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 393 articles )

Showing results 41 to 60 of 393.
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  1. 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)
  2. Melo, Wendel; Fampa, Marcia; Raupp, Fernanda: An overview of MINLP algorithms and their implementation in Muriqui optimizer (2020)
  3. Petsagkourakis, Panagiotis; Heath, William Paul; Theodoropoulos, Constantinos: Stability analysis of piecewise affine systems with multi-model predictive control (2020)
  4. Polcz, Péter; Péni, Tamás; Kulcsár, Balázs; Szederkényi, Gábor: Induced (\mathcalL_2)-gain computation for rational LPV systems using Finsler’s lemma and minimal generators (2020)
  5. Rontsis, Nikitas; Osborne, Michael A.; Goulart, Paul J.: Distributionally ambiguous optimization for batch Bayesian optimization (2020)
  6. Ryu, Ernest K.; Taylor, Adrien B.; Bergeling, Carolina; Giselsson, Pontus: Operator splitting performance estimation: tight contraction factors and optimal parameter selection (2020)
  7. Saha, Sujayam; Guntuboyina, Adityanand: On the nonparametric maximum likelihood estimator for Gaussian location mixture densities with application to Gaussian denoising (2020)
  8. Smith, Stanley W.; Arcak, Murat; Zamani, Majid: Approximate abstractions of control systems with an application to aggregation (2020)
  9. Stellato, Bartolomeo; Banjac, Goran; Goulart, Paul; Bemporad, Alberto; Boyd, Stephen: OSQP: an operator splitting solver for quadratic programs (2020)
  10. Takapoui, Reza; Moehle, Nicholas; Boyd, Stephen; Bemporad, Alberto: A simple effective heuristic for embedded mixed-integer quadratic programming (2020)
  11. Tang, Chunming; Liu, Shuai; Jian, Jinbao; Ou, Xiaomei: A multi-step doubly stabilized bundle method for nonsmooth convex optimization (2020)
  12. Tran-Dinh, Quoc; Zhu, Yuzixuan: Non-stationary first-order primal-dual algorithms with faster convergence rates (2020)
  13. Wang, Jie; Magron, Victor: A second order cone characterization for sums of nonnegative circuits (2020)
  14. Yin, He; Packard, Andrew; Arcak, Murat; Seiler, Peter: Reachability analysis using dissipation inequalities for uncertain nonlinear systems (2020)
  15. Yue Jiang, Wolfgang Stuerzlinger, Matthias Zwicker, Christof Lutteroth: ORCSolver: An Efficient Solver for Adaptive GUI Layout with OR-Constraints (2020) arXiv
  16. Ahmadi, Amir Ali; de Klerk, Etienne; Hall, Georgina: Polynomial norms (2019)
  17. Ahmadi, Amir Ali; Majumdar, Anirudha: DSOS and SDSOS optimization: more tractable alternatives to sum of squares and semidefinite optimization (2019)
  18. Alzalg, Baha: A primal-dual interior-point method based on various selections of displacement step for symmetric optimization (2019)
  19. Asadi, Soodabeh; Mansouri, Hossein: A Mehrotra type predictor-corrector interior-point algorithm for linear programming (2019)
  20. Atamtürk, Alper; Gómez, Andrés: Simplex QP-based methods for minimizing a conic quadratic objective over polyhedra (2019)

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