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 318 articles )

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  1. 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)
  2. Couellan, Nicolas; Jan, Sophie: Feature uncertainty bounds for explicit feature maps and large robust nonlinear SVM classifiers (2020)
  3. Fischetti, Matteo; Monaci, Michele: A branch-and-cut algorithm for mixed-integer bilinear programming (2020)
  4. Guigues, Vincent: Inexact cuts in stochastic dual dynamic programming (2020)
  5. Lesage-Landry, Antoine; Taylor, Joshua A.: A second-order cone model of transmission planning with alternating and direct current lines (2020)
  6. 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)
  7. Petsagkourakis, Panagiotis; Heath, William Paul; Theodoropoulos, Constantinos: Stability analysis of piecewise affine systems with multi-model predictive control (2020)
  8. Takapoui, Reza; Moehle, Nicholas; Boyd, Stephen; Bemporad, Alberto: A simple effective heuristic for embedded mixed-integer quadratic programming (2020)
  9. Ahmadi, Amir Ali; de Klerk, Etienne; Hall, Georgina: Polynomial norms (2019)
  10. Ahmadi, Amir Ali; Majumdar, Anirudha: DSOS and SDSOS optimization: more tractable alternatives to sum of squares and semidefinite optimization (2019)
  11. Alzalg, Baha: A primal-dual interior-point method based on various selections of displacement step for symmetric optimization (2019)
  12. Asadi, Soodabeh; Mansouri, Hossein: A Mehrotra type predictor-corrector interior-point algorithm for linear programming (2019)
  13. Atamtürk, Alper; Gómez, Andrés: Simplex QP-based methods for minimizing a conic quadratic objective over polyhedra (2019)
  14. Buchheim, Christoph; De Santis, Marianna: An active set algorithm for robust combinatorial optimization based on separation oracles (2019)
  15. Chen, Yunmei; Lan, Guanghui; Ouyang, Yuyuan; Zhang, Wei: Fast bundle-level methods for unconstrained and ball-constrained convex optimization (2019)
  16. Cui, Yiran; Morikuni, Keiichi; Tsuchiya, Takashi; Hayami, Ken: Implementation of interior-point methods for LP based on Krylov subspace iterative solvers with inner-iteration preconditioning (2019)
  17. Deford, Daryl; Lavenant, Hugo; Schutzman, Zachary; Solomon, Justin: Total variation isoperimetric profiles (2019)
  18. Dickinson, Peter J. C.; Povh, Janez: A new approximation hierarchy for polynomial conic optimization (2019)
  19. Eisenach, Carson; Liu, Han: Efficient, certifiably optimal clustering with applications to latent variable graphical models (2019)
  20. Fawzi, Hamza; Saunderson, James; Parrilo, Pablo A.: Semidefinite approximations of the matrix logarithm (2019)

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