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

146 CHAPTER 11. THE OPTIMIZERS FOR CONTINUOUS PROBLEMS
Parameter name
dparam.intpnt co tol pfeas
dparam.intpnt co tol dfeas
dparam.intpnt co tol rel gap
dparam.intpnt tol infeas
dparam.intpnt co tol mu red
Purpose
Controls primal feasibility
Controls dual feasibility
Controls relative gap
Controls when the problem is declared infeasible
Controls when the complementarity is reduced enough
Table 11.2: Parameters employed in termination criterion.
11.3 Linear network optimization
11.3.1 Network flow problems
Linear optimization problems with network flow structure can often be solved significantly faster with
a specialized version of the simplex method [12] than with the general solvers.
MOSEK includes a network simplex solver which frequently solves network problems significantly faster
than the standard simplex optimizers.
To use the network simplex optimizer, do the following:
• Input the network flow problem as an ordinary linear optimization problem.
• Set the parameters
– iparam.optimizer to optimizertype.network primal simplex.
• Optimize the problem using Task.optimize.
MOSEK will automatically detect the network structure and apply the specialized simplex optimizer.
11.4 Conic optimization
11.4.1 The interior-point optimizer
For conic optimization problems only an interior-point type optimizer is available. The interior-point
optimizer is an implementation of the so-called homogeneous and self-dual algorithm. For a detailed
description of the algorithm, please see [13].
11.4.1.1 Interior-point termination criteria
The parameters controlling when the conic interior-point optimizer terminates are shown in Table 11.2.