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 392 articles )

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  1. Dávid Papp, Sercan Yıldız: alfonso: Matlab package for nonsymmetric conic optimization (2021) arXiv
  2. Haeser, Gabriel; Hinder, Oliver; Ye, Yinyu: On the behavior of Lagrange multipliers in convex and nonconvex infeasible interior point methods (2021)
  3. Henrion, Didier; Naldi, Simone; Safey El Din, Mohab: Exact algorithms for semidefinite programs with degenerate feasible set (2021)
  4. Lin-Shi, Xuefang; Massioni, Paolo; Gauthier, Jean-Yves: Estimation of inverter voltage disturbances for induction machine drive using LPV observer with convex optimization (2021)
  5. Lipták, György; Hangos, Katalin M.; Szederkényi, Gábor: Stabilizing feedback design for time delayed polynomial systems using kinetic realizations (2021)
  6. Nakatsukasa, Yuji; Townsend, Alex: Error localization of best (L_1) polynomial approximants (2021)
  7. Polyak, B. T.; Khlebnikov, M. V.; Shcherbakov, P. S.: Linear matrix inequalities in control systems with uncertainty (2021)
  8. Wang, Jie; Magron, Victor; Lasserre, Jean-Bernard: TSSOS: a moment-SOS hierarchy that exploits term sparsity (2021)
  9. Wang, Jie; Magron, Victor; Lasserre, Jean-Bernard: Chordal-TSSOS: a moment-SOS hierarchy that exploits term sparsity with chordal extension (2021)
  10. Adriaens, Florian; De Bie, Tijl; Gionis, Aristides; Lijffijt, Jefrey; Matakos, Antonis; Rozenshtein, Polina: Relaxing the strong triadic closure problem for edge strength inference (2020)
  11. Alzalg, Baha: A logarithmic barrier interior-point method based on majorant functions for second-order cone programming (2020)
  12. Ariola, Marco; De Tommasi, Gianmaria; Mele, Adriano; Tartaglione, Gaetano: On the numerical solution of differential linear matrix inequalities (2020)
  13. Ben Hermans, Andreas Themelis, Panagiotis Patrinos: QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs (2020) arXiv
  14. Bolte, Jérôme; Chen, Zheng; Pauwels, Edouard: The multiproximal linearization method for convex composite problems (2020)
  15. Bonafini, Mauro; Oudet, Édouard: A convex approach to the Gilbert-Steiner problem (2020)
  16. Bonnard, Bernard; Cots, Olivier; Rouot, Jérémy; Verron, Thibaut: Time minimal saturation of a pair of spins and application in magnetic resonance imaging (2020)
  17. Böttcher, Ulrich; Wirth, Benedikt: Convex lifting-type methods for curvature regularization (2020)
  18. 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)
  19. Bruno, Hugo; Barros, Guilherme; Menezes, Ivan F. M.; Martha, Luiz Fernando: Return-mapping algorithms for associative isotropic hardening plasticity using conic optimization (2020)
  20. Cifuentes, Diego; Kahle, Thomas; Parrilo, Pablo: Sums of squares in Macaulay2 (2020)

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