The MOSEK Python optimizer API manual Version 7.0 (Revision 141)

400 APPENDIX B. PARAMETERS
• iparam.solution callback. Indicates whether solution call-backs will be performed during
the optimization.
Simplex optimizer parameters.
Parameters defining the behavior of the simplex optimizer for linear problems.
• dparam.basis rel tol s. Maximum relative dual bound violation allowed in an optimal
basic solution.
• dparam.basis tol s. Maximum absolute dual bound violation in an optimal basic solution.
• dparam.basis tol x.
basic solution.
Maximum absolute primal bound violation allowed in an optimal
• iparam.log sim. Controls the amount of log information from the simplex optimizers.
• iparam.log sim freq. Controls simplex logging frequency.
• iparam.log sim minor. Currently not in use.
• iparam.sim basis factor use. Controls whether a (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 penanlty.
• iparam.sim degen. Controls how aggressively degeneration is handled.
• iparam.sim dual phaseone method. An exprimental feature.
• iparam.sim exploit dupvec.
duplicated columns.
Controls if the simplex optimizers are allowed to exploit
• iparam.sim hotstart. Controls the type of hot-start that the simplex optimizer perform.
• iparam.sim integer. An exprimental feature.
• dparam.sim lu tol rel piv. Relative pivot tolerance employed when computing the LU
factorization of the basis matrix.
• iparam.sim max iterations. Maximum number of iterations that can be used by a simplex
optimizer.
• iparam.sim max num setbacks. Controls how many set-backs that are allowed within a
simplex optimizer.
• iparam.sim non singular. Controls if the simplex optimizer ensures a non-singular basis,
if possible.
• iparam.sim primal phaseone method. An exprimental feature.
• iparam.sim reformulation. Controls if the simplex optimizers are allowed to reformulate
the problem.
• iparam.sim save lu. Controls if the LU factorization stored should be replaced with the
LU factorization corresponding to the initial basis.
• iparam.sim scaling. Controls how much effort is used in scaling the problem before a
simplex optimizer is used.
• iparam.sim scaling method. Controls how the problem is scaled before a simplex optimizer
is used.