Mosek

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 437 articles )

Showing results 1 to 20 of 437.
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  1. Ahmadi, Amir Ali; El Khadir, Bachir: Time-varying semidefinite programs (2021)
  2. Ahmadi, Amir Ali; Zhang, Jeffrey: Semidefinite programming and Nash equilibria in bimatrix games (2021)
  3. Amir, Tal; Basri, Ronen; Nadler, Boaz: The trimmed lasso: sparse recovery guarantees and practical optimization by the generalized soft-min penalty (2021)
  4. Bramburger, Jason J.; Henderson, Christopher: The speed of traveling waves in a FKPP-Burgers system (2021)
  5. Breit, Dominic; Hofmanová, Martina; Loisel, Sébastien: Space-time approximation of stochastic (p)-Laplace-type systems (2021)
  6. Cavaleiro, Marta; Alizadeh, Farid: A dual simplex-type algorithm for the smallest enclosing ball of balls (2021)
  7. Dávid Papp, Sercan Yıldız: alfonso: Matlab package for nonsymmetric conic optimization (2021) arXiv
  8. Ding, Lijun; Udell, Madeleine: On the simplicity and conditioning of low rank semidefinite programs (2021)
  9. Ding, Lijun; Yurtsever, Alp; Cevher, Volkan; Tropp, Joel A.; Udell, Madeleine: An optimal-storage approach to semidefinite programming using approximate complementarity (2021)
  10. Fang, Kun; Fawzi, Hamza: Geometric Rényi divergence and its applications in quantum channel capacities (2021)
  11. Gaar, Elisabeth; Krenn, Daniel; Margulies, Susan; Wiegele, Angelika: Towards a computational proof of Vizing’s conjecture using semidefinite programming and sums-of-squares (2021)
  12. Garstka, Michael; Cannon, Mark; Goulart, Paul: COSMO: a conic operator splitting method for convex conic problems (2021)
  13. Guigues, Vincent: Multistage stochastic programs with a random number of stages: dynamic programming equations, solution methods, and application to portfolio selection (2021)
  14. Guigues, Vincent: Inexact stochastic mirror descent for two-stage nonlinear stochastic programs (2021)
  15. Guigues, Vincent; Juditsky, Anatoli; Nemirovski, Arkadi: Constant depth decision rules for multistage optimization under uncertainty (2021)
  16. Guigues, Vincent; Monteiro, Renato D. C.: Stochastic dynamic cutting plane for multistage stochastic convex programs (2021)
  17. Guigues, Vincent; Monteiro, Renato; Svaiter, Benar: Inexact cuts in stochastic dual dynamic programming applied to multistage stochastic nondifferentiable problems (2021)
  18. Haeser, Gabriel; Hinder, Oliver; Ye, Yinyu: On the behavior of Lagrange multipliers in convex and nonconvex infeasible interior point methods (2021)
  19. Hamedani, Erfan Yazdandoost; Aybat, Necdet Serhat: A primal-dual algorithm with line search for general convex-concave saddle point problems (2021)
  20. Hauenstein, Jonathan D.; Liddell, Alan C. jun.; McPherson, Sanesha; Zhang, Yi: Numerical algebraic geometry and semidefinite programming (2021)

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