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
- Conic quadratic.
- Conic with exponential and power cone.
- Semi-definite (Positive semi-definite matrix variables).
- Convex quadratic and quadratically constrained.
- Mixed integer linear, conic and quadratic.
- 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 continuous linear, quadratic and conic problems.
- Exploits hardware i.e. SSE2 instructions available in recent Intel CPUs.
- 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.
InterfacesSee supported platforms for details.
- Optimizer API: C, Java, .NET, Python.
- Fusion API: C++, Java, .NET, Python.
- MOSEK optimization toolbox for MATLAB.
- R package.
- Command line interface.
- Other third party commercial and open-source tools and products have interfaces to MOSEK.
- Supported platforms: OSX, Windows, Linux
- MOSEK can not solve nonconvex problems. Only convex problems including one or more integer constrained variables.
- MOSEK has no sequential quadratic optimizer because it is not competitive with the algorithms implemented in MOSEK.