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 393 articles )

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  1. Coey, Chris; Lubin, Miles; Vielma, Juan Pablo: Outer approximation with conic certificates for mixed-integer convex problems (2020)
  2. Colbert, Brendon K.; Peet, Matthew M.: A convex parametrization of a new class of universal kernel functions (2020)
  3. Couellan, Nicolas; Jan, Sophie: Feature uncertainty bounds for explicit feature maps and large robust nonlinear SVM classifiers (2020)
  4. Dean, Sarah; Mania, Horia; Matni, Nikolai; Recht, Benjamin; Tu, Stephen: On the sample complexity of the linear quadratic regulator (2020)
  5. Degue, Kwassi H.; Le Ny, Jerome: Estimation and outbreak detection with interval observers for uncertain discrete-time SEIR epidemic models (2020)
  6. Drori, Yoel; Taylor, Adrien B.: Efficient first-order methods for convex minimization: a constructive approach (2020)
  7. El Khadir, Bachir: On sum of squares representation of convex forms and generalized Cauchy-Schwarz inequalities (2020)
  8. Eltved, Anders; Dahl, Joachim; Andersen, Martin S.: On the robustness and scalability of semidefinite relaxation for optimal power flow problems (2020)
  9. Filová, Lenka; Harman, Radoslav: Ascent with quadratic assistance for the construction of exact experimental designs (2020)
  10. Fischetti, Matteo; Monaci, Michele: A branch-and-cut algorithm for mixed-integer bilinear programming (2020)
  11. Gaar, Elisabeth; Rendl, Franz: A computational study of exact subgraph based SDP bounds for max-cut, stable set and coloring (2020)
  12. Guigues, Vincent: Inexact cuts in stochastic dual dynamic programming (2020)
  13. Guigues, Vincent; Juditsky, Anatoli; Nemirovski, Arkadi: Hypothesis testing via Euclidean separation (2020)
  14. Guigues, Vincent; Lejeune, Migual A.; Tekaya, Wajdi: Regularized stochastic dual dynamic programming for convex nonlinear optimization problems (2020)
  15. Hooshmandi, Kaveh; Bayat, Farhad; Jahedmotlagh, Mohamadreza; Jalali, Aliakbar: Guaranteed cost nonlinear sampled-data control: applications to a class of chaotic systems (2020)
  16. Hu, Hao; Sotirov, Renata: On solving the quadratic shortest path problem (2020)
  17. Jiao, Chunxi; Kawai, Reiichiro: Computable primal and dual bounds for stochastic control (2020)
  18. Kalogeropoulos, Ioannis; Sarimveis, Haralambos: Predictive control algorithms for congestion management in electric power distribution grids (2020)
  19. Lakshmi, Mayur V.; Fantuzzi, Giovanni; Fernández-Caballero, Jesús D.; Hwang, Yongyun; Chernyshenko, Sergei I.: Finding extremal periodic orbits with polynomial optimization, with application to a nine-mode model of shear flow (2020)
  20. Lesage-Landry, Antoine; Taylor, Joshua A.: A second-order cone model of transmission planning with alternating and direct current lines (2020)

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