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 353 articles )

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  1. Goluskin, David; Fantuzzi, Giovanni: Bounds on mean energy in the Kuramoto-Sivashinsky equation computed using semidefinite programming (2019)
  2. González, Temoatzin; Sala, Antonio; Bernal, Miguel: A generalised integral polynomial Lyapunov function for nonlinear systems (2019)
  3. Gribling, Sander; de Laat, David; Laurent, Monique: Lower bounds on matrix factorization ranks via noncommutative polynomial optimization (2019)
  4. Gu, Jiaying; Volgushev, Stanislav: Panel data quantile regression with grouped fixed effects (2019)
  5. Holicki, Tobias; Scherer, Carsten W.: Stability analysis and output-feedback synthesis of hybrid systems affected by piecewise constant parameters via dynamic resetting scalings (2019)
  6. Ito, Naoki; Kim, Sunyoung; Kojima, Masakazu; Takeda, Akiko; Toh, Kim-Chuan: Algorithm 996: BBCPOP: a sparse doubly nonnegative relaxation of polynomial optimization problems with binary, box, and complementarity constraints (2019)
  7. Jiang, Rujun; Li, Duan: Novel reformulations and efficient algorithms for the generalized trust region subproblem (2019)
  8. Kimizuka, Masaki; Kim, Sunyoung; Yamashita, Makoto: Solving pooling problems with time discretization by LP and SOCP relaxations and rescheduling methods (2019)
  9. Koenker, Roger; Gu, Jiaying: Comment: Minimalist (g)-modeling (2019)
  10. Kuang, Xiaolong; Ghaddar, Bissan; Naoum-Sawaya, Joe; Zuluaga, Luis F.: Alternative SDP and SOCP approximations for polynomial optimization (2019)
  11. Lasserre, Jean-Bernard; Magron, Victor: In SDP relaxations, inaccurate solvers do robust optimization (2019)
  12. Liao, Lina; Park, Cheolwoo; Choi, Hosik: Penalized expectile regression: an alternative to penalized quantile regression (2019)
  13. Li, M.; Füssl, J.; Lukacevic, M.; Eberhardsteiner, J.; Martin, C. M.: An algorithm for adaptive introduction and arrangement of velocity discontinuities within 3D finite-element-based upper bound limit analysis approaches (2019)
  14. Liu, Yanli; Ryu, Ernest K.; Yin, Wotao: A new use of Douglas-Rachford splitting for identifying infeasible, unbounded, and pathological conic programs (2019)
  15. Magron, Victor; Garoche, Pierre-Loic; Henrion, Didier; Thirioux, Xavier: Semidefinite approximations of reachable sets for discrete-time polynomial systems (2019)
  16. Mohapatra, Debasis; Kumar, Jyant: Smoothed finite element approach for kinematic limit analysis of cohesive frictional materials (2019)
  17. Nugroho, Sebastian A.; Taha, Ahmad F.; Gatsis, Nikolaos; Summers, Tyler H.; Krishnan, Ram: Algorithms for joint sensor and control nodes selection in dynamic networks (2019)
  18. Polcz, Péter; Péni, Tamás; Szederkényi, Gábor: Computational method for estimating the domain of attraction of discrete-time uncertain rational systems (2019)
  19. Tang, Chunming; Jian, Jinbao; Li, Guoyin: A proximal-projection partial bundle method for convex constrained minimax problems (2019)
  20. Tao, Jiong; Deng, Bailin; Zhang, Juyong: A fast numerical solver for local barycentric coordinates (2019)

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