"conic"`

,`"primalSimplex"`

,`"dualSimplex"`

,`"freeSimplex"`

,`"mixedInt"`

- Example:
`M.setSolverParam("optimizer", "free")`

- Generic name:
MSK_IPAR_OPTIMIZER

- Groups:

- "presolveEliminatorMaxFill"¶
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:
`M.setSolverParam("presolveEliminatorMaxFill", -1)`

- Generic name:
MSK_IPAR_PRESOLVE_ELIMINATOR_MAX_FILL

- Groups:

- "presolveEliminatorMaxNumTries"¶
Control the maximum number of times the eliminator is tried. A negative value implies

**MOSEK**decides.- Default:
-1

- Accepted:
[-inf; +inf]

- Example:
`M.setSolverParam("presolveEliminatorMaxNumTries", -1)`

- Generic name:
MSK_IPAR_PRESOLVE_ELIMINATOR_MAX_NUM_TRIES

- Groups:

- "presolveLevel"¶
Currently not used.

- Default:
-1

- Accepted:
[-inf; +inf]

- Example:
`M.setSolverParam("presolveLevel", -1)`

- Generic name:
MSK_IPAR_PRESOLVE_LEVEL

- Groups:

- "presolveLindepAbsWorkTrh"¶
Controls linear dependency check in presolve. The linear dependency check is potentially computationally expensive.

- Default:
100

- Accepted:
[-inf; +inf]

- Example:
`M.setSolverParam("presolveLindepAbsWorkTrh", 100)`

- Generic name:
MSK_IPAR_PRESOLVE_LINDEP_ABS_WORK_TRH

- Groups:

- "presolveLindepNew"¶
Controls whether whether a new experimental linear dependency checker is employed.

- "presolveLindepRelWorkTrh"¶
Controls linear dependency check in presolve. The linear dependency check is potentially computationally expensive.

- Default:
100

- Accepted:
[-inf; +inf]

- Example:
`M.setSolverParam("presolveLindepRelWorkTrh", 100)`

- Generic name:
MSK_IPAR_PRESOLVE_LINDEP_REL_WORK_TRH

- Groups:

- "presolveLindepUse"¶
Controls whether the linear constraints are checked for linear dependencies.

- "presolveMaxNumPass"¶
Control the maximum number of times presolve passes over the problem. A negative value implies

**MOSEK**decides.- Default:
-1

- Accepted:
[-inf; +inf]

- Example:
`M.setSolverParam("presolveMaxNumPass", -1)`

- Generic name:
MSK_IPAR_PRESOLVE_MAX_NUM_PASS

- Groups:

- "presolveUse"¶
Controls whether the presolve is applied to a problem before it is optimized.

- "remoteUseCompression"¶
Use compression when sending data to an optimization server.

- "removeUnusedSolutions"¶
Removes unused solutions before the optimization is performed.

- Default:
- Accepted:
- Example:
`M.setSolverParam("removeUnusedSolutions", "off")`

- Generic name:
MSK_IPAR_REMOVE_UNUSED_SOLUTIONS

- Groups:

- "simBasisFactorUse"¶
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:
- Accepted:
- Example:
`M.setSolverParam("simBasisFactorUse", "on")`

- Generic name:
MSK_IPAR_SIM_BASIS_FACTOR_USE

- Groups:

- "simDegen"¶
Controls how aggressively degeneration is handled.

- Default:
- Accepted:
- Example:
`M.setSolverParam("simDegen", "free")`

- Generic name:
MSK_IPAR_SIM_DEGEN

- Groups:

- "simDualCrash"¶
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:
`M.setSolverParam("simDualCrash", 90)`

- Generic name:
MSK_IPAR_SIM_DUAL_CRASH

- Groups:

- "simDualPhaseoneMethod"¶
An experimental feature.

- Default:
0

- Accepted:
[0; 10]

- Example:
`M.setSolverParam("simDualPhaseoneMethod", 0)`

- Generic name:
MSK_IPAR_SIM_DUAL_PHASEONE_METHOD

- Groups:

- "simDualRestrictSelection"¶
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:
`M.setSolverParam("simDualRestrictSelection", 50)`

- Generic name:
MSK_IPAR_SIM_DUAL_RESTRICT_SELECTION

- Groups:

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

- "simExploitDupvec"¶
Controls if the simplex optimizers are allowed to exploit duplicated columns.

- Default:
- Accepted:
- Example:
`M.setSolverParam("simExploitDupvec", "off")`

- Generic name:
MSK_IPAR_SIM_EXPLOIT_DUPVEC

- Groups:

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

- Default:
- Accepted:
- Example:
`M.setSolverParam("simHotstart", "free")`

- Generic name:
MSK_IPAR_SIM_HOTSTART

- Groups:

- "simHotstartLu"¶
Determines if the simplex optimizer should exploit the initial factorization.

- Default:
- Accepted:
- Example:
`M.setSolverParam("simHotstartLu", "on")`

- Generic name:
MSK_IPAR_SIM_HOTSTART_LU

- Groups:

- "simMaxIterations"¶
Maximum number of iterations that can be used by a simplex optimizer.

- Default:
10000000

- Accepted:
[0; +inf]

- Example:
`M.setSolverParam("simMaxIterations", 10000000)`

- Generic name:
MSK_IPAR_SIM_MAX_ITERATIONS

- Groups:

- "simMaxNumSetbacks"¶
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:
`M.setSolverParam("simMaxNumSetbacks", 250)`

- Generic name:
MSK_IPAR_SIM_MAX_NUM_SETBACKS

- Groups:

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

- Default:
- Accepted:
- Example:
`M.setSolverParam("simNonSingular", "on")`

- Generic name:
MSK_IPAR_SIM_NON_SINGULAR

- Groups:

- "simPrimalCrash"¶
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:
`M.setSolverParam("simPrimalCrash", 90)`

- Generic name:
MSK_IPAR_SIM_PRIMAL_CRASH

- Groups:

- "simPrimalPhaseoneMethod"¶
An experimental feature.

- Default:
0

- Accepted:
[0; 10]

- Example:
`M.setSolverParam("simPrimalPhaseoneMethod", 0)`

- Generic name:
MSK_IPAR_SIM_PRIMAL_PHASEONE_METHOD

- Groups:

- "simPrimalRestrictSelection"¶
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:
`M.setSolverParam("simPrimalRestrictSelection", 50)`

- Generic name:
MSK_IPAR_SIM_PRIMAL_RESTRICT_SELECTION

- Groups:

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

- "simRefactorFreq"¶
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:
`M.setSolverParam("simRefactorFreq", 0)`

- Generic name:
MSK_IPAR_SIM_REFACTOR_FREQ

- Groups:

- "simReformulation"¶
Controls if the simplex optimizers are allowed to reformulate the problem.

- Default:
- Accepted:
- Example:
`M.setSolverParam("simReformulation", "off")`

- Generic name:
MSK_IPAR_SIM_REFORMULATION

- Groups:

- "simSaveLu"¶
Controls if the LU factorization stored should be replaced with the LU factorization corresponding to the initial basis.

- Default:
- Accepted:
- Example:
`M.setSolverParam("simSaveLu", "off")`

- Generic name:
MSK_IPAR_SIM_SAVE_LU

- Groups:

- "simScaling"¶
Controls how much effort is used in scaling the problem before a simplex optimizer is used.

- Default:
- Accepted:
- Example:
`M.setSolverParam("simScaling", "free")`

- Generic name:
MSK_IPAR_SIM_SCALING

- Groups:

- "simScalingMethod"¶
Controls how the problem is scaled before a simplex optimizer is used.

- Default:
- Accepted:
- Example:
`M.setSolverParam("simScalingMethod", "pow2")`

- Generic name:
MSK_IPAR_SIM_SCALING_METHOD

- Groups:

- "simSeed"¶
Sets the random seed used for randomization in the simplex optimizers.

- Default:
23456

- Accepted:
[0; 32749]

- Example:
`M.setSolverParam("simSeed", 23456)`

- Generic name:
MSK_IPAR_SIM_SEED

- Groups:

- "simSolveForm"¶
Controls whether the primal or the dual problem is solved by the primal-/dual-simplex optimizer.

- Default:
- Accepted:
- Example:
`M.setSolverParam("simSolveForm", "free")`

- Generic name:
MSK_IPAR_SIM_SOLVE_FORM

- Groups:

- "simSwitchOptimizer"¶
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:
- Accepted:
- Example:
`M.setSolverParam("simSwitchOptimizer", "off")`

- Generic name:
MSK_IPAR_SIM_SWITCH_OPTIMIZER

- Groups:

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

- Default:
- Accepted:
- Example:
`M.setSolverParam("writeJsonIndentation", "off")`

- Generic name:
MSK_IPAR_WRITE_JSON_INDENTATION

- Groups:

- "writeLpFullObj"¶
Write all variables, including the ones with 0-coefficients, in the objective.

- Default:
- Accepted:
- Example:
`M.setSolverParam("writeLpFullObj", "on")`

- Generic name:
MSK_IPAR_WRITE_LP_FULL_OBJ

- Groups:

- "writeLpLineWidth"¶
Maximum width of line in an LP file written by

**MOSEK**.- Default:
80

- Accepted:
[40; +inf]

- Example:
`M.setSolverParam("writeLpLineWidth", 80)`

- Generic name:
MSK_IPAR_WRITE_LP_LINE_WIDTH

- Groups:

## 14.4.3 String parameters¶

- "basSolFileName"¶
Name of the

`bas`

solution file.- Accepted:
Any valid file name.

- Example:
`M.setSolverParam("basSolFileName", "somevalue")`

- Generic name:
MSK_SPAR_BAS_SOL_FILE_NAME

- Groups:

- "dataFileName"¶
Data are read and written to this file.

- Accepted:
Any valid file name.

- Example:
`M.setSolverParam("dataFileName", "somevalue")`

- Generic name:
MSK_SPAR_DATA_FILE_NAME

- Groups:

- "intSolFileName"¶
Name of the

`int`

solution file.- Accepted:
Any valid file name.

- Example:
`M.setSolverParam("intSolFileName", "somevalue")`

- Generic name:
MSK_SPAR_INT_SOL_FILE_NAME

- Groups:

- "itrSolFileName"¶
Name of the

`itr`

solution file.- Accepted:
Any valid file name.

- Example:
`M.setSolverParam("itrSolFileName", "somevalue")`

- Generic name:
MSK_SPAR_ITR_SOL_FILE_NAME

- Groups:

- "remoteOptserverHost"¶
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:
`M.setSolverParam("remoteOptserverHost", "somevalue")`

- Generic name:
MSK_SPAR_REMOTE_OPTSERVER_HOST

- Groups:

- "remoteTlsCert"¶
List of known server certificates in PEM format.

- Accepted:
PEM files separated by new-lines.

- Example:
`M.setSolverParam("remoteTlsCert", "somevalue")`

- Generic name:
MSK_SPAR_REMOTE_TLS_CERT

- Groups:

- "remoteTlsCertPath"¶
Path to known server certificates in PEM format.

- Accepted:
Any valid path.

- Example:
`M.setSolverParam("remoteTlsCertPath", "somevalue")`

- Generic name:
MSK_SPAR_REMOTE_TLS_CERT_PATH

- Groups:

- "writeLpGenVarName"¶
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:
`M.setSolverParam("writeLpGenVarName", "xmskgen")`

- Generic name:
MSK_SPAR_WRITE_LP_GEN_VAR_NAME

- Groups: