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 310 articles )

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  1. Fischetti, Matteo; Monaci, Michele: A branch-and-cut algorithm for mixed-integer bilinear programming (2020)
  2. Lesage-Landry, Antoine; Taylor, Joshua A.: A second-order cone model of transmission planning with alternating and direct current lines (2020)
  3. Ahmadi, Amir Ali; de Klerk, Etienne; Hall, Georgina: Polynomial norms (2019)
  4. Ahmadi, Amir Ali; Majumdar, Anirudha: DSOS and SDSOS optimization: more tractable alternatives to sum of squares and semidefinite optimization (2019)
  5. Alzalg, Baha: A primal-dual interior-point method based on various selections of displacement step for symmetric optimization (2019)
  6. Asadi, Soodabeh; Mansouri, Hossein: A Mehrotra type predictor-corrector interior-point algorithm for linear programming (2019)
  7. Atamtürk, Alper; Gómez, Andrés: Simplex QP-based methods for minimizing a conic quadratic objective over polyhedra (2019)
  8. Chen, Yunmei; Lan, Guanghui; Ouyang, Yuyuan; Zhang, Wei: Fast bundle-level methods for unconstrained and ball-constrained convex optimization (2019)
  9. Cui, Yiran; Morikuni, Keiichi; Tsuchiya, Takashi; Hayami, Ken: Implementation of interior-point methods for LP based on Krylov subspace iterative solvers with inner-iteration preconditioning (2019)
  10. Deford, Daryl; Lavenant, Hugo; Schutzman, Zachary; Solomon, Justin: Total variation isoperimetric profiles (2019)
  11. Dickinson, Peter J. C.; Povh, Janez: A new approximation hierarchy for polynomial conic optimization (2019)
  12. Eisenach, Carson; Liu, Han: Efficient, certifiably optimal clustering with applications to latent variable graphical models (2019)
  13. Fawzi, Hamza; Saunderson, James; Parrilo, Pablo A.: Semidefinite approximations of the matrix logarithm (2019)
  14. Faybusovich, Leonid; Zhou, Cunlu: Long-step path-following algorithm for solving symmetric programming problems with nonlinear objective functions (2019)
  15. Frezzatto, Luciano; Lacerda, Márcio J.; Oliveira, Ricardo C. L. F.; Peres, Pedro L. D.: (\mathcalH_2) and (\mathcalH_\infty) fuzzy filters with memory for Takagi-Sugeno discrete-time systems (2019)
  16. Furini, Fabio; Traversi, Emiliano; Belotti, Pietro; Frangioni, Antonio; Gleixner, Ambros; Gould, Nick; Liberti, Leo; Lodi, Andrea; Misener, Ruth; Mittelmann, Hans; Sahinidis, Nikolaos V.; Vigerske, Stefan; Wiegele, Angelika: QPLIB: a library of quadratic programming instances (2019)
  17. Ghaddar, Bissan; Jabr, Rabih A.: Power transmission network expansion planning: a semidefinite programming branch-and-bound approach (2019)
  18. Goluskin, David; Fantuzzi, Giovanni: Bounds on mean energy in the Kuramoto-Sivashinsky equation computed using semidefinite programming (2019)
  19. González, Temoatzin; Sala, Antonio; Bernal, Miguel: A generalised integral polynomial Lyapunov function for nonlinear systems (2019)
  20. Gribling, Sander; de Laat, David; Laurent, Monique: Lower bounds on matrix factorization ranks via noncommutative polynomial optimization (2019)

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