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 226 articles )

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  1. Polcz, Péter; Péni, Tamás; Szederkényi, Gábor: Improved algorithm for computing the domain of attraction of rational nonlinear systems (2018)
  2. Adjé, Assalé; Garoche, Pierre-Loïc; Magron, Victor: A sums-of-squares extension of policy iterations (2017)
  3. Ahmadi, Amir Ali; Hall, Georgina: Sum of squares basis pursuit with linear and second order cone programming (2017)
  4. Amir Ali Ahmadi, Anirudha Majumdar: DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization (2017) arXiv
  5. Anqi Fu, Balasubramanian Narasimhan, Stephen Boyd: CVXR: An R Package for Disciplined Convex Optimization (2017) arXiv
  6. Chen, Chen; Atamtürk, Alper; Oren, Shmuel S.: A spatial branch-and-cut method for nonconvex QCQP with bounded complex variables (2017)
  7. Chen, Yunmei; Lan, Guanghui; Ouyang, Yuyuan: Accelerated schemes for a class of variational inequalities (2017)
  8. D’Ambrosio, Claudia; Vu, Ky; Lavor, Carlile; Liberti, Leo; Maculan, Nelson: New error measures and methods for realizing protein graphs from distance data (2017)
  9. Dym, Nadav; Lipman, Yaron: Exact recovery with symmetries for procrustes matching (2017)
  10. Fawzi, Hamza; Saunderson, James: Lieb’s concavity theorem, matrix geometric means, and semidefinite optimization (2017)
  11. 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)
  12. Guigues, Vincent: Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures (2017)
  13. Guigues, Vincent: Dual dynamic programming with cut selection: convergence proof and numerical experiments (2017)
  14. Hallac, David; Wong, Christopher; Diamond, Steven; Sharang, Abhijit; Sosič, Rok; Boyd, Stephen; Leskovec, Jure: SnapVX: a network-based convex optimization solver (2017)
  15. Harrow, Aram W.; Natarajan, Anand; Wu, Xiaodi: An improved semidefinite programming hierarchy for testing entanglement (2017)
  16. Hirano, Masahiro; Watanabe, Yoshihiro; Ishikawa, Masatoshi: Rapid blending of closed curves based on curvature flow (2017)
  17. Hosoe, S.; Tuan, H.D.; Nguyen, T.N.: 2D bilinear programming for robust PID/DD controller design (2017)
  18. Huang, Kuo-Ling; Mehrotra, Sanjay: Solution of monotone complementarity and general convex programming problems using a modified potential reduction interior point method (2017)
  19. Ketabchi, Saeed; Moosaei, Hossein; Parandegan, M.; Navidi, Hamidreza: Computing minimum norm solution of linear systems of equations by the generalized Newton method (2017)
  20. Kočvara, Michal; Outrata, Jiří V.: Inverse truss design as a conic mathematical program with equilibrium constraints (2017)

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