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 245 articles )

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  1. Ahmadi, Amir Ali; Hall, Georgina: DC decomposition of nonconvex polynomials with algebraic techniques (2018)
  2. Bonafini, M.: Convex relaxation and variational approximation of the Steiner problem: theory and numerics (2018)
  3. Filip, Silviu-Ioan; Nakatsukasa, Yuji; Trefethen, Lloyd N.; Beckermann, Bernhard: Rational minimax approximation via adaptive barycentric representations (2018)
  4. Goluskin, David: Bounding averages rigorously using semidefinite programming: mean moments of the Lorenz system (2018)
  5. Guigues, Vincent; Krätschmer, Volker; Shapiro, Alexander: A central limit theorem and hypotheses testing for risk-averse stochastic programs (2018)
  6. Park, Jaehyun; Boyd, Stephen: A semidefinite programming method for integer convex quadratic minimization (2018)
  7. Polcz, Péter; Péni, Tamás; Szederkényi, Gábor: Improved algorithm for computing the domain of attraction of rational nonlinear systems (2018)
  8. Sotirov, Renata: Graph bisection revisited (2018)
  9. Weisser, Tillmann; Lasserre, Jean B.; Toh, Kim-Chuan: Sparse-BSOS: a bounded degree SOS hierarchy for large scale polynomial optimization with sparsity (2018)
  10. Xu, Guanglin; Burer, Samuel: A copositive approach for two-stage adjustable robust optimization with uncertain right-hand sides (2018)
  11. Adjé, Assalé; Garoche, Pierre-Loïc; Magron, Victor: A sums-of-squares extension of policy iterations (2017)
  12. Ahmadi, Amir Ali; Hall, Georgina: Sum of squares basis pursuit with linear and second order cone programming (2017)
  13. Amir Ali Ahmadi, Anirudha Majumdar: DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization (2017) arXiv
  14. Anqi Fu, Balasubramanian Narasimhan, Stephen Boyd: CVXR: An R Package for Disciplined Convex Optimization (2017) arXiv
  15. Chen, Chen; Atamtürk, Alper; Oren, Shmuel S.: A spatial branch-and-cut method for nonconvex QCQP with bounded complex variables (2017)
  16. Chen, Yunmei; Lan, Guanghui; Ouyang, Yuyuan: Accelerated schemes for a class of variational inequalities (2017)
  17. Couellan, Nicolas; Wang, Wenjuan: Uncertainty-safe large scale support vector machines (2017)
  18. D’Ambrosio, Claudia; Vu, Ky; Lavor, Carlile; Liberti, Leo; Maculan, Nelson: New error measures and methods for realizing protein graphs from distance data (2017)
  19. Do, Hien V.; Nguyen-Xuan, H.: Limit and shakedown isogeometric analysis of structures based on Bézier extraction (2017)
  20. Dym, Nadav; Lipman, Yaron: Exact recovery with symmetries for procrustes matching (2017)

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