MOSEK is a software package for the solution of linear, mixed-integer linear, quadratic, mixed-integer quadratic, quadratically constraint, conic and convex nonlinear mathematical optimization problems. The emphasis in MOSEK is on solving large scale sparse problems. Particularly the interior-point optimizer for linear, conic quadratic (aka. Second-order cone programming) and semi-definite (aka. semidefinite programming) problems is very efficient. A special feature of the MOSEK interior-point optimizer is that it is based on the so-called homogeneous model which implies MOSEK can reliably detect a primal and/or dual infeasible status as documented in several published papers.
In addition to the interior-point optimizer MOSEK includes:
- Primal and dual simplex optimizer for linear problems.
- A primal network simplex optimizer for problems with special network structure.
- Mixed-integer optimizer for linear, quadratic and conic quadratic problems.
MOSEK provides interfaces to the C, C#, Java and Python languages. Most major modeling systems are made compatible for MOSEK, examples are: AMPL, and GAMS. MOSEK can also be used from popular tools such as matlab, R, CVX, and YALMIP.
- E. D. Andersen and Y. Ye. A computational study of the homogeneous algorithm for large-scale convex optimization. Computational Optimization and Applications, 10:243–269, 1998
- E. D. Andersen and K. D. Andersen. The MOSEK interior point optimizer for linear programming: an implementation of the homogeneous algorithm.In H. Frenk, K. Roos, T. Terlaky, and S. Zhang, editors, High Performance Optimization, pages 197–232. Kluwer Academic Publishers, 2000
- E. D. Andersen, C. Roos, and T. Terlaky. On implementing a primal-dual interior-point method for conic quadratic optimization. Math. Programming, 95(2), February 2003
- https://r-forge.r-project.org/projects/rmosek/ Rmosek
- MOSEK @ Yalmip homepage
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