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 238 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. Goluskin, David: Bounding averages rigorously using semidefinite programming: mean moments of the Lorenz system (2018)
  4. Guigues, Vincent; Krätschmer, Volker; Shapiro, Alexander: A central limit theorem and hypotheses testing for risk-averse stochastic programs (2018)
  5. Park, Jaehyun; Boyd, Stephen: A semidefinite programming method for integer convex quadratic minimization (2018)
  6. Polcz, Péter; Péni, Tamás; Szederkényi, Gábor: Improved algorithm for computing the domain of attraction of rational nonlinear systems (2018)
  7. Weisser, Tillmann; Lasserre, Jean B.; Toh, Kim-Chuan: Sparse-BSOS: a bounded degree SOS hierarchy for large scale polynomial optimization with sparsity (2018)
  8. Xu, Guanglin; Burer, Samuel: A copositive approach for two-stage adjustable robust optimization with uncertain right-hand sides (2018)
  9. Adjé, Assalé; Garoche, Pierre-Loïc; Magron, Victor: A sums-of-squares extension of policy iterations (2017)
  10. Ahmadi, Amir Ali; Hall, Georgina: Sum of squares basis pursuit with linear and second order cone programming (2017)
  11. Amir Ali Ahmadi, Anirudha Majumdar: DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization (2017) arXiv
  12. Anqi Fu, Balasubramanian Narasimhan, Stephen Boyd: CVXR: An R Package for Disciplined Convex Optimization (2017) arXiv
  13. Chen, Chen; Atamtürk, Alper; Oren, Shmuel S.: A spatial branch-and-cut method for nonconvex QCQP with bounded complex variables (2017)
  14. Chen, Yunmei; Lan, Guanghui; Ouyang, Yuyuan: Accelerated schemes for a class of variational inequalities (2017)
  15. D’Ambrosio, Claudia; Vu, Ky; Lavor, Carlile; Liberti, Leo; Maculan, Nelson: New error measures and methods for realizing protein graphs from distance data (2017)
  16. Dym, Nadav; Lipman, Yaron: Exact recovery with symmetries for procrustes matching (2017)
  17. Fawzi, Hamza; Saunderson, James: Lieb’s concavity theorem, matrix geometric means, and semidefinite optimization (2017)
  18. Frezzatto, Luciano; de Oliveira, Maurício; Oliveira, Ricardo C. L. F.; Peres, Pedro L. D.: Robust non-minimal order filter and smoother design for discrete-time uncertain systems (2017)
  19. Gerber, Samuel; Maggioni, Mauro: Multiscale strategies for computing optimal transport (2017)
  20. Guigues, Vincent: Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures (2017)

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