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 189 articles )

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  1. Ahmadi, Amir Ali; Hall, Georgina: Sum of squares basis pursuit with linear and second order cone programming (2017)
  2. Amir Ali Ahmadi, Anirudha Majumdar: DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization (2017) arXiv
  3. D’Ambrosio, Claudia; Vu, Ky; Lavor, Carlile; Liberti, Leo; Maculan, Nelson: New error measures and methods for realizing protein graphs from distance data (2017)
  4. Dym, Nadav; Lipman, Yaron: Exact recovery with symmetries for procrustes matching (2017)
  5. Fawzi, Hamza; Saunderson, James: Lieb’s concavity theorem, matrix geometric means, and semidefinite optimization (2017)
  6. Guigues, Vincent: Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures (2017)
  7. Harrow, Aram W.; Natarajan, Anand; Wu, Xiaodi: An improved semidefinite programming hierarchy for testing entanglement (2017)
  8. Huang, Kuo-Ling; Mehrotra, Sanjay: Solution of monotone complementarity and general convex programming problems using a modified potential reduction interior point method (2017)
  9. Ketabchi, Saeed; Moosaei, Hossein; Parandegan, M.; Navidi, Hamidreza: Computing minimum norm solution of linear systems of equations by the generalized Newton method (2017)
  10. Korda, Milan; Jones, Colin N.: Stability and performance verification of optimization-based controllers (2017)
  11. Liu, Meijiao; Liu, Yong-Jin: Fast algorithm for singly linearly constrained quadratic programs with box-like constraints (2017)
  12. Mohammad-Nezhad, Ali; Terlaky, Tamás: A polynomial primal-dual affine scaling algorithm for symmetric conic optimization (2017)
  13. Permenter, Frank; Friberg, Henrik A.; Andersen, Erling D.: Solving conic optimization problems via self-dual embedding and facial reduction: A unified approach (2017)
  14. Simon, K.; Sheorey, S.; Jacobs, D.W.; Basri, R.: A hyperelastic two-scale optimization model for shape matching (2017)
  15. Taylor, Adrien B.; Hendrickx, Julien M.; Glineur, François: Exact worst-case performance of first-order methods for composite convex optimization (2017)
  16. Taylor, Adrien B.; Hendrickx, Julien M.; Glineur, François: Smooth strongly convex interpolation and exact worst-case performance of first-order methods (2017)
  17. Adasme, Pablo; Lisser, Abdel: Uplink scheduling for joint wireless orthogonal frequency and time division multiple access networks (2016)
  18. Ahmadi, Amir Ali; Majumdar, Anirudha: Some applications of polynomial optimization in operations research and real-time decision making (2016)
  19. Asadi, Alireza; Roos, Cornelis: Infeasible interior-point methods for linear optimization based on large neighborhood (2016)
  20. Aswani, Anil: Low-rank approximation and completion of positive tensors (2016)

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