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 371 articles )

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  1. Adriaens, Florian; De Bie, Tijl; Gionis, Aristides; Lijffijt, Jefrey; Matakos, Antonis; Rozenshtein, Polina: Relaxing the strong triadic closure problem for edge strength inference (2020)
  2. Alzalg, Baha: A logarithmic barrier interior-point method based on majorant functions for second-order cone programming (2020)
  3. Ariola, Marco; De Tommasi, Gianmaria; Mele, Adriano; Tartaglione, Gaetano: On the numerical solution of differential linear matrix inequalities (2020)
  4. Ben Hermans, Andreas Themelis, Panagiotis Patrinos: QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs (2020) arXiv
  5. Bolte, Jérôme; Chen, Zheng; Pauwels, Edouard: The multiproximal linearization method for convex composite problems (2020)
  6. Bonafini, Mauro; Oudet, Édouard: A convex approach to the Gilbert-Steiner problem (2020)
  7. Bonnard, Bernard; Cots, Olivier; Rouot, Jérémy; Verron, Thibaut: Time minimal saturation of a pair of spins and application in magnetic resonance imaging (2020)
  8. Böttcher, Ulrich; Wirth, Benedikt: Convex lifting-type methods for curvature regularization (2020)
  9. Brändle, Stefanie; Schmitt, Syn; Müller, Matthias A.: A systems-theoretic analysis of low-level human motor control: application to a single-joint arm model (2020)
  10. Bruno, Hugo; Barros, Guilherme; Menezes, Ivan F. M.; Martha, Luiz Fernando: Return-mapping algorithms for associative isotropic hardening plasticity using conic optimization (2020)
  11. Cifuentes, Diego; Kahle, Thomas; Parrilo, Pablo: Sums of squares in Macaulay2 (2020)
  12. Coey, Chris; Lubin, Miles; Vielma, Juan Pablo: Outer approximation with conic certificates for mixed-integer convex problems (2020)
  13. Colbert, Brendon K.; Peet, Matthew M.: A convex parametrization of a new class of universal kernel functions (2020)
  14. Couellan, Nicolas; Jan, Sophie: Feature uncertainty bounds for explicit feature maps and large robust nonlinear SVM classifiers (2020)
  15. Dean, Sarah; Mania, Horia; Matni, Nikolai; Recht, Benjamin; Tu, Stephen: On the sample complexity of the linear quadratic regulator (2020)
  16. Drori, Yoel; Taylor, Adrien B.: Efficient first-order methods for convex minimization: a constructive approach (2020)
  17. El Khadir, Bachir: On sum of squares representation of convex forms and generalized Cauchy-Schwarz inequalities (2020)
  18. Eltved, Anders; Dahl, Joachim; Andersen, Martin S.: On the robustness and scalability of semidefinite relaxation for optimal power flow problems (2020)
  19. Filová, Lenka; Harman, Radoslav: Ascent with quadratic assistance for the construction of exact experimental designs (2020)
  20. Fischetti, Matteo; Monaci, Michele: A branch-and-cut algorithm for mixed-integer bilinear programming (2020)

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