MOSEK

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.^[1][2][3]

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: AIMMS, AMPL, and GAMS. MOSEK can also be used from popular tools such as matlab,^[4] R,^[5] CVX, and YALMIP.^[6]

References

External links

• Mosek licensing options
  • 30-days trial license
  • Academic license