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 255 articles )

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  1. Ahmadi, Amir Ali; Hall, Georgina: DC decomposition of nonconvex polynomials with algebraic techniques (2018)
  2. Armstrong, John; Pennanen, Teemu; Rakwongwan, Udomsak: Pricing index options by static hedging under finite liquidity (2018)
  3. Aßmann, Denis; Liers, Frauke; Stingl, Michael; Vera, Juan C.: Deciding robust feasibility and infeasibility using a set containment approach: an application to stationary passive gas network operations (2018)
  4. Bonafini, M.: Convex relaxation and variational approximation of the Steiner problem: theory and numerics (2018)
  5. El Karoui, Noureddine; Purdom, Elizabeth: Can we trust the bootstrap in high-dimensions? The case of linear models (2018)
  6. Filip, Silviu-Ioan; Nakatsukasa, Yuji; Trefethen, Lloyd N.; Beckermann, Bernhard: Rational minimax approximation via adaptive barycentric representations (2018)
  7. Goluskin, David: Bounding averages rigorously using semidefinite programming: mean moments of the Lorenz system (2018)
  8. Guigues, Vincent; Krätschmer, Volker; Shapiro, Alexander: A central limit theorem and hypotheses testing for risk-averse stochastic programs (2018)
  9. Lasserre, Jean-Bernard; Magron, Victor: Optimal data fitting: a moment approach (2018)
  10. Park, Jaehyun; Boyd, Stephen: A semidefinite programming method for integer convex quadratic minimization (2018)
  11. Polcz, Péter; Péni, Tamás; Szederkényi, Gábor: Improved algorithm for computing the domain of attraction of rational nonlinear systems (2018)
  12. Roux, Pierre; Voronin, Yuen-Lam; Sankaranarayanan, Sriram: Validating numerical semidefinite programming solvers for polynomial invariants (2018)
  13. Sotirov, Renata: Graph bisection revisited (2018)
  14. Wang, Guanglei; Hijazi, Hassan: Mathematical programming methods for microgrid design and operations: a survey on deterministic and stochastic approaches (2018)
  15. Weisser, Tillmann; Lasserre, Jean B.; Toh, Kim-Chuan: Sparse-BSOS: a bounded degree SOS hierarchy for large scale polynomial optimization with sparsity (2018)
  16. Xu, Guanglin; Burer, Samuel: A copositive approach for two-stage adjustable robust optimization with uncertain right-hand sides (2018)
  17. Xu, Guanglin; Burer, Samuel: A data-driven distributionally robust bound on the expected optimal value of uncertain mixed 0-1 linear programming (2018)
  18. Yamashita, Makoto; Mullin, Tim J.; Safarina, Sena: An efficient second-order cone programming approach for optimal selection in tree breeding (2018)
  19. Adjé, Assalé; Garoche, Pierre-Loïc; Magron, Victor: A sums-of-squares extension of policy iterations (2017)
  20. Ahmadi, Amir Ali; Hall, Georgina: Sum of squares basis pursuit with linear and second order cone programming (2017)

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