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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.〔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〕 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,〔http://docs.mosek.com/7.0/toolbox/〕 R,〔https://r-forge.r-project.org/projects/rmosek/ Rmosek〕 CVX, and YALMIP.〔(MOSEK @ Yalmip homepage )〕 ==References== 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「MOSEK」の詳細全文を読む スポンサード リンク
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