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

Chapter 11
The optimizers for continuous
problems
The most essential part of MOSEK is the optimizers. Each optimizer is designed to solve a particular
class of problems i.e. linear, conic, or general nonlinear problems. The purpose of the present chapter
is to discuss which optimizers are available for the continuous problem classes and how the performance
of an optimizer can be tuned, if needed.
This chapter deals with the optimizers for continuous problems with no integer variables.
11.1 How an optimizer works
When the optimizer is called, it roughly performs the following steps:
Presolve:
Dualizer:
Scaling:
Preprocessing to reduce the size of the problem.
Choosing whether to solve the primal or the dual form of the problem.
Scaling the problem for better numerical stability.
Optimize:
Solve the problem using selected method.
The first three preprocessing steps are transparent to the user, but useful to know about for tuning
purposes. In general, the purpose of the preprocessing steps is to make the actual optimization more
efficient and robust.
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