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 349 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. Bonnard, Bernard; Cots, Olivier; Rouot, Jérémy; Verron, Thibaut: Time minimal saturation of a pair of spins and application in magnetic resonance imaging (2020)
  5. 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)
  6. Bruno, Hugo; Barros, Guilherme; Menezes, Ivan F. M.; Martha, Luiz Fernando: Return-mapping algorithms for associative isotropic hardening plasticity using conic optimization (2020)
  7. Cifuentes, Diego; Kahle, Thomas; Parrilo, Pablo: Sums of squares in Macaulay2 (2020)
  8. Couellan, Nicolas; Jan, Sophie: Feature uncertainty bounds for explicit feature maps and large robust nonlinear SVM classifiers (2020)
  9. Fischetti, Matteo; Monaci, Michele: A branch-and-cut algorithm for mixed-integer bilinear programming (2020)
  10. Guigues, Vincent: Inexact cuts in stochastic dual dynamic programming (2020)
  11. Hooshmandi, Kaveh; Bayat, Farhad; Jahedmotlagh, Mohamadreza; Jalali, Aliakbar: Guaranteed cost nonlinear sampled-data control: applications to a class of chaotic systems (2020)
  12. Kalogeropoulos, Ioannis; Sarimveis, Haralambos: Predictive control algorithms for congestion management in electric power distribution grids (2020)
  13. 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)
  14. Lesage-Landry, Antoine; Taylor, Joshua A.: A second-order cone model of transmission planning with alternating and direct current lines (2020)
  15. Marandi, Ahmadreza; de Klerk, Etienne; Dahl, Joachim: Solving sparse polynomial optimization problems with chordal structure using the sparse bounded-degree sum-of-squares hierarchy (2020)
  16. Melo, Wendel; Fampa, Marcia; Raupp, Fernanda: An overview of MINLP algorithms and their implementation in Muriqui optimizer (2020)
  17. Petsagkourakis, Panagiotis; Heath, William Paul; Theodoropoulos, Constantinos: Stability analysis of piecewise affine systems with multi-model predictive control (2020)
  18. Takapoui, Reza; Moehle, Nicholas; Boyd, Stephen; Bemporad, Alberto: A simple effective heuristic for embedded mixed-integer quadratic programming (2020)
  19. Tang, Chunming; Liu, Shuai; Jian, Jinbao; Ou, Xiaomei: A multi-step doubly stabilized bundle method for nonsmooth convex optimization (2020)
  20. Ahmadi, Amir Ali; de Klerk, Etienne; Hall, Georgina: Polynomial norms (2019)

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