15.7 Parameters (alphabetical list sorted by type)¶

15.7.1 Double parameters¶

dparam

The enumeration type containing all double parameters.

dparam.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

task.putdouparam(dparam.ana_sol_infeas_tol, 1e-6)

Generic name

MSK_DPAR_ANA_SOL_INFEAS_TOL

Groups

Analysis

dparam.basis_rel_tol_s

Maximum relative dual bound violation allowed in an optimal basic solution.

Default

1.0e-12

Accepted

[0.0; +inf]

Example

task.putdouparam(dparam.basis_rel_tol_s, 1.0e-12)

Generic name

MSK_DPAR_BASIS_REL_TOL_S

Groups
dparam.basis_tol_s

Maximum absolute dual bound violation in an optimal basic solution.

Default

1.0e-6

Accepted

[1.0e-9; +inf]

Example

task.putdouparam(dparam.basis_tol_s, 1.0e-6)

Generic name

MSK_DPAR_BASIS_TOL_S

Groups
dparam.basis_tol_x

Maximum absolute primal bound violation allowed in an optimal basic solution.

Default

1.0e-6

Accepted

[1.0e-9; +inf]

Example

task.putdouparam(dparam.basis_tol_x, 1.0e-6)

Generic name

MSK_DPAR_BASIS_TOL_X

Groups
dparam.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

task.putdouparam(dparam.check_convexity_rel_tol, 1e-10)

Generic name

MSK_DPAR_CHECK_CONVEXITY_REL_TOL

Groups

Interior-point method

dparam.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

task.putdouparam(dparam.data_sym_mat_tol, 1.0e-12)

Generic name

MSK_DPAR_DATA_SYM_MAT_TOL

Groups

Data check

dparam.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

task.putdouparam(dparam.data_sym_mat_tol_huge, 1.0e20)

Generic name

MSK_DPAR_DATA_SYM_MAT_TOL_HUGE

Groups

Data check

dparam.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

task.putdouparam(dparam.data_sym_mat_tol_large, 1.0e10)

Generic name

MSK_DPAR_DATA_SYM_MAT_TOL_LARGE

Groups

Data check

dparam.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

task.putdouparam(dparam.data_tol_aij_huge, 1.0e20)

Generic name

MSK_DPAR_DATA_TOL_AIJ_HUGE

Groups

Data check

dparam.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

task.putdouparam(dparam.data_tol_aij_large, 1.0e10)

Generic name

MSK_DPAR_DATA_TOL_AIJ_LARGE

Groups

Data check

dparam.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

task.putdouparam(dparam.data_tol_bound_inf, 1.0e16)

Generic name

MSK_DPAR_DATA_TOL_BOUND_INF

Groups

Data check

dparam.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

task.putdouparam(dparam.data_tol_bound_wrn, 1.0e8)

Generic name

MSK_DPAR_DATA_TOL_BOUND_WRN

Groups

Data check

dparam.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

task.putdouparam(dparam.data_tol_c_huge, 1.0e16)

Generic name

MSK_DPAR_DATA_TOL_C_HUGE

Groups

Data check

dparam.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

task.putdouparam(dparam.data_tol_cj_large, 1.0e8)

Generic name

MSK_DPAR_DATA_TOL_CJ_LARGE

Groups

Data check

dparam.data_tol_qij

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

Default

1.0e-16

Accepted

[0.0; +inf]

Example

task.putdouparam(dparam.data_tol_qij, 1.0e-16)

Generic name

MSK_DPAR_DATA_TOL_QIJ

Groups

Data check

dparam.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

task.putdouparam(dparam.data_tol_x, 1.0e-8)

Generic name

MSK_DPAR_DATA_TOL_X

Groups

Data check

dparam.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

task.putdouparam(dparam.intpnt_co_tol_dfeas, 1.0e-8)

See also

dparam.intpnt_co_tol_near_rel

Generic name

MSK_DPAR_INTPNT_CO_TOL_DFEAS

Groups
dparam.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

task.putdouparam(dparam.intpnt_co_tol_infeas, 1.0e-12)

Generic name

MSK_DPAR_INTPNT_CO_TOL_INFEAS

Groups
dparam.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

task.putdouparam(dparam.intpnt_co_tol_mu_red, 1.0e-8)

Generic name

MSK_DPAR_INTPNT_CO_TOL_MU_RED

Groups
dparam.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

task.putdouparam(dparam.intpnt_co_tol_near_rel, 1000)

Generic name

MSK_DPAR_INTPNT_CO_TOL_NEAR_REL

Groups
dparam.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

task.putdouparam(dparam.intpnt_co_tol_pfeas, 1.0e-8)

See also

dparam.intpnt_co_tol_near_rel

Generic name

MSK_DPAR_INTPNT_CO_TOL_PFEAS

Groups
dparam.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

task.putdouparam(dparam.intpnt_co_tol_rel_gap, 1.0e-8)

See also

dparam.intpnt_co_tol_near_rel

Generic name

MSK_DPAR_INTPNT_CO_TOL_REL_GAP

Groups
dparam.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

task.putdouparam(dparam.intpnt_qo_tol_dfeas, 1.0e-8)

See also

dparam.intpnt_qo_tol_near_rel

Generic name

MSK_DPAR_INTPNT_QO_TOL_DFEAS

Groups
dparam.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

task.putdouparam(dparam.intpnt_qo_tol_infeas, 1.0e-12)

Generic name

MSK_DPAR_INTPNT_QO_TOL_INFEAS

Groups
dparam.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

task.putdouparam(dparam.intpnt_qo_tol_mu_red, 1.0e-8)

Generic name

MSK_DPAR_INTPNT_QO_TOL_MU_RED

Groups
dparam.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

task.putdouparam(dparam.intpnt_qo_tol_near_rel, 1000)

Generic name

MSK_DPAR_INTPNT_QO_TOL_NEAR_REL

Groups
dparam.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

task.putdouparam(dparam.intpnt_qo_tol_pfeas, 1.0e-8)

See also

dparam.intpnt_qo_tol_near_rel

Generic name

MSK_DPAR_INTPNT_QO_TOL_PFEAS

Groups
dparam.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

task.putdouparam(dparam.intpnt_qo_tol_rel_gap, 1.0e-8)

See also

dparam.intpnt_qo_tol_near_rel

Generic name

MSK_DPAR_INTPNT_QO_TOL_REL_GAP

Groups
dparam.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

task.putdouparam(dparam.intpnt_tol_dfeas, 1.0e-8)

Generic name

MSK_DPAR_INTPNT_TOL_DFEAS

Groups
dparam.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

task.putdouparam(dparam.intpnt_tol_dsafe, 1.0)

Generic name

MSK_DPAR_INTPNT_TOL_DSAFE

Groups

Interior-point method

dparam.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

task.putdouparam(dparam.intpnt_tol_infeas, 1.0e-10)

Generic name

MSK_DPAR_INTPNT_TOL_INFEAS

Groups
dparam.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

task.putdouparam(dparam.intpnt_tol_mu_red, 1.0e-16)

Generic name

MSK_DPAR_INTPNT_TOL_MU_RED

Groups
dparam.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

task.putdouparam(dparam.intpnt_tol_path, 1.0e-8)

Generic name

MSK_DPAR_INTPNT_TOL_PATH

Groups

Interior-point method

dparam.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

task.putdouparam(dparam.intpnt_tol_pfeas, 1.0e-8)

Generic name

MSK_DPAR_INTPNT_TOL_PFEAS

Groups
dparam.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

task.putdouparam(dparam.intpnt_tol_psafe, 1.0)

Generic name

MSK_DPAR_INTPNT_TOL_PSAFE

Groups

Interior-point method

dparam.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

task.putdouparam(dparam.intpnt_tol_rel_gap, 1.0e-8)

Generic name

MSK_DPAR_INTPNT_TOL_REL_GAP

Groups
dparam.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

task.putdouparam(dparam.intpnt_tol_rel_step, 0.9999)

Generic name

MSK_DPAR_INTPNT_TOL_REL_STEP

Groups

Interior-point method

dparam.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

task.putdouparam(dparam.intpnt_tol_step_size, 1.0e-6)

Generic name

MSK_DPAR_INTPNT_TOL_STEP_SIZE

Groups

Interior-point method

dparam.lower_obj_cut

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

Default

-1.0e30

Accepted

[-inf; +inf]

Example

task.putdouparam(dparam.lower_obj_cut, -1.0e30)

See also

dparam.lower_obj_cut_finite_trh

Generic name

MSK_DPAR_LOWER_OBJ_CUT

Groups

Termination criteria

dparam.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. dparam.lower_obj_cut is treated as $$-\infty$$.

Default

-0.5e30

Accepted

[-inf; +inf]

Example

task.putdouparam(dparam.lower_obj_cut_finite_trh, -0.5e30)

Generic name

MSK_DPAR_LOWER_OBJ_CUT_FINITE_TRH

Groups

Termination criteria

dparam.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

task.putdouparam(dparam.mio_djc_max_bigm, 1.0e6)

Generic name

MSK_DPAR_MIO_DJC_MAX_BIGM

Groups

Mixed-integer optimization

dparam.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

task.putdouparam(dparam.mio_max_time, -1.0)

Generic name

MSK_DPAR_MIO_MAX_TIME

Groups
dparam.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

task.putdouparam(dparam.mio_rel_gap_const, 1.0e-10)

Generic name

MSK_DPAR_MIO_REL_GAP_CONST

Groups
dparam.mio_tol_abs_gap

Absolute optimality tolerance employed by the mixed-integer optimizer.

Default

0.0

Accepted

[0.0; +inf]

Example

task.putdouparam(dparam.mio_tol_abs_gap, 0.0)

Generic name

MSK_DPAR_MIO_TOL_ABS_GAP

Groups

Mixed-integer optimization

dparam.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

task.putdouparam(dparam.mio_tol_abs_relax_int, 1.0e-5)

Generic name

MSK_DPAR_MIO_TOL_ABS_RELAX_INT

Groups

Mixed-integer optimization

dparam.mio_tol_feas

Feasibility tolerance for mixed integer solver.

Default

1.0e-6

Accepted

[1e-9; 1e-3]

Example

task.putdouparam(dparam.mio_tol_feas, 1.0e-6)

Generic name

MSK_DPAR_MIO_TOL_FEAS

Groups

Mixed-integer optimization

dparam.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

task.putdouparam(dparam.mio_tol_rel_dual_bound_improvement, 0.0)

Generic name

MSK_DPAR_MIO_TOL_REL_DUAL_BOUND_IMPROVEMENT

Groups

Mixed-integer optimization

dparam.mio_tol_rel_gap

Relative optimality tolerance employed by the mixed-integer optimizer.

Default

1.0e-4

Accepted

[0.0; +inf]

Example

task.putdouparam(dparam.mio_tol_rel_gap, 1.0e-4)

Generic name

MSK_DPAR_MIO_TOL_REL_GAP

Groups
dparam.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

task.putdouparam(dparam.optimizer_max_time, -1.0)

Generic name

MSK_DPAR_OPTIMIZER_MAX_TIME

Groups

Termination criteria

dparam.presolve_tol_abs_lindep

Absolute tolerance employed by the linear dependency checker.

Default

1.0e-6

Accepted

[0.0; +inf]

Example

task.putdouparam(dparam.presolve_tol_abs_lindep, 1.0e-6)

Generic name

MSK_DPAR_PRESOLVE_TOL_ABS_LINDEP

Groups

Presolve

dparam.presolve_tol_aij

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

Default

1.0e-12

Accepted

[1.0e-15; +inf]

Example

task.putdouparam(dparam.presolve_tol_aij, 1.0e-12)

Generic name

MSK_DPAR_PRESOLVE_TOL_AIJ

Groups

Presolve

dparam.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

task.putdouparam(dparam.presolve_tol_primal_infeas_perturbation, 1.0e-6)

Generic name

MSK_DPAR_PRESOLVE_TOL_PRIMAL_INFEAS_PERTURBATION

Groups

Presolve

dparam.presolve_tol_rel_lindep

Relative tolerance employed by the linear dependency checker.

Default

1.0e-10

Accepted

[0.0; +inf]

Example

task.putdouparam(dparam.presolve_tol_rel_lindep, 1.0e-10)

Generic name

MSK_DPAR_PRESOLVE_TOL_REL_LINDEP

Groups

Presolve

dparam.presolve_tol_s

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

Default

1.0e-8

Accepted

[0.0; +inf]

Example

task.putdouparam(dparam.presolve_tol_s, 1.0e-8)

Generic name

MSK_DPAR_PRESOLVE_TOL_S

Groups

Presolve

dparam.presolve_tol_x

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

Default

1.0e-8

Accepted

[0.0; +inf]

Example

task.putdouparam(dparam.presolve_tol_x, 1.0e-8)

Generic name

MSK_DPAR_PRESOLVE_TOL_X

Groups

Presolve

dparam.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

task.putdouparam(dparam.qcqo_reformulate_rel_drop_tol, 1e-15)

Generic name

MSK_DPAR_QCQO_REFORMULATE_REL_DROP_TOL

Groups

Interior-point method

dparam.semidefinite_tol_approx

Tolerance to define a matrix to be positive semidefinite.

Default

1.0e-10

Accepted

[1.0e-15; +inf]

Example

task.putdouparam(dparam.semidefinite_tol_approx, 1.0e-10)

Generic name

MSK_DPAR_SEMIDEFINITE_TOL_APPROX

Groups

Data check

dparam.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

task.putdouparam(dparam.sim_lu_tol_rel_piv, 0.01)

Generic name

MSK_DPAR_SIM_LU_TOL_REL_PIV

Groups
dparam.simplex_abs_tol_piv

Absolute pivot tolerance employed by the simplex optimizers.

Default

1.0e-7

Accepted

[1.0e-12; +inf]

Example

task.putdouparam(dparam.simplex_abs_tol_piv, 1.0e-7)

Generic name

MSK_DPAR_SIMPLEX_ABS_TOL_PIV

Groups

Simplex optimizer

dparam.upper_obj_cut

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

Default

1.0e30

Accepted

[-inf; +inf]

Example

task.putdouparam(dparam.upper_obj_cut, 1.0e30)

See also

dparam.upper_obj_cut_finite_trh

Generic name

MSK_DPAR_UPPER_OBJ_CUT

Groups

Termination criteria

dparam.upper_obj_cut_finite_trh

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

Default

0.5e30

Accepted

[-inf; +inf]

Example

task.putdouparam(dparam.upper_obj_cut_finite_trh, 0.5e30)

Generic name

MSK_DPAR_UPPER_OBJ_CUT_FINITE_TRH

Groups

Termination criteria

15.7.2 Integer parameters¶

iparam

The enumeration type containing all integer parameters.

iparam.ana_sol_basis

Controls whether the basis matrix is analyzed in solution analyzer.

Default

on

Accepted
Example

task.putintparam(iparam.ana_sol_basis, onoffkey.on)

Generic name

MSK_IPAR_ANA_SOL_BASIS

Groups

Analysis

iparam.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 dparam.ana_sol_infeas_tol will be printed.

Default

off

Accepted
Example

task.putintparam(iparam.ana_sol_print_violated, onoffkey.off)

Generic name

MSK_IPAR_ANA_SOL_PRINT_VIOLATED

Groups

Analysis

iparam.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

task.putintparam(iparam.auto_sort_a_before_opt, onoffkey.off)

Generic name

MSK_IPAR_AUTO_SORT_A_BEFORE_OPT

Groups

Debugging

iparam.auto_update_sol_info

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

Default

off

Accepted
Example

task.putintparam(iparam.auto_update_sol_info, onoffkey.off)

Generic name

MSK_IPAR_AUTO_UPDATE_SOL_INFO

Groups

Overall system

iparam.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 onoffkey.on, -1 is replaced by 1.

This has significance for the results returned by the Task.solvewithbasis function.

Default

off

Accepted
Example

task.putintparam(iparam.basis_solve_use_plus_one, onoffkey.off)

Generic name

MSK_IPAR_BASIS_SOLVE_USE_PLUS_ONE

Groups

Simplex optimizer

iparam.bi_clean_optimizer

Controls which simplex optimizer is used in the clean-up phase. Anything else than optimizertype.primal_simplex or optimizertype.dual_simplex is equivalent to optimizertype.free_simplex.

Default

free

Accepted
Example

task.putintparam(iparam.bi_clean_optimizer, optimizertype.free)

Generic name

MSK_IPAR_BI_CLEAN_OPTIMIZER

Groups
iparam.bi_ignore_max_iter

If the parameter iparam.intpnt_basis has the value basindtype.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 onoffkey.on.

Default

off

Accepted
Example

task.putintparam(iparam.bi_ignore_max_iter, onoffkey.off)

Generic name

MSK_IPAR_BI_IGNORE_MAX_ITER

Groups
iparam.bi_ignore_num_error

If the parameter iparam.intpnt_basis has the value basindtype.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 onoffkey.on.

Default

off

Accepted
Example

task.putintparam(iparam.bi_ignore_num_error, onoffkey.off)

Generic name

MSK_IPAR_BI_IGNORE_NUM_ERROR

Groups
iparam.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

task.putintparam(iparam.bi_max_iterations, 1000000)

Generic name

MSK_IPAR_BI_MAX_ITERATIONS

Groups

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

By default the license is checked out for the lifetime of the MOSEK environment by the first call to Task.optimize.

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

Default

on

Accepted
Example

task.putintparam(iparam.cache_license, onoffkey.on)

Generic name

Groups

iparam.check_convexity

Specify the level of convexity check on quadratic problems.

Default

full

Accepted
Example

task.putintparam(iparam.check_convexity, checkconvexitytype.full)

Generic name

MSK_IPAR_CHECK_CONVEXITY

Groups

Data check

iparam.compress_statfile

Control compression of stat files.

Default

on

Accepted
Example

task.putintparam(iparam.compress_statfile, onoffkey.on)

Generic name

MSK_IPAR_COMPRESS_STATFILE

iparam.infeas_generic_names

Controls whether generic names are used when an infeasible subproblem is created.

Default

off

Accepted
Example

task.putintparam(iparam.infeas_generic_names, onoffkey.off)

Generic name

MSK_IPAR_INFEAS_GENERIC_NAMES

Groups

Infeasibility report

iparam.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

task.putintparam(iparam.infeas_prefer_primal, onoffkey.on)

Generic name

MSK_IPAR_INFEAS_PREFER_PRIMAL

Groups

Overall solver

iparam.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

task.putintparam(iparam.infeas_report_auto, onoffkey.off)

Generic name

MSK_IPAR_INFEAS_REPORT_AUTO

Groups
iparam.infeas_report_level

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

Default

1

Accepted

[0; +inf]

Example

task.putintparam(iparam.infeas_report_level, 1)

Generic name

MSK_IPAR_INFEAS_REPORT_LEVEL

Groups
iparam.intpnt_basis

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

Default

always

Accepted
Example

task.putintparam(iparam.intpnt_basis, basindtype.always)

See also
Generic name

MSK_IPAR_INTPNT_BASIS

Groups
iparam.intpnt_diff_step

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

Default

on

Accepted

Example

task.putintparam(iparam.intpnt_diff_step, onoffkey.on)

Generic name

MSK_IPAR_INTPNT_DIFF_STEP

Groups

Interior-point method

iparam.intpnt_hotstart

Currently not in use.

Default

none

Accepted
Example

task.putintparam(iparam.intpnt_hotstart, intpnthotstart.none)

Generic name

MSK_IPAR_INTPNT_HOTSTART

Groups

Interior-point method

iparam.intpnt_max_iterations

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

Default

400

Accepted

[0; +inf]

Example

task.putintparam(iparam.intpnt_max_iterations, 400)

Generic name

MSK_IPAR_INTPNT_MAX_ITERATIONS

Groups
iparam.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

task.putintparam(iparam.intpnt_max_num_cor, -1)

Generic name

MSK_IPAR_INTPNT_MAX_NUM_COR

Groups

Interior-point method

iparam.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

task.putintparam(iparam.intpnt_max_num_refinement_steps, -1)

Generic name

MSK_IPAR_INTPNT_MAX_NUM_REFINEMENT_STEPS

Groups

Interior-point method

iparam.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

task.putintparam(iparam.intpnt_off_col_trh, 40)

Generic name

MSK_IPAR_INTPNT_OFF_COL_TRH

Groups

Interior-point method

iparam.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

task.putintparam(iparam.intpnt_order_gp_num_seeds, 0)

Generic name

MSK_IPAR_INTPNT_ORDER_GP_NUM_SEEDS

Groups

Interior-point method

iparam.intpnt_order_method

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

Default

free

Accepted
Example

task.putintparam(iparam.intpnt_order_method, orderingtype.free)

Generic name

MSK_IPAR_INTPNT_ORDER_METHOD

Groups

Interior-point method

iparam.intpnt_purify

Currently not in use.

Default

none

Accepted
Example

task.putintparam(iparam.intpnt_purify, purify.none)

Generic name

MSK_IPAR_INTPNT_PURIFY

Groups

Interior-point method

iparam.intpnt_regularization_use

Controls whether regularization is allowed.

Default

on

Accepted
Example

task.putintparam(iparam.intpnt_regularization_use, onoffkey.on)

Generic name

MSK_IPAR_INTPNT_REGULARIZATION_USE

Groups

Interior-point method

iparam.intpnt_scaling

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

Default

free

Accepted
Example

task.putintparam(iparam.intpnt_scaling, scalingtype.free)

Generic name

MSK_IPAR_INTPNT_SCALING

Groups

Interior-point method

iparam.intpnt_solve_form

Controls whether the primal or the dual problem is solved.

Default

free

Accepted
Example

task.putintparam(iparam.intpnt_solve_form, solveform.free)

Generic name

MSK_IPAR_INTPNT_SOLVE_FORM

Groups

Interior-point method

iparam.intpnt_starting_point

Starting point used by the interior-point optimizer.

Default

free

Accepted
Example

task.putintparam(iparam.intpnt_starting_point, startpointtype.free)

Generic name

MSK_IPAR_INTPNT_STARTING_POINT

Groups

Interior-point method

This option is used to turn on debugging of the license manager.

Default

off

Accepted
Example

task.putintparam(iparam.license_debug, onoffkey.off)

Generic name

Groups

If iparam.license_wait is onoffkey.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

task.putintparam(iparam.license_pause_time, 100)

Generic name

Groups

Controls whether license features expire warnings are suppressed.

Default

off

Accepted
Example

task.putintparam(iparam.license_suppress_expire_wrns, onoffkey.off)

Generic name

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

task.putintparam(iparam.license_trh_expiry_wrn, 7)

Generic name

Groups

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

task.putintparam(iparam.license_wait, onoffkey.off)

Generic name

Groups
iparam.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 iparam.log_cut_second_opt for the second and any subsequent optimizations.

Default

10

Accepted

[0; +inf]

Example

task.putintparam(iparam.log, 10)

See also

iparam.log_cut_second_opt

Generic name

MSK_IPAR_LOG

Groups
iparam.log_ana_pro

Controls amount of output from the problem analyzer.

Default

1

Accepted

[0; +inf]

Example

task.putintparam(iparam.log_ana_pro, 1)

Generic name

MSK_IPAR_LOG_ANA_PRO

Groups
iparam.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

task.putintparam(iparam.log_bi, 1)

Generic name

MSK_IPAR_LOG_BI

Groups
iparam.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

task.putintparam(iparam.log_bi_freq, 2500)

Generic name

MSK_IPAR_LOG_BI_FREQ

Groups
iparam.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

task.putintparam(iparam.log_check_convexity, 0)

Generic name

MSK_IPAR_LOG_CHECK_CONVEXITY

Groups

Data check

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

Default

1

Accepted

[0; +inf]

Example

task.putintparam(iparam.log_cut_second_opt, 1)

See also
Generic name

MSK_IPAR_LOG_CUT_SECOND_OPT

Groups
iparam.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

task.putintparam(iparam.log_expand, 0)

Generic name

MSK_IPAR_LOG_EXPAND

Groups
iparam.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

task.putintparam(iparam.log_feas_repair, 1)

Generic name

MSK_IPAR_LOG_FEAS_REPAIR

Groups
iparam.log_file

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

Default

1

Accepted

[0; +inf]

Example

task.putintparam(iparam.log_file, 1)

Generic name

MSK_IPAR_LOG_FILE

Groups
iparam.log_include_summary

If on, then the solution summary will be printed by Task.optimize, so a separate call to Task.solutionsummary is not necessary.

Default

off

Accepted
Example

task.putintparam(iparam.log_include_summary, onoffkey.off)

Generic name

MSK_IPAR_LOG_INCLUDE_SUMMARY

Groups
iparam.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

task.putintparam(iparam.log_infeas_ana, 1)

Generic name

MSK_IPAR_LOG_INFEAS_ANA

Groups
iparam.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

task.putintparam(iparam.log_intpnt, 1)

Generic name

MSK_IPAR_LOG_INTPNT

Groups
iparam.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

task.putintparam(iparam.log_local_info, onoffkey.on)

Generic name

MSK_IPAR_LOG_LOCAL_INFO

Groups
iparam.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

task.putintparam(iparam.log_mio, 4)

Generic name

MSK_IPAR_LOG_MIO

Groups
iparam.log_mio_freq

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

Default

10

Accepted

[-inf; +inf]

Example

task.putintparam(iparam.log_mio_freq, 10)

Generic name

MSK_IPAR_LOG_MIO_FREQ

Groups
iparam.log_order

If turned on, then factor lines are added to the log.

Default

1

Accepted

[0; +inf]

Example

task.putintparam(iparam.log_order, 1)

Generic name

MSK_IPAR_LOG_ORDER

Groups
iparam.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

task.putintparam(iparam.log_presolve, 1)

Generic name

MSK_IPAR_LOG_PRESOLVE

Groups

Logging

iparam.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

task.putintparam(iparam.log_response, 0)

Generic name

MSK_IPAR_LOG_RESPONSE

Groups
iparam.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

task.putintparam(iparam.log_sensitivity, 1)

Generic name

MSK_IPAR_LOG_SENSITIVITY

Groups
iparam.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

task.putintparam(iparam.log_sensitivity_opt, 0)

Generic name

MSK_IPAR_LOG_SENSITIVITY_OPT

Groups
iparam.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

task.putintparam(iparam.log_sim, 4)

Generic name

MSK_IPAR_LOG_SIM

Groups
iparam.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

task.putintparam(iparam.log_sim_freq, 1000)

Generic name

MSK_IPAR_LOG_SIM_FREQ

Groups
iparam.log_sim_minor

Currently not in use.

Default

1

Accepted

[0; +inf]

Example

task.putintparam(iparam.log_sim_minor, 1)

Generic name

MSK_IPAR_LOG_SIM_MINOR

Groups
iparam.log_storage

When turned on, MOSEK prints messages regarding the storage usage and allocation.

Default

0

Accepted

[0; +inf]

Example

task.putintparam(iparam.log_storage, 0)

Generic name

MSK_IPAR_LOG_STORAGE

Groups
iparam.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

task.putintparam(iparam.max_num_warnings, 10)

Generic name

MSK_IPAR_MAX_NUM_WARNINGS

Groups

Output information

iparam.mio_branch_dir

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

Default

free

Accepted
Example

task.putintparam(iparam.mio_branch_dir, branchdir.free)

Generic name

MSK_IPAR_MIO_BRANCH_DIR

Groups

Mixed-integer optimization

iparam.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

task.putintparam(iparam.mio_conic_outer_approximation, onoffkey.off)

Generic name

MSK_IPAR_MIO_CONIC_OUTER_APPROXIMATION

Groups

Mixed-integer optimization

iparam.mio_construct_sol

If set to onoffkey.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

task.putintparam(iparam.mio_construct_sol, onoffkey.off)

Generic name

MSK_IPAR_MIO_CONSTRUCT_SOL

Groups

Mixed-integer optimization

iparam.mio_cut_clique

Controls whether clique cuts should be generated.

Default

on

Accepted
Example

task.putintparam(iparam.mio_cut_clique, onoffkey.on)

Generic name

MSK_IPAR_MIO_CUT_CLIQUE

Groups

Mixed-integer optimization

iparam.mio_cut_cmir

Controls whether mixed integer rounding cuts should be generated.

Default

on

Accepted
Example

task.putintparam(iparam.mio_cut_cmir, onoffkey.on)

Generic name

MSK_IPAR_MIO_CUT_CMIR

Groups

Mixed-integer optimization

iparam.mio_cut_gmi

Controls whether GMI cuts should be generated.

Default

on

Accepted
Example

task.putintparam(iparam.mio_cut_gmi, onoffkey.on)

Generic name

MSK_IPAR_MIO_CUT_GMI

Groups

Mixed-integer optimization

iparam.mio_cut_implied_bound

Controls whether implied bound cuts should be generated.

Default

on

Accepted
Example

task.putintparam(iparam.mio_cut_implied_bound, onoffkey.on)

Generic name

MSK_IPAR_MIO_CUT_IMPLIED_BOUND

Groups

Mixed-integer optimization

iparam.mio_cut_knapsack_cover

Controls whether knapsack cover cuts should be generated.

Default

off

Accepted
Example

task.putintparam(iparam.mio_cut_knapsack_cover, onoffkey.off)

Generic name

MSK_IPAR_MIO_CUT_KNAPSACK_COVER

Groups

Mixed-integer optimization

iparam.mio_cut_lipro

Controls whether lift-and-project cuts should be generated.

Default

off

Accepted
Example

task.putintparam(iparam.mio_cut_lipro, onoffkey.off)

Generic name

MSK_IPAR_MIO_CUT_LIPRO

Groups

Mixed-integer optimization

iparam.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

task.putintparam(iparam.mio_cut_selection_level, -1)

Generic name

MSK_IPAR_MIO_CUT_SELECTION_LEVEL

Groups

Mixed-integer optimization

iparam.mio_data_permutation_method

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

Default

none

Accepted
Example

task.putintparam(iparam.mio_data_permutation_method, miodatapermmethod.none)

Generic name

MSK_IPAR_MIO_DATA_PERMUTATION_METHOD

Groups

Mixed-integer optimization

iparam.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

task.putintparam(iparam.mio_feaspump_level, -1)

Generic name

MSK_IPAR_MIO_FEASPUMP_LEVEL

Groups

Mixed-integer optimization

iparam.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

task.putintparam(iparam.mio_heuristic_level, -1)

Generic name

MSK_IPAR_MIO_HEURISTIC_LEVEL

Groups

Mixed-integer optimization

iparam.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

task.putintparam(iparam.mio_max_num_branches, -1)

Generic name

MSK_IPAR_MIO_MAX_NUM_BRANCHES

Groups
iparam.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

task.putintparam(iparam.mio_max_num_relaxs, -1)

Generic name

MSK_IPAR_MIO_MAX_NUM_RELAXS

Groups

Mixed-integer optimization

iparam.mio_max_num_root_cut_rounds

Maximum number of cut separation rounds at the root node.

Default

100

Accepted

[0; +inf]

Example

task.putintparam(iparam.mio_max_num_root_cut_rounds, 100)

Generic name

MSK_IPAR_MIO_MAX_NUM_ROOT_CUT_ROUNDS

Groups
iparam.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

task.putintparam(iparam.mio_max_num_solutions, -1)

Generic name

MSK_IPAR_MIO_MAX_NUM_SOLUTIONS

Groups
iparam.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

task.putintparam(iparam.mio_memory_emphasis_level, 0)

Generic name

MSK_IPAR_MIO_MEMORY_EMPHASIS_LEVEL

Groups

Mixed-integer optimization

iparam.mio_mode

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

Default

satisfied

Accepted
Example

task.putintparam(iparam.mio_mode, miomode.satisfied)

Generic name

MSK_IPAR_MIO_MODE

Groups

Overall solver

iparam.mio_node_optimizer

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

Default

free

Accepted
Example

task.putintparam(iparam.mio_node_optimizer, optimizertype.free)

Generic name

MSK_IPAR_MIO_NODE_OPTIMIZER

Groups

Mixed-integer optimization

iparam.mio_node_selection

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

Default

free

Accepted
Example

task.putintparam(iparam.mio_node_selection, mionodeseltype.free)

Generic name

MSK_IPAR_MIO_NODE_SELECTION

Groups

Mixed-integer optimization

iparam.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

task.putintparam(iparam.mio_numerical_emphasis_level, 0)

Generic name

MSK_IPAR_MIO_NUMERICAL_EMPHASIS_LEVEL

Groups

Mixed-integer optimization

iparam.mio_perspective_reformulate

Enables or disables perspective reformulation in presolve.

Default

on

Accepted
Example

task.putintparam(iparam.mio_perspective_reformulate, onoffkey.on)

Generic name

MSK_IPAR_MIO_PERSPECTIVE_REFORMULATE

Groups

Mixed-integer optimization

iparam.mio_presolve_aggregator_use

Controls if the aggregator should be used.

Default

on

Accepted
Example

task.putintparam(iparam.mio_presolve_aggregator_use, onoffkey.on)

Generic name

MSK_IPAR_MIO_PRESOLVE_AGGREGATOR_USE

Groups

Presolve

iparam.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

task.putintparam(iparam.mio_probing_level, -1)

Generic name

MSK_IPAR_MIO_PROBING_LEVEL

Groups

Mixed-integer optimization

iparam.mio_propagate_objective_constraint

Use objective domain propagation.

Default

off

Accepted
Example

task.putintparam(iparam.mio_propagate_objective_constraint, onoffkey.off)

Generic name

MSK_IPAR_MIO_PROPAGATE_OBJECTIVE_CONSTRAINT

Groups

Mixed-integer optimization

iparam.mio_qcqo_reformulation_method

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

Default

free

Accepted
Example

task.putintparam(iparam.mio_qcqo_reformulation_method, miqcqoreformmethod.free)

Generic name

MSK_IPAR_MIO_QCQO_REFORMULATION_METHOD

Groups

Mixed-integer optimization

iparam.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

task.putintparam(iparam.mio_rins_max_nodes, -1)

Generic name

MSK_IPAR_MIO_RINS_MAX_NODES

Groups

Mixed-integer optimization

iparam.mio_root_optimizer

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

Default

free

Accepted
Example

task.putintparam(iparam.mio_root_optimizer, optimizertype.free)

Generic name

MSK_IPAR_MIO_ROOT_OPTIMIZER

Groups

Mixed-integer optimization

iparam.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

task.putintparam(iparam.mio_root_repeat_presolve_level, -1)

Generic name

MSK_IPAR_MIO_ROOT_REPEAT_PRESOLVE_LEVEL

Groups

Mixed-integer optimization

iparam.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

task.putintparam(iparam.mio_seed, 42)

Generic name

MSK_IPAR_MIO_SEED

Groups

Mixed-integer optimization

iparam.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

task.putintparam(iparam.mio_symmetry_level, -1)

Generic name

MSK_IPAR_MIO_SYMMETRY_LEVEL

Groups

Mixed-integer optimization

iparam.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

task.putintparam(iparam.mio_vb_detection_level, -1)

Generic name

MSK_IPAR_MIO_VB_DETECTION_LEVEL

Groups

Mixed-integer optimization

iparam.mt_spincount

Set the number of iterations to spin before sleeping.

Default

0

Accepted

[0; 1000000000]

Example

task.putintparam(iparam.mt_spincount, 0)

Generic name

MSK_IPAR_MT_SPINCOUNT

Groups

Overall system

iparam.ng

Not in use.

Default

off

Accepted
Example

task.putintparam(iparam.ng, onoffkey.off)

Generic name

MSK_IPAR_NG

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

task.putintparam(iparam.num_threads, 0)

Generic name

Groups

Overall system

Write a text header with date and MOSEK version in an OPF file.

Default

on

Accepted
Example

task.putintparam(iparam.opf_write_header, onoffkey.on)

Generic name

Groups

Data input/output

iparam.opf_write_hints

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

Default

on

Accepted
Example

task.putintparam(iparam.opf_write_hints, onoffkey.on)

Generic name

MSK_IPAR_OPF_WRITE_HINTS

Groups

Data input/output

iparam.opf_write_line_length

Aim to keep lines in OPF files not much longer than this.

Default

80

Accepted

[0; +inf]

Example

task.putintparam(iparam.opf_write_line_length, 80)

Generic name

MSK_IPAR_OPF_WRITE_LINE_LENGTH

Groups

Data input/output

iparam.opf_write_parameters

Write a parameter section in an OPF file.

Default

off

Accepted
Example

task.putintparam(iparam.opf_write_parameters, onoffkey.off)

Generic name

MSK_IPAR_OPF_WRITE_PARAMETERS

Groups

Data input/output

iparam.opf_write_problem

Write objective, constraints, bounds etc. to an OPF file.

Default

on

Accepted
Example

task.putintparam(iparam.opf_write_problem, onoffkey.on)

Generic name

MSK_IPAR_OPF_WRITE_PROBLEM

Groups

Data input/output

iparam.opf_write_sol_bas

If iparam.opf_write_solutions is onoffkey.on and a basic solution is defined, include the basic solution in OPF files.

Default

on

Accepted
Example

task.putintparam(iparam.opf_write_sol_bas, onoffkey.on)

Generic name

MSK_IPAR_OPF_WRITE_SOL_BAS

Groups

Data input/output

iparam.opf_write_sol_itg

If iparam.opf_write_solutions is onoffkey.on and an integer solution is defined, write the integer solution in OPF files.

Default

on

Accepted
Example

task.putintparam(iparam.opf_write_sol_itg, onoffkey.on)

Generic name

MSK_IPAR_OPF_WRITE_SOL_ITG

Groups

Data input/output

iparam.opf_write_sol_itr

If iparam.opf_write_solutions is onoffkey.on and an interior solution is defined, write the interior solution in OPF files.

Default

on

Accepted
Example

task.putintparam(iparam.opf_write_sol_itr, onoffkey.on)

Generic name

MSK_IPAR_OPF_WRITE_SOL_ITR

Groups

Data input/output

iparam.opf_write_solutions

Enable inclusion of solutions in the OPF files.

Default

off

Accepted
Example

task.putintparam(iparam.opf_write_solutions, onoffkey.off)

Generic name

MSK_IPAR_OPF_WRITE_SOLUTIONS

Groups

Data input/output

iparam.optimizer

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

Default

free

Accepted
Example

task.putintparam(iparam.optimizer, optimizertype.free)

Generic name

MSK_IPAR_OPTIMIZER

Groups

Overall solver

If turned on, then names in the parameter file are case sensitive.

Default

on

Accepted
Example

task.putintparam(iparam.param_read_case_name, onoffkey.on)

Generic name

Groups

Data input/output

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

Default

off

Accepted
Example

task.putintparam(iparam.param_read_ign_error, onoffkey.off)

Generic name

Groups

Data input/output

iparam.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

task.putintparam(iparam.presolve_eliminator_max_fill, -1)

Generic name

MSK_IPAR_PRESOLVE_ELIMINATOR_MAX_FILL

Groups

Presolve

iparam.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

task.putintparam(iparam.presolve_eliminator_max_num_tries, -1)

Generic name

MSK_IPAR_PRESOLVE_ELIMINATOR_MAX_NUM_TRIES

Groups

Presolve

iparam.presolve_level

Currently not used.

Default

-1

Accepted

[-inf; +inf]

Example

task.putintparam(iparam.presolve_level, -1)

Generic name

MSK_IPAR_PRESOLVE_LEVEL

Groups
iparam.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

task.putintparam(iparam.presolve_lindep_abs_work_trh, 100)

Generic name

MSK_IPAR_PRESOLVE_LINDEP_ABS_WORK_TRH

Groups

Presolve

iparam.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

task.putintparam(iparam.presolve_lindep_rel_work_trh, 100)

Generic name

MSK_IPAR_PRESOLVE_LINDEP_REL_WORK_TRH

Groups

Presolve

iparam.presolve_lindep_use

Controls whether the linear constraints are checked for linear dependencies.

Default

on

Accepted
Example

task.putintparam(iparam.presolve_lindep_use, onoffkey.on)

Generic name

MSK_IPAR_PRESOLVE_LINDEP_USE

Groups

Presolve

iparam.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

task.putintparam(iparam.presolve_max_num_pass, -1)

Generic name

MSK_IPAR_PRESOLVE_MAX_NUM_PASS

Groups

Presolve

iparam.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

task.putintparam(iparam.presolve_max_num_reductions, -1)

Generic name

MSK_IPAR_PRESOLVE_MAX_NUM_REDUCTIONS

Groups
iparam.presolve_use

Controls whether the presolve is applied to a problem before it is optimized.

Default

free

Accepted
Example

task.putintparam(iparam.presolve_use, presolvemode.free)

Generic name

MSK_IPAR_PRESOLVE_USE

Groups
iparam.primal_repair_optimizer

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

Default

free

Accepted
Example

task.putintparam(iparam.primal_repair_optimizer, optimizertype.free)

Generic name

MSK_IPAR_PRIMAL_REPAIR_OPTIMIZER

Groups

Overall solver

iparam.ptf_write_parameters

If iparam.ptf_write_parameters is onoffkey.on, the parameters section is written.

Default

off

Accepted
Example

task.putintparam(iparam.ptf_write_parameters, onoffkey.off)

Generic name

MSK_IPAR_PTF_WRITE_PARAMETERS

Groups

Data input/output

iparam.ptf_write_solutions

If iparam.ptf_write_solutions is onoffkey.on, the solution section is written if any solutions are available, otherwise solution section is not written even if solutions are available.

Default

off

Accepted
Example

task.putintparam(iparam.ptf_write_solutions, onoffkey.off)

Generic name

MSK_IPAR_PTF_WRITE_SOLUTIONS

Groups

Data input/output

iparam.ptf_write_transform

If iparam.ptf_write_transform is onoffkey.on, constraint blocks with identifiable conic slacks are transformed into conic constraints and the slacks are eliminated.

Default

on

Accepted
Example

task.putintparam(iparam.ptf_write_transform, onoffkey.on)

Generic name

MSK_IPAR_PTF_WRITE_TRANSFORM

Groups

Data input/output

Turns on additional debugging information when reading files.

Default

off

Accepted
Example

task.putintparam(iparam.read_debug, onoffkey.off)

Generic name

Groups

Data input/output

Controls whether the free constraints are included in the problem.

Default

off

Accepted

Example

task.putintparam(iparam.read_keep_free_con, onoffkey.off)

Generic name

Groups

Data input/output

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

Default

free

Accepted
Example

task.putintparam(iparam.read_mps_format, mpsformat.free)

Generic name

Groups

Data input/output

Controls the maximal number of characters allowed in one line of the MPS file.

Default

1024

Accepted

[80; +inf]

Example

task.putintparam(iparam.read_mps_width, 1024)

Generic name

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

task.putintparam(iparam.read_task_ignore_param, onoffkey.off)

Generic name

Groups

Data input/output

iparam.remote_use_compression

Use compression when sending data to an optimization server.

Default

zstd

Accepted
Example

task.putintparam(iparam.remote_use_compression, compresstype.zstd)

Generic name

MSK_IPAR_REMOTE_USE_COMPRESSION

iparam.remove_unused_solutions

Removes unused solutions before the optimization is performed.

Default

off

Accepted
Example

task.putintparam(iparam.remove_unused_solutions, onoffkey.off)

Generic name

MSK_IPAR_REMOVE_UNUSED_SOLUTIONS

Groups

Overall system

iparam.sensitivity_all

If set to onoffkey.on, then Task.sensitivityreport analyzes all bounds and variables instead of reading a specification from the file.

Default

off

Accepted
Example

task.putintparam(iparam.sensitivity_all, onoffkey.off)

Generic name

MSK_IPAR_SENSITIVITY_ALL

Groups

Overall solver

iparam.sensitivity_optimizer

Controls which optimizer is used for optimal partition sensitivity analysis.

Default

free_simplex

Accepted
Example

task.putintparam(iparam.sensitivity_optimizer, optimizertype.free_simplex)

Generic name

MSK_IPAR_SENSITIVITY_OPTIMIZER

Groups
iparam.sensitivity_type

Controls which type of sensitivity analysis is to be performed.

Default

basis

Accepted
Example

task.putintparam(iparam.sensitivity_type, sensitivitytype.basis)

Generic name

MSK_IPAR_SENSITIVITY_TYPE

Groups

Overall solver

iparam.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

task.putintparam(iparam.sim_basis_factor_use, onoffkey.on)

Generic name

MSK_IPAR_SIM_BASIS_FACTOR_USE

Groups

Simplex optimizer

iparam.sim_degen

Controls how aggressively degeneration is handled.

Default

free

Accepted
Example

task.putintparam(iparam.sim_degen, simdegen.free)

Generic name

MSK_IPAR_SIM_DEGEN

Groups

Simplex optimizer

iparam.sim_detect_pwl

Not in use.

Default

on

Accepted

Example

task.putintparam(iparam.sim_detect_pwl, onoffkey.on)

Generic name

MSK_IPAR_SIM_DETECT_PWL

Groups

Simplex optimizer

iparam.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

task.putintparam(iparam.sim_dual_crash, 90)

Generic name

MSK_IPAR_SIM_DUAL_CRASH

Groups

Dual simplex

iparam.sim_dual_phaseone_method

An experimental feature.

Default

0

Accepted

[0; 10]

Example

task.putintparam(iparam.sim_dual_phaseone_method, 0)

Generic name

MSK_IPAR_SIM_DUAL_PHASEONE_METHOD

Groups

Simplex optimizer

iparam.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

task.putintparam(iparam.sim_dual_restrict_selection, 50)

Generic name

MSK_IPAR_SIM_DUAL_RESTRICT_SELECTION

Groups

Dual simplex

iparam.sim_dual_selection

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

Default

free

Accepted
Example

task.putintparam(iparam.sim_dual_selection, simseltype.free)

Generic name

MSK_IPAR_SIM_DUAL_SELECTION

Groups

Dual simplex

iparam.sim_exploit_dupvec

Controls if the simplex optimizers are allowed to exploit duplicated columns.

Default

off

Accepted
Example

task.putintparam(iparam.sim_exploit_dupvec, simdupvec.off)

Generic name

MSK_IPAR_SIM_EXPLOIT_DUPVEC

Groups

Simplex optimizer

iparam.sim_hotstart

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

Default

free

Accepted
Example

task.putintparam(iparam.sim_hotstart, simhotstart.free)

Generic name

MSK_IPAR_SIM_HOTSTART

Groups

Simplex optimizer

iparam.sim_hotstart_lu

Determines if the simplex optimizer should exploit the initial factorization.

Default

on

Accepted

Example

task.putintparam(iparam.sim_hotstart_lu, onoffkey.on)

Generic name

MSK_IPAR_SIM_HOTSTART_LU

Groups

Simplex optimizer

iparam.sim_max_iterations

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

Default

10000000

Accepted

[0; +inf]

Example

task.putintparam(iparam.sim_max_iterations, 10000000)

Generic name

MSK_IPAR_SIM_MAX_ITERATIONS

Groups
iparam.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

task.putintparam(iparam.sim_max_num_setbacks, 250)

Generic name

MSK_IPAR_SIM_MAX_NUM_SETBACKS

Groups

Simplex optimizer

iparam.sim_non_singular

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

Default

on

Accepted
Example

task.putintparam(iparam.sim_non_singular, onoffkey.on)

Generic name

MSK_IPAR_SIM_NON_SINGULAR

Groups

Simplex optimizer

iparam.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

task.putintparam(iparam.sim_primal_crash, 90)

Generic name

MSK_IPAR_SIM_PRIMAL_CRASH

Groups

Primal simplex

iparam.sim_primal_phaseone_method

An experimental feature.

Default

0

Accepted

[0; 10]

Example

task.putintparam(iparam.sim_primal_phaseone_method, 0)

Generic name

MSK_IPAR_SIM_PRIMAL_PHASEONE_METHOD

Groups

Simplex optimizer

iparam.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

task.putintparam(iparam.sim_primal_restrict_selection, 50)

Generic name

MSK_IPAR_SIM_PRIMAL_RESTRICT_SELECTION

Groups

Primal simplex

iparam.sim_primal_selection

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

Default

free

Accepted
Example

task.putintparam(iparam.sim_primal_selection, simseltype.free)

Generic name

MSK_IPAR_SIM_PRIMAL_SELECTION

Groups

Primal simplex

iparam.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

task.putintparam(iparam.sim_refactor_freq, 0)

Generic name

MSK_IPAR_SIM_REFACTOR_FREQ

Groups

Simplex optimizer

iparam.sim_reformulation

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

Default

off

Accepted
Example

task.putintparam(iparam.sim_reformulation, simreform.off)

Generic name

MSK_IPAR_SIM_REFORMULATION

Groups

Simplex optimizer

iparam.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

task.putintparam(iparam.sim_save_lu, onoffkey.off)

Generic name

MSK_IPAR_SIM_SAVE_LU

Groups

Simplex optimizer

iparam.sim_scaling

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

Default

free

Accepted
Example

task.putintparam(iparam.sim_scaling, scalingtype.free)

Generic name

MSK_IPAR_SIM_SCALING

Groups

Simplex optimizer

iparam.sim_scaling_method

Controls how the problem is scaled before a simplex optimizer is used.

Default

pow2

Accepted
Example

task.putintparam(iparam.sim_scaling_method, scalingmethod.pow2)

Generic name

MSK_IPAR_SIM_SCALING_METHOD

Groups

Simplex optimizer

iparam.sim_seed

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

Default

23456

Accepted

[0; 32749]

Example

task.putintparam(iparam.sim_seed, 23456)

Generic name

MSK_IPAR_SIM_SEED

Groups

Simplex optimizer

iparam.sim_solve_form

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

Default

free

Accepted
Example

task.putintparam(iparam.sim_solve_form, solveform.free)

Generic name

MSK_IPAR_SIM_SOLVE_FORM

Groups

Simplex optimizer

iparam.sim_stability_priority

Controls how high priority the numerical stability should be given.

Default

50

Accepted

[0; 100]

Example

task.putintparam(iparam.sim_stability_priority, 50)

Generic name

MSK_IPAR_SIM_STABILITY_PRIORITY

Groups

Simplex optimizer

iparam.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

task.putintparam(iparam.sim_switch_optimizer, onoffkey.off)

Generic name

MSK_IPAR_SIM_SWITCH_OPTIMIZER

Groups

Simplex optimizer

iparam.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

task.putintparam(iparam.sol_filter_keep_basic, onoffkey.off)

Generic name

MSK_IPAR_SOL_FILTER_KEEP_BASIC

Groups

Solution input/output

iparam.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

task.putintparam(iparam.sol_filter_keep_ranged, onoffkey.off)

Generic name

MSK_IPAR_SOL_FILTER_KEEP_RANGED

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

task.putintparam(iparam.sol_read_name_width, -1)

Generic name

Groups

Controls the maximal acceptable width of line in the solutions when read by MOSEK.

Default

1024

Accepted

[80; +inf]

Example

task.putintparam(iparam.sol_read_width, 1024)

Generic name

Groups
iparam.solution_callback

Indicates whether solution callbacks will be performed during the optimization.

Default

off

Accepted
Example

task.putintparam(iparam.solution_callback, onoffkey.off)

Generic name

MSK_IPAR_SOLUTION_CALLBACK

Groups
iparam.timing_level

Controls the amount of timing performed inside MOSEK.

Default

1

Accepted

[0; +inf]

Example

task.putintparam(iparam.timing_level, 1)

Generic name

MSK_IPAR_TIMING_LEVEL

Groups

Overall system

iparam.write_bas_constraints

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

Default

on

Accepted
Example

task.putintparam(iparam.write_bas_constraints, onoffkey.on)

Generic name

MSK_IPAR_WRITE_BAS_CONSTRAINTS

Groups

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

Default

on

Accepted
Example

task.putintparam(iparam.write_bas_head, onoffkey.on)

Generic name

Groups
iparam.write_bas_variables

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

Default

on

Accepted
Example

task.putintparam(iparam.write_bas_variables, onoffkey.on)

Generic name

MSK_IPAR_WRITE_BAS_VARIABLES

Groups
iparam.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

task.putintparam(iparam.write_compression, 9)

Generic name

MSK_IPAR_WRITE_COMPRESSION

Groups

Data input/output

iparam.write_data_param

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

Default

off

Accepted
Example

task.putintparam(iparam.write_data_param, onoffkey.off)

Generic name

MSK_IPAR_WRITE_DATA_PARAM

Groups

Data input/output

iparam.write_free_con

Controls whether the free constraints are written to the data file.

Default

on

Accepted
Example

task.putintparam(iparam.write_free_con, onoffkey.on)

Generic name

MSK_IPAR_WRITE_FREE_CON

Groups

Data input/output

iparam.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

task.putintparam(iparam.write_generic_names, onoffkey.off)

Generic name

MSK_IPAR_WRITE_GENERIC_NAMES

Groups

Data input/output

iparam.write_generic_names_io

Index origin used in generic names.

Default

1

Accepted

[0; +inf]

Example

task.putintparam(iparam.write_generic_names_io, 1)

Generic name

MSK_IPAR_WRITE_GENERIC_NAMES_IO

Groups

Data input/output

iparam.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

task.putintparam(iparam.write_ignore_incompatible_items, onoffkey.off)

Generic name

MSK_IPAR_WRITE_IGNORE_INCOMPATIBLE_ITEMS

Groups

Data input/output

iparam.write_int_constraints

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

Default

on

Accepted
Example

task.putintparam(iparam.write_int_constraints, onoffkey.on)

Generic name

MSK_IPAR_WRITE_INT_CONSTRAINTS

Groups

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

Default

on

Accepted
Example

task.putintparam(iparam.write_int_head, onoffkey.on)

Generic name

Groups
iparam.write_int_variables

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

Default

on

Accepted
Example

task.putintparam(iparam.write_int_variables, onoffkey.on)

Generic name

MSK_IPAR_WRITE_INT_VARIABLES

Groups
iparam.write_json_indentation

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

Default

off

Accepted
Example

task.putintparam(iparam.write_json_indentation, onoffkey.off)

Generic name

MSK_IPAR_WRITE_JSON_INDENTATION

Groups

Data input/output

iparam.write_lp_full_obj

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

Default

on

Accepted
Example

task.putintparam(iparam.write_lp_full_obj, onoffkey.on)

Generic name

MSK_IPAR_WRITE_LP_FULL_OBJ

Groups

Data input/output

iparam.write_lp_line_width

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

Default

80

Accepted

[40; +inf]

Example

task.putintparam(iparam.write_lp_line_width, 80)

Generic name

MSK_IPAR_WRITE_LP_LINE_WIDTH

Groups

Data input/output

iparam.write_mps_format

Controls in which format the MPS is written.

Default

free

Accepted
Example

task.putintparam(iparam.write_mps_format, mpsformat.free)

Generic name

MSK_IPAR_WRITE_MPS_FORMAT

Groups

Data input/output

iparam.write_mps_int

Controls if marker records are written to the MPS file to indicate whether variables are integer restricted.

Default

on

Accepted
Example

task.putintparam(iparam.write_mps_int, onoffkey.on)

Generic name

MSK_IPAR_WRITE_MPS_INT

Groups

Data input/output

iparam.write_sol_barvariables

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

Default

on

Accepted
Example

task.putintparam(iparam.write_sol_barvariables, onoffkey.on)

Generic name

MSK_IPAR_WRITE_SOL_BARVARIABLES

Groups
iparam.write_sol_constraints

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

Default

on

Accepted
Example

task.putintparam(iparam.write_sol_constraints, onoffkey.on)

Generic name

MSK_IPAR_WRITE_SOL_CONSTRAINTS

Groups

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

Default

on

Accepted
Example

task.putintparam(iparam.write_sol_head, onoffkey.on)

Generic name

Groups
iparam.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

task.putintparam(iparam.write_sol_ignore_invalid_names, onoffkey.off)

Generic name

MSK_IPAR_WRITE_SOL_IGNORE_INVALID_NAMES

Groups
iparam.write_sol_variables

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

Default

on

Accepted
Example

task.putintparam(iparam.write_sol_variables, onoffkey.on)

Generic name

MSK_IPAR_WRITE_SOL_VARIABLES

Groups

Controls whether the solutions are stored in the task file too.

Default

on

Accepted
Example

task.putintparam(iparam.write_task_inc_sol, onoffkey.on)

Generic name

Groups

Data input/output

iparam.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

task.putintparam(iparam.write_xml_mode, xmlwriteroutputtype.row)

Generic name

MSK_IPAR_WRITE_XML_MODE

Groups

Data input/output

15.7.3 String parameters¶

sparam

The enumeration type containing all string parameters.

sparam.bas_sol_file_name

Name of the bas solution file.

Accepted

Any valid file name.

Example

task.putstrparam(sparam.bas_sol_file_name, "somevalue")

Generic name

MSK_SPAR_BAS_SOL_FILE_NAME

Groups
sparam.data_file_name

Data are read and written to this file.

Accepted

Any valid file name.

Example

task.putstrparam(sparam.data_file_name, "somevalue")

Generic name

MSK_SPAR_DATA_FILE_NAME

Groups

Data input/output

sparam.debug_file_name

MOSEK debug file.

Accepted

Any valid file name.

Example

task.putstrparam(sparam.debug_file_name, "somevalue")

Generic name

MSK_SPAR_DEBUG_FILE_NAME

Groups

Data input/output

sparam.int_sol_file_name

Name of the int solution file.

Accepted

Any valid file name.

Example

task.putstrparam(sparam.int_sol_file_name, "somevalue")

Generic name

MSK_SPAR_INT_SOL_FILE_NAME

Groups
sparam.itr_sol_file_name

Name of the itr solution file.

Accepted

Any valid file name.

Example

task.putstrparam(sparam.itr_sol_file_name, "somevalue")

Generic name

MSK_SPAR_ITR_SOL_FILE_NAME

Groups
sparam.mio_debug_string

For internal debugging purposes.

Accepted

Any valid string.

Example

task.putstrparam(sparam.mio_debug_string, "somevalue")

Generic name

MSK_SPAR_MIO_DEBUG_STRING

Groups

Data input/output

sparam.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

task.putstrparam(sparam.param_comment_sign, "%%")

Generic name

MSK_SPAR_PARAM_COMMENT_SIGN

Groups

Data input/output

Modifications to the parameter database is read from this file.

Accepted

Any valid file name.

Example

task.putstrparam(sparam.param_read_file_name, "somevalue")

Generic name

Groups

Data input/output

sparam.param_write_file_name

The parameter database is written to this file.

Accepted

Any valid file name.

Example

task.putstrparam(sparam.param_write_file_name, "somevalue")

Generic name

MSK_SPAR_PARAM_WRITE_FILE_NAME

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

task.putstrparam(sparam.read_mps_bou_name, "somevalue")

Generic name

Groups

Data input/output

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

task.putstrparam(sparam.read_mps_obj_name, "somevalue")

Generic name

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

task.putstrparam(sparam.read_mps_ran_name, "somevalue")

Generic name

Groups

Data input/output

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

Accepted

Any valid MPS name.

Example

task.putstrparam(sparam.read_mps_rhs_name, "somevalue")

Generic name

Groups

Data input/output

sparam.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

task.putstrparam(sparam.remote_optserver_host, "somevalue")

Generic name

MSK_SPAR_REMOTE_OPTSERVER_HOST

Groups

Overall system

sparam.remote_tls_cert

List of known server certificates in PEM format.

Accepted

PEM files separated by new-lines.

Example

task.putstrparam(sparam.remote_tls_cert, "somevalue")

Generic name

MSK_SPAR_REMOTE_TLS_CERT

Groups

Overall system

sparam.remote_tls_cert_path

Path to known server certificates in PEM format.

Accepted

Any valid path.

Example

task.putstrparam(sparam.remote_tls_cert_path, "somevalue")

Generic name

MSK_SPAR_REMOTE_TLS_CERT_PATH

Groups

Overall system

sparam.sensitivity_file_name

If defined Task.sensitivityreport reads this file as a sensitivity analysis data file specifying the type of analysis to be done.

Accepted

Any valid string.

Example

task.putstrparam(sparam.sensitivity_file_name, "somevalue")

Generic name

MSK_SPAR_SENSITIVITY_FILE_NAME

Groups

Data input/output

sparam.sensitivity_res_file_name

If this is a nonempty string, then Task.sensitivityreport writes results to this file.

Accepted

Any valid string.

Example

task.putstrparam(sparam.sensitivity_res_file_name, "somevalue")

Generic name

MSK_SPAR_SENSITIVITY_RES_FILE_NAME

Groups

Data input/output

sparam.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

task.putstrparam(sparam.sol_filter_xc_low, "somevalue")

Generic name

MSK_SPAR_SOL_FILTER_XC_LOW

Groups
sparam.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

task.putstrparam(sparam.sol_filter_xc_upr, "somevalue")

Generic name

MSK_SPAR_SOL_FILTER_XC_UPR

Groups
sparam.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

task.putstrparam(sparam.sol_filter_xx_low, "somevalue")

Generic name

MSK_SPAR_SOL_FILTER_XX_LOW

Groups
sparam.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

task.putstrparam(sparam.sol_filter_xx_upr, "somevalue")

Generic name

MSK_SPAR_SOL_FILTER_XX_UPR

Groups
sparam.stat_key

Key used when writing the summary file.

Accepted

Any valid string.

Example

task.putstrparam(sparam.stat_key, "somevalue")

Generic name

MSK_SPAR_STAT_KEY

Groups

Data input/output

sparam.stat_name

Name used when writing the statistics file.

Accepted

Any valid XML string.

Example

task.putstrparam(sparam.stat_name, "somevalue")

Generic name

MSK_SPAR_STAT_NAME

Groups

Data input/output

sparam.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

task.putstrparam(sparam.write_lp_gen_var_name, "xmskgen")

Generic name

MSK_SPAR_WRITE_LP_GEN_VAR_NAME

Groups

Data input/output