# 15.9 Enumerations¶

basindtype

Basis identification

basindtype.never

Never do basis identification.

basindtype.always

Basis identification is always performed even if the interior-point optimizer terminates abnormally.

basindtype.no_error

Basis identification is performed if the interior-point optimizer terminates without an error.

basindtype.if_feasible

Basis identification is not performed if the interior-point optimizer terminates with a problem status saying that the problem is primal or dual infeasible.

basindtype.reservered

Not currently in use.

boundkey

Bound keys

boundkey.lo

The constraint or variable has a finite lower bound and an infinite upper bound.

boundkey.up

The constraint or variable has an infinite lower bound and an finite upper bound.

boundkey.fx

The constraint or variable is fixed.

boundkey.fr

The constraint or variable is free.

boundkey.ra

The constraint or variable is ranged.

mark

Mark

mark.lo

The lower bound is selected for sensitivity analysis.

mark.up

The upper bound is selected for sensitivity analysis.

simdegen

Degeneracy strategies

simdegen.none

The simplex optimizer should use no degeneration strategy.

simdegen.free

The simplex optimizer chooses the degeneration strategy.

simdegen.aggressive

The simplex optimizer should use an aggressive degeneration strategy.

simdegen.moderate

The simplex optimizer should use a moderate degeneration strategy.

simdegen.minimum

The simplex optimizer should use a minimum degeneration strategy.

transpose

Transposed matrix.

transpose.no

No transpose is applied.

transpose.yes

A transpose is applied.

uplo

Triangular part of a symmetric matrix.

uplo.lo

Lower part.

uplo.up

Upper part.

simreform

Problem reformulation.

simreform.on

Allow the simplex optimizer to reformulate the problem.

simreform.off

Disallow the simplex optimizer to reformulate the problem.

simreform.free

The simplex optimizer can choose freely.

simreform.aggressive

The simplex optimizer should use an aggressive reformulation strategy.

simdupvec

Exploit duplicate columns.

simdupvec.on

Allow the simplex optimizer to exploit duplicated columns.

simdupvec.off

Disallow the simplex optimizer to exploit duplicated columns.

simdupvec.free

The simplex optimizer can choose freely.

simhotstart

Hot-start type employed by the simplex optimizer

simhotstart.none

The simplex optimizer performs a coldstart.

simhotstart.free

The simplex optimize chooses the hot-start type.

simhotstart.status_keys

Only the status keys of the constraints and variables are used to choose the type of hot-start.

intpnthotstart

Hot-start type employed by the interior-point optimizers.

intpnthotstart.none

The interior-point optimizer performs a coldstart.

intpnthotstart.primal

The interior-point optimizer exploits the primal solution only.

intpnthotstart.dual

The interior-point optimizer exploits the dual solution only.

intpnthotstart.primal_dual

The interior-point optimizer exploits both the primal and dual solution.

purify

Solution purification employed optimizer.

purify.none

The optimizer performs no solution purification.

purify.primal

The optimizer purifies the primal solution.

purify.dual

The optimizer purifies the dual solution.

purify.primal_dual

The optimizer purifies both the primal and dual solution.

purify.auto

TBD

callbackcode

Progress callback codes

callbackcode.begin_bi

The basis identification procedure has been started.

callbackcode.begin_conic

The callback function is called when the conic optimizer is started.

callbackcode.begin_dual_bi

The callback function is called from within the basis identification procedure when the dual phase is started.

callbackcode.begin_dual_sensitivity

Dual sensitivity analysis is started.

callbackcode.begin_dual_setup_bi

The callback function is called when the dual BI phase is started.

callbackcode.begin_dual_simplex

The callback function is called when the dual simplex optimizer started.

callbackcode.begin_dual_simplex_bi

The callback function is called from within the basis identification procedure when the dual simplex clean-up phase is started.

callbackcode.begin_infeas_ana

The callback function is called when the infeasibility analyzer is started.

callbackcode.begin_intpnt

The callback function is called when the interior-point optimizer is started.

callbackcode.begin_license_wait

Begin waiting for license.

callbackcode.begin_mio

The callback function is called when the mixed-integer optimizer is started.

callbackcode.begin_optimizer

The callback function is called when the optimizer is started.

callbackcode.begin_presolve

The callback function is called when the presolve is started.

callbackcode.begin_primal_bi

The callback function is called from within the basis identification procedure when the primal phase is started.

callbackcode.begin_primal_repair

Begin primal feasibility repair.

callbackcode.begin_primal_sensitivity

Primal sensitivity analysis is started.

callbackcode.begin_primal_setup_bi

The callback function is called when the primal BI setup is started.

callbackcode.begin_primal_simplex

The callback function is called when the primal simplex optimizer is started.

callbackcode.begin_primal_simplex_bi

The callback function is called from within the basis identification procedure when the primal simplex clean-up phase is started.

callbackcode.begin_qcqo_reformulate

Begin QCQO reformulation.

callbackcode.begin_read

MOSEK has started reading a problem file.

callbackcode.begin_root_cutgen

The callback function is called when root cut generation is started.

callbackcode.begin_simplex

The callback function is called when the simplex optimizer is started.

callbackcode.begin_simplex_bi

The callback function is called from within the basis identification procedure when the simplex clean-up phase is started.

callbackcode.begin_solve_root_relax

The callback function is called when solution of root relaxation is started.

callbackcode.begin_to_conic

Begin conic reformulation.

callbackcode.begin_write

MOSEK has started writing a problem file.

callbackcode.conic

The callback function is called from within the conic optimizer after the information database has been updated.

callbackcode.dual_simplex

The callback function is called from within the dual simplex optimizer.

callbackcode.end_bi

The callback function is called when the basis identification procedure is terminated.

callbackcode.end_conic

The callback function is called when the conic optimizer is terminated.

callbackcode.end_dual_bi

The callback function is called from within the basis identification procedure when the dual phase is terminated.

callbackcode.end_dual_sensitivity

Dual sensitivity analysis is terminated.

callbackcode.end_dual_setup_bi

The callback function is called when the dual BI phase is terminated.

callbackcode.end_dual_simplex

The callback function is called when the dual simplex optimizer is terminated.

callbackcode.end_dual_simplex_bi

The callback function is called from within the basis identification procedure when the dual clean-up phase is terminated.

callbackcode.end_infeas_ana

The callback function is called when the infeasibility analyzer is terminated.

callbackcode.end_intpnt

The callback function is called when the interior-point optimizer is terminated.

callbackcode.end_license_wait

End waiting for license.

callbackcode.end_mio

The callback function is called when the mixed-integer optimizer is terminated.

callbackcode.end_optimizer

The callback function is called when the optimizer is terminated.

callbackcode.end_presolve

The callback function is called when the presolve is completed.

callbackcode.end_primal_bi

The callback function is called from within the basis identification procedure when the primal phase is terminated.

callbackcode.end_primal_repair

End primal feasibility repair.

callbackcode.end_primal_sensitivity

Primal sensitivity analysis is terminated.

callbackcode.end_primal_setup_bi

The callback function is called when the primal BI setup is terminated.

callbackcode.end_primal_simplex

The callback function is called when the primal simplex optimizer is terminated.

callbackcode.end_primal_simplex_bi

The callback function is called from within the basis identification procedure when the primal clean-up phase is terminated.

callbackcode.end_qcqo_reformulate

End QCQO reformulation.

callbackcode.end_read

MOSEK has finished reading a problem file.

callbackcode.end_root_cutgen

The callback function is called when root cut generation is terminated.

callbackcode.end_simplex

The callback function is called when the simplex optimizer is terminated.

callbackcode.end_simplex_bi

The callback function is called from within the basis identification procedure when the simplex clean-up phase is terminated.

callbackcode.end_solve_root_relax

The callback function is called when solution of root relaxation is terminated.

callbackcode.end_to_conic

End conic reformulation.

callbackcode.end_write

MOSEK has finished writing a problem file.

callbackcode.im_bi

The callback function is called from within the basis identification procedure at an intermediate point.

callbackcode.im_conic

The callback function is called at an intermediate stage within the conic optimizer where the information database has not been updated.

callbackcode.im_dual_bi

The callback function is called from within the basis identification procedure at an intermediate point in the dual phase.

callbackcode.im_dual_sensivity

The callback function is called at an intermediate stage of the dual sensitivity analysis.

callbackcode.im_dual_simplex

The callback function is called at an intermediate point in the dual simplex optimizer.

callbackcode.im_intpnt

The callback function is called at an intermediate stage within the interior-point optimizer where the information database has not been updated.

callbackcode.im_license_wait

MOSEK is waiting for a license.

callbackcode.im_lu

The callback function is called from within the LU factorization procedure at an intermediate point.

callbackcode.im_mio

The callback function is called at an intermediate point in the mixed-integer optimizer.

callbackcode.im_mio_dual_simplex

The callback function is called at an intermediate point in the mixed-integer optimizer while running the dual simplex optimizer.

callbackcode.im_mio_intpnt

The callback function is called at an intermediate point in the mixed-integer optimizer while running the interior-point optimizer.

callbackcode.im_mio_primal_simplex

The callback function is called at an intermediate point in the mixed-integer optimizer while running the primal simplex optimizer.

callbackcode.im_order

The callback function is called from within the matrix ordering procedure at an intermediate point.

callbackcode.im_presolve

The callback function is called from within the presolve procedure at an intermediate stage.

callbackcode.im_primal_bi

The callback function is called from within the basis identification procedure at an intermediate point in the primal phase.

callbackcode.im_primal_sensivity

The callback function is called at an intermediate stage of the primal sensitivity analysis.

callbackcode.im_primal_simplex

The callback function is called at an intermediate point in the primal simplex optimizer.

callbackcode.im_qo_reformulate

The callback function is called at an intermediate stage of the conic quadratic reformulation.

callbackcode.im_read

Intermediate stage in reading.

callbackcode.im_root_cutgen

The callback is called from within root cut generation at an intermediate stage.

callbackcode.im_simplex

The callback function is called from within the simplex optimizer at an intermediate point.

callbackcode.im_simplex_bi

The callback function is called from within the basis identification procedure at an intermediate point in the simplex clean-up phase. The frequency of the callbacks is controlled by the iparam.log_sim_freq parameter.

callbackcode.intpnt

The callback function is called from within the interior-point optimizer after the information database has been updated.

callbackcode.new_int_mio

The callback function is called after a new integer solution has been located by the mixed-integer optimizer.

callbackcode.primal_simplex

The callback function is called from within the primal simplex optimizer.

callbackcode.read_opf

The callback function is called from the OPF reader.

callbackcode.read_opf_section

A chunk of $$Q$$ non-zeros has been read from a problem file.

callbackcode.solving_remote

The callback function is called while the task is being solved on a remote server.

callbackcode.update_dual_bi

The callback function is called from within the basis identification procedure at an intermediate point in the dual phase.

callbackcode.update_dual_simplex

The callback function is called in the dual simplex optimizer.

callbackcode.update_dual_simplex_bi

The callback function is called from within the basis identification procedure at an intermediate point in the dual simplex clean-up phase. The frequency of the callbacks is controlled by the iparam.log_sim_freq parameter.

callbackcode.update_presolve

The callback function is called from within the presolve procedure.

callbackcode.update_primal_bi

The callback function is called from within the basis identification procedure at an intermediate point in the primal phase.

callbackcode.update_primal_simplex

The callback function is called in the primal simplex optimizer.

callbackcode.update_primal_simplex_bi

The callback function is called from within the basis identification procedure at an intermediate point in the primal simplex clean-up phase. The frequency of the callbacks is controlled by the iparam.log_sim_freq parameter.

callbackcode.update_simplex

The callback function is called from simplex optimizer.

callbackcode.write_opf

The callback function is called from the OPF writer.

checkconvexitytype

Types of convexity checks.

checkconvexitytype.none

No convexity check.

checkconvexitytype.simple

Perform simple and fast convexity check.

checkconvexitytype.full

Perform a full convexity check.

compresstype

Compression types

compresstype.none

No compression is used.

compresstype.free

The type of compression used is chosen automatically.

compresstype.gzip

The type of compression used is gzip compatible.

compresstype.zstd

The type of compression used is zstd compatible.

conetype

Cone types

conetype.quad

The cone is a quadratic cone.

conetype.rquad

The cone is a rotated quadratic cone.

conetype.pexp

A primal exponential cone.

conetype.dexp

A dual exponential cone.

conetype.ppow

A primal power cone.

conetype.dpow

A dual power cone.

conetype.zero

The zero cone.

domaintype

Cone types

domaintype.r
domaintype.rzero

The zero vector.

domaintype.rplus

The positive orthant.

domaintype.rminus

The negative orthant.

domaintype.quadratic_cone

The quadratic cone.

domaintype.rquadratic_cone

The rotated quadratic cone.

domaintype.primal_exp_cone

The primal exponential cone.

domaintype.dual_exp_cone

The dual exponential cone.

domaintype.primal_power_cone

The primal power cone.

domaintype.dual_power_cone

The dual power cone.

domaintype.primal_geo_mean_cone

The primal geometric mean cone.

domaintype.dual_geo_mean_cone

The dual geometric mean cone.

domaintype.svec_psd_cone

The vectorized positive semidefinite cone.

nametype

Name types

nametype.gen

General names. However, no duplicate and blank names are allowed.

nametype.mps

MPS type names.

nametype.lp

LP type names.

symmattype

Cone types

symmattype.sparse

Sparse symmetric matrix.

dataformat

Data format types

dataformat.extension

The file extension is used to determine the data file format.

dataformat.mps

The data file is MPS formatted.

dataformat.lp

The data file is LP formatted.

dataformat.op

The data file is an optimization problem formatted file.

dataformat.free_mps

The data a free MPS formatted file.

dataformat.task

Generic task dump file.

dataformat.ptf

(P)retty (T)ext (F)format.

dataformat.cb

Conic benchmark format,

dataformat.json_task

JSON based task format.

solformat

Data format types

solformat.extension

The file extension is used to determine the data file format.

solformat.b

Simple binary format

solformat.task

Tar based format.

solformat.json_task

JSON based format.

dinfitem

Double information items

dinfitem.ana_pro_scalarized_constraint_matrix_density

Density percentage of the scalarized constraint matrix.

dinfitem.bi_clean_dual_time

Time spent within the dual clean-up optimizer of the basis identification procedure since its invocation.

dinfitem.bi_clean_primal_time

Time spent within the primal clean-up optimizer of the basis identification procedure since its invocation.

dinfitem.bi_clean_time

Time spent within the clean-up phase of the basis identification procedure since its invocation.

dinfitem.bi_dual_time

Time spent within the dual phase basis identification procedure since its invocation.

dinfitem.bi_primal_time

Time spent within the primal phase of the basis identification procedure since its invocation.

dinfitem.bi_time

Time spent within the basis identification procedure since its invocation.

dinfitem.intpnt_dual_feas

Dual feasibility measure reported by the interior-point optimizer. (For the interior-point optimizer this measure is not directly related to the original problem because a homogeneous model is employed.)

dinfitem.intpnt_dual_obj

Dual objective value reported by the interior-point optimizer.

dinfitem.intpnt_factor_num_flops

An estimate of the number of flops used in the factorization.

dinfitem.intpnt_opt_status

A measure of optimality of the solution. It should converge to $$+1$$ if the problem has a primal-dual optimal solution, and converge to $$-1$$ if the problem is (strictly) primal or dual infeasible. If the measure converges to another constant, or fails to settle, the problem is usually ill-posed.

dinfitem.intpnt_order_time

Order time (in seconds).

dinfitem.intpnt_primal_feas

Primal feasibility measure reported by the interior-point optimizer. (For the interior-point optimizer this measure is not directly related to the original problem because a homogeneous model is employed).

dinfitem.intpnt_primal_obj

Primal objective value reported by the interior-point optimizer.

dinfitem.intpnt_time

Time spent within the interior-point optimizer since its invocation.

dinfitem.mio_clique_separation_time

Separation time for clique cuts.

dinfitem.mio_cmir_separation_time

Separation time for CMIR cuts.

dinfitem.mio_construct_solution_obj

If MOSEK has successfully constructed an integer feasible solution, then this item contains the optimal objective value corresponding to the feasible solution.

dinfitem.mio_dual_bound_after_presolve

Value of the dual bound after presolve but before cut generation.

dinfitem.mio_gmi_separation_time

Separation time for GMI cuts.

dinfitem.mio_implied_bound_time

Separation time for implied bound cuts.

dinfitem.mio_initial_feasible_solution_obj

If the user provided solution was found to be feasible this information item contains it’s objective value.

dinfitem.mio_knapsack_cover_separation_time

Separation time for knapsack cover.

dinfitem.mio_lipro_separation_time

Separation time for lift-and-project cuts.

dinfitem.mio_obj_abs_gap

Given the mixed-integer optimizer has computed a feasible solution and a bound on the optimal objective value, then this item contains the absolute gap defined by

$|\mbox{(objective value of feasible solution)}-\mbox{(objective bound)}|.$

Otherwise it has the value -1.0.

dinfitem.mio_obj_bound

The best known bound on the objective function. This value is undefined until at least one relaxation has been solved: To see if this is the case check that iinfitem.mio_num_relax is strictly positive.

dinfitem.mio_obj_int

The primal objective value corresponding to the best integer feasible solution. Please note that at least one integer feasible solution must have been located i.e. check iinfitem.mio_num_int_solutions.

dinfitem.mio_obj_rel_gap

Given that the mixed-integer optimizer has computed a feasible solution and a bound on the optimal objective value, then this item contains the relative gap defined by

$\frac{| \mbox{(objective value of feasible solution)}-\mbox{(objective bound)} | }{\max(\delta,|\mbox{(objective value of feasible solution)}|)}.$

where $$\delta$$ is given by the parameter dparam.mio_rel_gap_const. Otherwise it has the value $$-1.0$$.

dinfitem.mio_probing_time

Total time for probing.

dinfitem.mio_root_cutgen_time

Total time for cut generation.

dinfitem.mio_root_optimizer_time

Time spent in the contiuous optimizer while processing the root node relaxation.

dinfitem.mio_root_presolve_time

Time spent presolving the problem at the root node.

dinfitem.mio_root_time

Time spent processing the root node.

dinfitem.mio_time

Time spent in the mixed-integer optimizer.

dinfitem.mio_user_obj_cut

If the objective cut is used, then this information item has the value of the cut.

dinfitem.optimizer_time

Total time spent in the optimizer since it was invoked.

dinfitem.presolve_eli_time

Total time spent in the eliminator since the presolve was invoked.

dinfitem.presolve_lindep_time

Total time spent in the linear dependency checker since the presolve was invoked.

dinfitem.presolve_time

Total time (in seconds) spent in the presolve since it was invoked.

dinfitem.presolve_total_primal_perturbation

Total perturbation of the bounds of the primal problem.

dinfitem.primal_repair_penalty_obj

The optimal objective value of the penalty function.

dinfitem.qcqo_reformulate_max_perturbation

Maximum absolute diagonal perturbation occurring during the QCQO reformulation.

dinfitem.qcqo_reformulate_time

Time spent with conic quadratic reformulation.

dinfitem.qcqo_reformulate_worst_cholesky_column_scaling

Worst Cholesky column scaling.

dinfitem.qcqo_reformulate_worst_cholesky_diag_scaling

Worst Cholesky diagonal scaling.

dinfitem.read_data_time

Time spent reading the data file.

dinfitem.remote_time

The total real time in seconds spent when optimizing on a server by the process performing the optimization on the server

dinfitem.sim_dual_time

Time spent in the dual simplex optimizer since invoking it.

dinfitem.sim_feas

Feasibility measure reported by the simplex optimizer.

dinfitem.sim_obj

Objective value reported by the simplex optimizer.

dinfitem.sim_primal_time

Time spent in the primal simplex optimizer since invoking it.

dinfitem.sim_time

Time spent in the simplex optimizer since invoking it.

dinfitem.sol_bas_dual_obj

Dual objective value of the basic solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_bas_dviolcon

Maximal dual bound violation for $$x^c$$ in the basic solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_bas_dviolvar

Maximal dual bound violation for $$x^x$$ in the basic solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_bas_nrm_barx

Infinity norm of $$\barX$$ in the basic solution.

dinfitem.sol_bas_nrm_slc

Infinity norm of $$s_l^c$$ in the basic solution.

dinfitem.sol_bas_nrm_slx

Infinity norm of $$s_l^x$$ in the basic solution.

dinfitem.sol_bas_nrm_suc

Infinity norm of $$s_u^c$$ in the basic solution.

dinfitem.sol_bas_nrm_sux

Infinity norm of $$s_u^X$$ in the basic solution.

dinfitem.sol_bas_nrm_xc

Infinity norm of $$x^c$$ in the basic solution.

dinfitem.sol_bas_nrm_xx

Infinity norm of $$x^x$$ in the basic solution.

dinfitem.sol_bas_nrm_y

Infinity norm of $$y$$ in the basic solution.

dinfitem.sol_bas_primal_obj

Primal objective value of the basic solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_bas_pviolcon

Maximal primal bound violation for $$x^c$$ in the basic solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_bas_pviolvar

Maximal primal bound violation for $$x^x$$ in the basic solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itg_nrm_barx

Infinity norm of $$\barX$$ in the integer solution.

dinfitem.sol_itg_nrm_xc

Infinity norm of $$x^c$$ in the integer solution.

dinfitem.sol_itg_nrm_xx

Infinity norm of $$x^x$$ in the integer solution.

dinfitem.sol_itg_primal_obj

Primal objective value of the integer solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itg_pviolacc

Maximal primal violation for affine conic constraints in the integer solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itg_pviolbarvar

Maximal primal bound violation for $$\barX$$ in the integer solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itg_pviolcon

Maximal primal bound violation for $$x^c$$ in the integer solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itg_pviolcones

Maximal primal violation for primal conic constraints in the integer solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itg_pvioldjc

Maximal primal violation for disjunctive constraints in the integer solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itg_pviolitg

Maximal violation for the integer constraints in the integer solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itg_pviolvar

Maximal primal bound violation for $$x^x$$ in the integer solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itr_dual_obj

Dual objective value of the interior-point solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itr_dviolacc

Maximal dual violation for the affine conic constraints in the interior-point solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itr_dviolbarvar

Maximal dual bound violation for $$\barX$$ in the interior-point solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itr_dviolcon

Maximal dual bound violation for $$x^c$$ in the interior-point solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itr_dviolcones

Maximal dual violation for conic constraints in the interior-point solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itr_dviolvar

Maximal dual bound violation for $$x^x$$ in the interior-point solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itr_nrm_bars

Infinity norm of $$\barS$$ in the interior-point solution.

dinfitem.sol_itr_nrm_barx

Infinity norm of $$\barX$$ in the interior-point solution.

dinfitem.sol_itr_nrm_slc

Infinity norm of $$s_l^c$$ in the interior-point solution.

dinfitem.sol_itr_nrm_slx

Infinity norm of $$s_l^x$$ in the interior-point solution.

dinfitem.sol_itr_nrm_snx

Infinity norm of $$s_n^x$$ in the interior-point solution.

dinfitem.sol_itr_nrm_suc

Infinity norm of $$s_u^c$$ in the interior-point solution.

dinfitem.sol_itr_nrm_sux

Infinity norm of $$s_u^X$$ in the interior-point solution.

dinfitem.sol_itr_nrm_xc

Infinity norm of $$x^c$$ in the interior-point solution.

dinfitem.sol_itr_nrm_xx

Infinity norm of $$x^x$$ in the interior-point solution.

dinfitem.sol_itr_nrm_y

Infinity norm of $$y$$ in the interior-point solution.

dinfitem.sol_itr_primal_obj

Primal objective value of the interior-point solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itr_pviolacc

Maximal primal violation for affine conic constraints in the interior-point solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itr_pviolbarvar

Maximal primal bound violation for $$\barX$$ in the interior-point solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itr_pviolcon

Maximal primal bound violation for $$x^c$$ in the interior-point solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itr_pviolcones

Maximal primal violation for conic constraints in the interior-point solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.sol_itr_pviolvar

Maximal primal bound violation for $$x^x$$ in the interior-point solution. Updated if iparam.auto_update_sol_info is set or by the method Task.updatesolutioninfo.

dinfitem.to_conic_time

Time spent in the last to conic reformulation.

dinfitem.write_data_time

Time spent writing the data file.

feature

License feature

feature.pts

Base system.

feature.pton

Conic extension.

liinfitem

Long integer information items.

liinfitem.ana_pro_scalarized_constraint_matrix_num_columns

Number of columns in the scalarized constraint matrix.

liinfitem.ana_pro_scalarized_constraint_matrix_num_nz

Number of non-zero entries in the scalarized constraint matrix.

liinfitem.ana_pro_scalarized_constraint_matrix_num_rows

Number of rows in the scalarized constraint matrix.

liinfitem.bi_clean_dual_deg_iter

Number of dual degenerate clean iterations performed in the basis identification.

liinfitem.bi_clean_dual_iter

Number of dual clean iterations performed in the basis identification.

liinfitem.bi_clean_primal_deg_iter

Number of primal degenerate clean iterations performed in the basis identification.

liinfitem.bi_clean_primal_iter

Number of primal clean iterations performed in the basis identification.

liinfitem.bi_dual_iter

Number of dual pivots performed in the basis identification.

liinfitem.bi_primal_iter

Number of primal pivots performed in the basis identification.

liinfitem.intpnt_factor_num_nz

Number of non-zeros in factorization.

liinfitem.mio_anz

Number of non-zero entries in the constraint matrix of the problem to be solved by the mixed-integer optimizer.

liinfitem.mio_intpnt_iter

Number of interior-point iterations performed by the mixed-integer optimizer.

liinfitem.mio_num_dual_illposed_cer

Number of dual illposed certificates encountered by the mixed-integer optimizer.

liinfitem.mio_num_prim_illposed_cer

Number of primal illposed certificates encountered by the mixed-integer optimizer.

liinfitem.mio_presolved_anz

Number of non-zero entries in the constraint matrix of the problem after the mixed-integer optimizer’s presolve.

liinfitem.mio_simplex_iter

Number of simplex iterations performed by the mixed-integer optimizer.

liinfitem.rd_numacc

Number of affince conic constraints.

liinfitem.rd_numanz

Number of non-zeros in A that is read.

liinfitem.rd_numdjc

Number of disjuncive constraints.

liinfitem.rd_numqnz

Number of Q non-zeros.

liinfitem.simplex_iter

Number of iterations performed by the simplex optimizer.

iinfitem

Integer information items.

iinfitem.ana_pro_num_con

Number of constraints in the problem. This value is set by Task.analyzeproblem.

iinfitem.ana_pro_num_con_eq

Number of equality constraints. This value is set by Task.analyzeproblem.

iinfitem.ana_pro_num_con_fr

Number of unbounded constraints. This value is set by Task.analyzeproblem.

iinfitem.ana_pro_num_con_lo

Number of constraints with a lower bound and an infinite upper bound. This value is set by Task.analyzeproblem.

iinfitem.ana_pro_num_con_ra

Number of constraints with finite lower and upper bounds. This value is set by Task.analyzeproblem.

iinfitem.ana_pro_num_con_up

Number of constraints with an upper bound and an infinite lower bound. This value is set by Task.analyzeproblem.

iinfitem.ana_pro_num_var

Number of variables in the problem. This value is set by Task.analyzeproblem.

iinfitem.ana_pro_num_var_bin

Number of binary (0-1) variables. This value is set by Task.analyzeproblem.

iinfitem.ana_pro_num_var_cont

Number of continuous variables. This value is set by Task.analyzeproblem.

iinfitem.ana_pro_num_var_eq

Number of fixed variables. This value is set by Task.analyzeproblem.

iinfitem.ana_pro_num_var_fr

Number of free variables. This value is set by Task.analyzeproblem.

iinfitem.ana_pro_num_var_int

Number of general integer variables. This value is set by Task.analyzeproblem.

iinfitem.ana_pro_num_var_lo

Number of variables with a lower bound and an infinite upper bound. This value is set by Task.analyzeproblem.

iinfitem.ana_pro_num_var_ra

Number of variables with finite lower and upper bounds. This value is set by Task.analyzeproblem.

iinfitem.ana_pro_num_var_up

Number of variables with an upper bound and an infinite lower bound. This value is set by Task.analyzeproblem.

iinfitem.intpnt_factor_dim_dense

Dimension of the dense sub system in factorization.

iinfitem.intpnt_iter

Number of interior-point iterations since invoking the interior-point optimizer.

iinfitem.intpnt_num_threads

Number of threads that the interior-point optimizer is using.

iinfitem.intpnt_solve_dual

Non-zero if the interior-point optimizer is solving the dual problem.

iinfitem.mio_absgap_satisfied

Non-zero if absolute gap is within tolerances.

iinfitem.mio_clique_table_size

Size of the clique table.

iinfitem.mio_construct_solution

This item informs if MOSEK constructed an initial integer feasible solution.

• -1: tried, but failed,

• 0: no partial solution supplied by the user,

• 1: constructed feasible solution.

iinfitem.mio_initial_feasible_solution

This item informs if MOSEK found the solution provided by the user to be feasible

• 0: solution provided by the user was not found to be feasible for the current problem,

• 1: user provided solution was found to be feasible.

iinfitem.mio_node_depth

Depth of the last node solved.

iinfitem.mio_num_active_nodes

Number of active branch and bound nodes.

iinfitem.mio_num_branch

Number of branches performed during the optimization.

iinfitem.mio_num_clique_cuts

Number of clique cuts.

iinfitem.mio_num_cmir_cuts

Number of Complemented Mixed Integer Rounding (CMIR) cuts.

iinfitem.mio_num_gomory_cuts

Number of Gomory cuts.

iinfitem.mio_num_implied_bound_cuts

Number of implied bound cuts.

iinfitem.mio_num_int_solutions

Number of integer feasible solutions that have been found.

iinfitem.mio_num_knapsack_cover_cuts

Number of clique cuts.

iinfitem.mio_num_lipro_cuts

Number of lift-and-project cuts.

iinfitem.mio_num_relax

Number of relaxations solved during the optimization.

iinfitem.mio_num_repeated_presolve

Number of times presolve was repeated at root.

iinfitem.mio_numbin

Number of binary variables in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numbinconevar

Number of binary cone variables in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numcon

Number of constraints in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numcone

Number of cones in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numconevar

Number of cone variables in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numcont

Number of continuous variables in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numcontconevar

Number of continuous cone variables in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numdexpcones

Number of dual exponential cones in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numdjc

Number of disjunctive constraints in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numdpowcones

Number of dual power cones in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numint

Number of integer variables in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numintconevar

Number of integer cone variables in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numpexpcones

Number of primal exponential cones in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numppowcones

Number of primal power cones in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numqcones

Number of quadratic cones in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numrqcones

Number of rotated quadratic cones in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_numvar

Number of variables in the problem to be solved by the mixed-integer optimizer.

iinfitem.mio_obj_bound_defined

Non-zero if a valid objective bound has been found, otherwise zero.

iinfitem.mio_presolved_numbin

Number of binary variables in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numbinconevar

Number of binary cone variables in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numcon

Number of constraints in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numcone

Number of cones in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numconevar

Number of cone variables in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numcont

Number of continuous variables in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numcontconevar

Number of continuous cone variables in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numdexpcones

Number of dual exponential cones in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numdjc

Number of disjunctive constraints in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numdpowcones

Number of dual power cones in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numint

Number of integer variables in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numintconevar

Number of integer cone variables in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numpexpcones

Number of primal exponential cones in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numppowcones

Number of primal power cones in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numqcones

Number of quadratic cones in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numrqcones

Number of rotated quadratic cones in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_presolved_numvar

Number of variables in the problem after the mixed-integer optimizer’s presolve.

iinfitem.mio_relgap_satisfied

Non-zero if relative gap is within tolerances.

iinfitem.mio_total_num_cuts

Total number of cuts generated by the mixed-integer optimizer.

iinfitem.mio_user_obj_cut

If it is non-zero, then the objective cut is used.

iinfitem.opt_numcon

Number of constraints in the problem solved when the optimizer is called.

iinfitem.opt_numvar

Number of variables in the problem solved when the optimizer is called

iinfitem.optimize_response

The response code returned by optimize.

iinfitem.presolve_num_primal_perturbations

Number perturbations to thhe bounds of the primal problem.

iinfitem.purify_dual_success

Is nonzero if the dual solution is purified.

iinfitem.purify_primal_success

Is nonzero if the primal solution is purified.

iinfitem.rd_numbarvar

Number of symmetric variables read.

iinfitem.rd_numcon

Number of constraints read.

iinfitem.rd_numcone

Number of conic constraints read.

iinfitem.rd_numintvar

Number of integer-constrained variables read.

iinfitem.rd_numq

Number of nonempty Q matrices read.

iinfitem.rd_numvar

Number of variables read.

iinfitem.rd_protype

Problem type.

iinfitem.sim_dual_deg_iter

The number of dual degenerate iterations.

iinfitem.sim_dual_hotstart

If 1 then the dual simplex algorithm is solving from an advanced basis.

iinfitem.sim_dual_hotstart_lu

If 1 then a valid basis factorization of full rank was located and used by the dual simplex algorithm.

iinfitem.sim_dual_inf_iter

The number of iterations taken with dual infeasibility.

iinfitem.sim_dual_iter

Number of dual simplex iterations during the last optimization.

iinfitem.sim_numcon

Number of constraints in the problem solved by the simplex optimizer.

iinfitem.sim_numvar

Number of variables in the problem solved by the simplex optimizer.

iinfitem.sim_primal_deg_iter

The number of primal degenerate iterations.

iinfitem.sim_primal_hotstart

If 1 then the primal simplex algorithm is solving from an advanced basis.

iinfitem.sim_primal_hotstart_lu

If 1 then a valid basis factorization of full rank was located and used by the primal simplex algorithm.

iinfitem.sim_primal_inf_iter

The number of iterations taken with primal infeasibility.

iinfitem.sim_primal_iter

Number of primal simplex iterations during the last optimization.

iinfitem.sim_solve_dual

Is non-zero if dual problem is solved.

iinfitem.sol_bas_prosta

Problem status of the basic solution. Updated after each optimization.

iinfitem.sol_bas_solsta

Solution status of the basic solution. Updated after each optimization.

iinfitem.sol_itg_prosta

Problem status of the integer solution. Updated after each optimization.

iinfitem.sol_itg_solsta

Solution status of the integer solution. Updated after each optimization.

iinfitem.sol_itr_prosta

Problem status of the interior-point solution. Updated after each optimization.

iinfitem.sol_itr_solsta

Solution status of the interior-point solution. Updated after each optimization.

iinfitem.sto_num_a_realloc

Number of times the storage for storing $$A$$ has been changed. A large value may indicates that memory fragmentation may occur.

inftype

Information item types

inftype.dou_type

Is a double information type.

inftype.int_type

Is an integer.

inftype.lint_type

Is a long integer.

iomode

Input/output modes

iomode.read

The file is read-only.

iomode.write

The file is write-only. If the file exists then it is truncated when it is opened. Otherwise it is created when it is opened.

iomode.readwrite

The file is to read and write.

branchdir

Specifies the branching direction.

branchdir.free

The mixed-integer optimizer decides which branch to choose.

branchdir.up

The mixed-integer optimizer always chooses the up branch first.

branchdir.down

The mixed-integer optimizer always chooses the down branch first.

branchdir.near

Branch in direction nearest to selected fractional variable.

branchdir.far

Branch in direction farthest from selected fractional variable.

branchdir.root_lp

Chose direction based on root lp value of selected variable.

branchdir.guided

Branch in direction of current incumbent.

branchdir.pseudocost

Branch based on the pseudocost of the variable.

miqcqoreformmethod

Specifies the reformulation method for mixed-integer quadratic problems.

miqcqoreformmethod.free

The mixed-integer optimizer decides which reformulation method to apply.

miqcqoreformmethod.none

No reformulation method is applied.

miqcqoreformmethod.linearization

A reformulation via linearization is applied.

miqcqoreformmethod.eigen_val_method

The eigenvalue method is applied.

miqcqoreformmethod.diag_sdp

A perturbation of matrix diagonals via the solution of SDPs is applied.

miqcqoreformmethod.relax_sdp

A Reformulation based on the solution of an SDP-relaxation of the problem is applied.

miodatapermmethod

Specifies the problem data permutation method for mixed-integer problems.

miodatapermmethod.none

No problem data permutation is applied.

miodatapermmethod.cyclic_shift

A random cyclic shift is applied to permute the problem data.

miodatapermmethod.random

A random permutation is applied to the problem data.

miocontsoltype

Continuous mixed-integer solution type

miocontsoltype.none

No interior-point or basic solution are reported when the mixed-integer optimizer is used.

miocontsoltype.root

The reported interior-point and basic solutions are a solution to the root node problem when mixed-integer optimizer is used.

miocontsoltype.itg

The reported interior-point and basic solutions are a solution to the problem with all integer variables fixed at the value they have in the integer solution. A solution is only reported in case the problem has a primal feasible solution.

miocontsoltype.itg_rel

In case the problem is primal feasible then the reported interior-point and basic solutions are a solution to the problem with all integer variables fixed at the value they have in the integer solution. If the problem is primal infeasible, then the solution to the root node problem is reported.

miomode

Integer restrictions

miomode.ignored

The integer constraints are ignored and the problem is solved as a continuous problem.

miomode.satisfied

Integer restrictions should be satisfied.

mionodeseltype

Mixed-integer node selection types

mionodeseltype.free

The optimizer decides the node selection strategy.

mionodeseltype.first

The optimizer employs a depth first node selection strategy.

mionodeseltype.best

The optimizer employs a best bound node selection strategy.

mionodeseltype.pseudo

The optimizer employs selects the node based on a pseudo cost estimate.

mpsformat

MPS file format type

mpsformat.strict

It is assumed that the input file satisfies the MPS format strictly.

mpsformat.relaxed

It is assumed that the input file satisfies a slightly relaxed version of the MPS format.

mpsformat.free

It is assumed that the input file satisfies the free MPS format. This implies that spaces are not allowed in names. Otherwise the format is free.

mpsformat.cplex

The CPLEX compatible version of the MPS format is employed.

objsense

Objective sense types

objsense.minimize

The problem should be minimized.

objsense.maximize

The problem should be maximized.

onoffkey

On/off

onoffkey.on

Switch the option on.

onoffkey.off

Switch the option off.

optimizertype

Optimizer types

optimizertype.conic

The optimizer for problems having conic constraints.

optimizertype.dual_simplex

The dual simplex optimizer is used.

optimizertype.free

The optimizer is chosen automatically.

optimizertype.free_simplex

One of the simplex optimizers is used.

optimizertype.intpnt

The interior-point optimizer is used.

optimizertype.mixed_int

The mixed-integer optimizer.

optimizertype.primal_simplex

The primal simplex optimizer is used.

orderingtype

Ordering strategies

orderingtype.free

The ordering method is chosen automatically.

orderingtype.appminloc

Approximate minimum local fill-in ordering is employed.

orderingtype.experimental

This option should not be used.

orderingtype.try_graphpar

Always try the graph partitioning based ordering.

orderingtype.force_graphpar

Always use the graph partitioning based ordering even if it is worse than the approximate minimum local fill ordering.

orderingtype.none

No ordering is used.

presolvemode

Presolve method.

presolvemode.off

The problem is not presolved before it is optimized.

presolvemode.on

The problem is presolved before it is optimized.

presolvemode.free

It is decided automatically whether to presolve before the problem is optimized.

parametertype

Parameter type

parametertype.invalid_type

Not a valid parameter.

parametertype.dou_type

Is a double parameter.

parametertype.int_type

Is an integer parameter.

parametertype.str_type

Is a string parameter.

problemitem

Problem data items

problemitem.var

Item is a variable.

problemitem.con

Item is a constraint.

problemitem.cone

Item is a cone.

problemtype

Problem types

problemtype.lo

The problem is a linear optimization problem.

problemtype.qo

The problem is a quadratic optimization problem.

problemtype.qcqo

The problem is a quadratically constrained optimization problem.

problemtype.conic

A conic optimization.

problemtype.mixed

General nonlinear constraints and conic constraints. This combination can not be solved by MOSEK.

prosta

Problem status keys

prosta.unknown

Unknown problem status.

prosta.prim_and_dual_feas

The problem is primal and dual feasible.

prosta.prim_feas

The problem is primal feasible.

prosta.dual_feas

The problem is dual feasible.

prosta.prim_infeas

The problem is primal infeasible.

prosta.dual_infeas

The problem is dual infeasible.

prosta.prim_and_dual_infeas

The problem is primal and dual infeasible.

prosta.ill_posed

The problem is ill-posed. For example, it may be primal and dual feasible but have a positive duality gap.

prosta.prim_infeas_or_unbounded

The problem is either primal infeasible or unbounded. This may occur for mixed-integer problems.

xmlwriteroutputtype

XML writer output mode

xmlwriteroutputtype.row

Write in row order.

xmlwriteroutputtype.col

Write in column order.

rescodetype

Response code type

rescodetype.ok

The response code is OK.

rescodetype.wrn

The response code is a warning.

rescodetype.trm

The response code is an optimizer termination status.

rescodetype.err

The response code is an error.

rescodetype.unk

The response code does not belong to any class.

scalingtype

Scaling type

scalingtype.free

The optimizer chooses the scaling heuristic.

scalingtype.none

No scaling is performed.

scalingmethod

Scaling method

scalingmethod.pow2

Scales only with power of 2 leaving the mantissa untouched.

scalingmethod.free

The optimizer chooses the scaling heuristic.

sensitivitytype

Sensitivity types

sensitivitytype.basis

Basis sensitivity analysis is performed.

simseltype

Simplex selection strategy

simseltype.free

The optimizer chooses the pricing strategy.

simseltype.full

The optimizer uses full pricing.

simseltype.ase

The optimizer uses approximate steepest-edge pricing.

simseltype.devex

The optimizer uses devex steepest-edge pricing (or if it is not available an approximate steep-edge selection).

simseltype.se

The optimizer uses steepest-edge selection (or if it is not available an approximate steep-edge selection).

simseltype.partial

The optimizer uses a partial selection approach. The approach is usually beneficial if the number of variables is much larger than the number of constraints.

solitem

Solution items

solitem.xc

Solution for the constraints.

solitem.xx

Variable solution.

solitem.y

Lagrange multipliers for equations.

solitem.slc

Lagrange multipliers for lower bounds on the constraints.

solitem.suc

Lagrange multipliers for upper bounds on the constraints.

solitem.slx

Lagrange multipliers for lower bounds on the variables.

solitem.sux

Lagrange multipliers for upper bounds on the variables.

solitem.snx

Lagrange multipliers corresponding to the conic constraints on the variables.

solsta

Solution status keys

solsta.unknown

Status of the solution is unknown.

solsta.optimal

The solution is optimal.

solsta.prim_feas

The solution is primal feasible.

solsta.dual_feas

The solution is dual feasible.

solsta.prim_and_dual_feas

The solution is both primal and dual feasible.

solsta.prim_infeas_cer

The solution is a certificate of primal infeasibility.

solsta.dual_infeas_cer

The solution is a certificate of dual infeasibility.

solsta.prim_illposed_cer

The solution is a certificate that the primal problem is illposed.

solsta.dual_illposed_cer

The solution is a certificate that the dual problem is illposed.

solsta.integer_optimal

The primal solution is integer optimal.

soltype

Solution types

soltype.bas

The basic solution.

soltype.itr

The interior solution.

soltype.itg

The integer solution.

solveform

Solve primal or dual form

solveform.free

The optimizer is free to solve either the primal or the dual problem.

solveform.primal

The optimizer should solve the primal problem.

solveform.dual

The optimizer should solve the dual problem.

stakey

Status keys

stakey.unk

The status for the constraint or variable is unknown.

stakey.bas

The constraint or variable is in the basis.

stakey.supbas

The constraint or variable is super basic.

stakey.low

The constraint or variable is at its lower bound.

stakey.upr

The constraint or variable is at its upper bound.

stakey.fix

The constraint or variable is fixed.

stakey.inf

The constraint or variable is infeasible in the bounds.

startpointtype

Starting point types

startpointtype.free

The starting point is chosen automatically.

startpointtype.guess

The optimizer guesses a starting point.

startpointtype.constant

The optimizer constructs a starting point by assigning a constant value to all primal and dual variables. This starting point is normally robust.

startpointtype.satisfy_bounds

The starting point is chosen to satisfy all the simple bounds on nonlinear variables. If this starting point is employed, then more care than usual should employed when choosing the bounds on the nonlinear variables. In particular very tight bounds should be avoided.

streamtype

Stream types

streamtype.log

Log stream. Contains the aggregated contents of all other streams. This means that a message written to any other stream will also be written to this stream.

streamtype.msg

Message stream. Log information relating to performance and progress of the optimization is written to this stream.

streamtype.err

Error stream. Error messages are written to this stream.

streamtype.wrn

Warning stream. Warning messages are written to this stream.

value

Integer values

value.max_str_len

Maximum string length allowed in MOSEK.

value.license_buffer_length

The length of a license key buffer.

variabletype

Variable types

variabletype.type_cont

Is a continuous variable.

variabletype.type_int

Is an integer variable.