The MOSEK Python optimizer API manual Version 7.0 (Revision 141)
Optimizer API for Python - Documentation - Mosek Optimizer API for Python - Documentation - Mosek
578 APPENDIX D. API CONSTANTS D.38 Sensitivity types sensitivitytype.basis Basis sensitivity analysis is performed. sensitivitytype.optimal partition Optimal partition sensitivity analysis is performed. D.39 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. D.40 Exploit duplicate columns. simdupvec.off Disallow the simplex optimizer to exploit duplicated columns. simdupvec.on Allow the simplex optimizer to exploit duplicated columns. simdupvec.free The simplex optimizer can choose freely. D.41 Hot-start type employed by the simplex optimizer simhotstart.none The simplex optimizer performs a coldstart.
D.42. PROBLEM REFORMULATION. 579 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. D.42 Problem reformulation. simreform.off Disallow the simplex optimizer to reformulate the problem. simreform.on Allow 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. D.43 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.
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D.42. PROBLEM REFORMULATION. 579<br />
simhotstart.free<br />
<strong>The</strong> simplex optimize chooses the hot-start type.<br />
simhotstart.status keys<br />
Only the status keys of the constraints and variables are used to choose the type of hot-start.<br />
D.42 Problem reformulation.<br />
simreform.off<br />
Disallow the simplex <strong>optimizer</strong> to reformulate the problem.<br />
simreform.on<br />
Allow the simplex <strong>optimizer</strong> to reformulate the problem.<br />
simreform.free<br />
<strong>The</strong> simplex <strong>optimizer</strong> can choose freely.<br />
simreform.aggressive<br />
<strong>The</strong> simplex <strong>optimizer</strong> should use an aggressive reformulation strategy.<br />
D.43 Simplex selection strategy<br />
simseltype.free<br />
<strong>The</strong> <strong>optimizer</strong> chooses the pricing strategy.<br />
simseltype.full<br />
<strong>The</strong> <strong>optimizer</strong> uses full pricing.<br />
simseltype.ase<br />
<strong>The</strong> <strong>optimizer</strong> uses approximate steepest-edge pricing.<br />
simseltype.devex<br />
<strong>The</strong> <strong>optimizer</strong> uses devex steepest-edge pricing (or if it is not available an approximate steep-edge<br />
selection).<br />
simseltype.se<br />
<strong>The</strong> <strong>optimizer</strong> uses steepest-edge selection (or if it is not available an approximate steep-edge<br />
selection).<br />
simseltype.partial<br />
<strong>The</strong> <strong>optimizer</strong> uses a partial selection approach. <strong>The</strong> approach is usually beneficial if the number<br />
of variables is much larger than the number of constraints.
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