MOSEK is a tool for solving mathematical optimization problems. Some examples of problems MOSEK can solve are linear programs, quadratic programs, conic problems and mixed integer problems. Such problems occurs frequently in Financial applications e.g. portfolio management, Supply chain management, Analog chip design, Forestry and farming, Medical and hospital management, Power supply and network planning, Logistics, TV commercial scheduling, Structural engineering. Due the strengths of the linear and conic optimizers in MOSEK, then MOSEK is currently employed widely in the financial industry. MOSEK has also been employed extensively in energy and forestry industry due to its powerful interior-point optimizer.

References in zbMATH (referenced in 371 articles )

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  1. Lippert, Ross A.: Discrete approximations to continuum optimal flow problems (2006)
  2. Makrodimopoulos, A.; Martin, C. M.: Lower bound limit analysis of cohesive-frictional materials using second-order cone programming (2006)
  3. Trillat, Malorie; Pastor, Joseph; Thoré, Philippe: Limit analysis and conic programming: `porous Drucker-Prager’ material and Gurson’s model (2006)
  4. Watson, G. Alistair: Fitting enclosing cylinders to data in (\mathbbR^n) (2006)
  5. Berkelaar, Arjan; Gromicho, Joaquim A. S.; Kouwenberg, Roy; Zhang, Shuzhong: A primal-dual decomposition algorithm for multistage stochastic convex programming (2005)
  6. Evtushenko, Yu. G.; Golikov, A. I.; Mollaverdy, N.: Augmented Lagrangian method for large-scale linear programming problems (2005)
  7. Fourer, Robert; Lopes, Leo; Martin, Kipp: LPFML: A W3C XML schema for linear and integer programming (2005)
  8. Gould, Nick; Orban, Dominique; Toint, Philippe: Numerical methods for large-scale nonlinear optimization (2005)
  9. Pluymers, B.; Roobrouck, L.; Buijs, J.; Suykens, J. A. K.; De Moor, B.: Constrained linear MPC with time-varying terminal cost using convex combinations (2005)
  10. Stanimirović, Predrag S.; Stojković, Nebojša V.; Kovačević-Vujčić, Vera V.: Stabilization of Mehrotra’s primal-dual algorithm and its implementation (2005)
  11. Trillat, Malorie; Pastor, Joseph: Limit analysis and Gurson’s model (2005)
  12. Lanckriet, Gert R. G.; Cristianini, Nello; Bartlett, Peter; El Ghaoui, Laurent; Jordan, Michael I.: Learning the kernel matrix with semidefinite programming (2004)
  13. Andersen, E. D.; Roos, C.; Terlaky, T.: On implementing a primal-dual interior-point method for conic quadratic optimization (2003)
  14. Gondzio, Jacek; Grothey, Andreas: Reoptimization with the primal-dual interior point method (2003)
  15. Kullmann, Oliver: Lean clause-sets: Generalizations of minimally unsatisfiable clause-sets (2003)
  16. Lanckriet, Gert R. G.; El Ghaoui, Laurent; Bhattacharyya, Chiranjib; Jordan, Michael I.: A robust minimax approach to classification (2003)
  17. Berkelaar, Arjan; Dert, Cees; Oldenkamp, Bart; Zhang, Shuzhong: A primal-dual decomposition-based interior point approach to two-stage stochastic linear programming (2002)
  18. Burke, James V.; Lewis, Adrian S.; Overton, Michael L.: Two numerical methods for optimizing matrix stability (2002)
  19. De Klerk, Etienne: Aspects of semidefinite programming. Interior point algorithms and selected applications (2002)
  20. Illés, Tibor; Terlaky, Tamás: Pivot versus interior point methods: Pros and cons (2002)

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