High performance software for large-scale LP, QP, SOCP, SDP and MIP including interfaces to C, Java, MATLAB, .NET, R and Python. 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
Semidefinite (Positive semidefinite 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.
Interior-point optimizer with basis identification.
Primal and dual simplex optimizers for linear programming.
Highly efficient presolver for reducing problem size before optimization.
Network-simplex for problems with network flow structure.
Branch&bound&cut algorithm for mixed integer problems.

Strengths of MOSEK

The strong point of MOSEK is its state-of-the-art interior-point optimizer for continous linear, quadratic and conic problems.
Parallel capabilities

The interior-point optimizer is capable of exploiting multiple CPUs/cores. A concurrent optimizer is available that makes it possible to solve one problem with different optimizers simultaneously. The default mixed integer optimizer is parallelized and is run-to-run deterministic.
Other features of MOSEK

Reads and writes industry standard formats such as the MPS and LP formats. Includes tools for infeasibility diagnosis and repair. Sensitivity analysis for linear problems.
Deployment

The MOSEK optimizer includes several interfaces.

Developer APIs

An API provides homogeneous full access to all solvers from several programming languages:
Optimizer API : the most efficient, low-level, matrix oriented interface to the optimizer.
Fusion API: an object orientated API that allows to express an optimization models very closely to the way it is formulated.

Only for V.I.P