Mosek

Features

The MOSEK optimization software is designed to solve large-scale mathematical optimization problems. MOSEK main features are listed below. For additional questions, contact our support or browse the online documentation.

Problem types MOSEK can solve

  • Linear.
  • Conic quadratic.
  • Semi-definite (Positive semi-definite matrix variables).
  • Quadratic and quadratically constrained.
  • General convex nonlinear.
  • Mixed integer linear, conic and quadratic.

Technical highlights

  • Problem size limited only by the available memory.
  • Primal and dual simplex optimizers for linear programming.
  • Highly efficient pre-solver for reducing problem size before optimization.
  • Branch&bound&cut algorithm for mixed integer problems.

Strengths and features of MOSEK

  • The strongest point of MOSEK is its state-of-the-art interior-point optimizer for continous linear, quadratic and conic problems.
  • The optimizer is parallelized and capable of exploiting multiple CPUs/cores.
  • The optimizer is run-to-run deterministic.
  • Reads and writes industry standard formats such as the MPS, CBF and LP formats.
  • Includes tools for infeasibility diagnosis, repair and sensitivity analysis for linear problems.
  • Ships with an optimization server for remote optimization.

Interfaces

  • Optimizer API: C, Java, .NET, Python.
  • Fusion API: C++, Java, .NET, Python, MATLAB.
  • MOSEK optimization toolbox for MATLAB.
  • R package.
  • Command line interface.
  • Other third party commercial and open-source tools and products have interfaces to MOSEK.

Try our Anaconda Package!

If you are using MOSEK from Python, we recommend the Anaconda distribution and the MOSEK Anaconda package.