CVX

CVX is a modeling system for constructing and solving disciplined convex programs (DCPs). CVX supports a number of standard problem types, including linear and quadratic programs (LPs/QPs), second-order cone programs (SOCPs), and semidefinite programs (SDPs). CVX can also solve much more complex convex optimization problems, including many involving nondifferentiable functions, such as ℓ1 norms. You can use CVX to conveniently formulate and solve constrained norm minimization, entropy maximization, determinant maximization, and many other convex programs. As of version 2.0, CVX also solves mixed integer disciplined convex programs (MIDCPs) as well, with an appropriate integer-capable solver.


References in zbMATH (referenced in 437 articles , 1 standard article )

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  1. Ankenman, Jerrod; Leeb, William: Mixed Hölder matrix discovery via wavelet shrinkage and Calderón-Zygmund decompositions (2018)
  2. Arunachalam, Srinivasan; Molina, Abel; Russo, Vincent: Quantum hedging in two-round prover-verifier interactions (2018)
  3. Bakolas, E.: Constrained minimum variance control for discrete-time stochastic linear systems (2018)
  4. Bakolas, Efstathios: Finite-horizon covariance control for discrete-time stochastic linear systems subject to input constraints (2018)
  5. Bayraktar, Erhan; Wang, Gu: Quantile hedging in a semi-static market with model uncertainty (2018)
  6. Campi, Marco C.; Garatti, Simone: Wait-and-judge scenario optimization (2018)
  7. Candogan, Utkan Onur; Chandrasekaran, Venkat: Finding planted subgraphs with few eigenvalues using the Schur-Horn relaxation (2018)
  8. Cao, Lei; Woerdeman, Hugo J.: Real zero polynomials and A. Horn’s problem (2018)
  9. Cheng, Jianqiang; Li-Yang Chen, Richard; Najm, Habib N.; Pinar, Ali; Safta, Cosmin; Watson, Jean-Paul: Distributionally robust optimization with principal component analysis (2018)
  10. Chieu, N. H.; Feng, J. W.; Gao, W.; Li, G.; Wu, D.: SOS-convex semialgebraic programs and its applications to robust optimization: a tractable class of nonsmooth convex optimization (2018)
  11. Deng, Zhibin; Fang, Shu-Cherng; Lu, Cheng; Guo, Xiaoling: A branch-and-cut algorithm using polar cuts for solving nonconvex quadratic programming problems (2018)
  12. Duarte, Belmiro P. M.; Sagnol, Guillaume; Wong, Weng Kee: An algorithm based on semidefinite programming for finding minimax optimal designs (2018)
  13. Duarte, Belmiro P. M.; Wong, Weng Kee; Dette, Holger: Adaptive grid semidefinite programming for finding optimal designs (2018)
  14. Ferragut, Andres; Rodriguez, Ismael; Paganini, Fernando: Optimal timer-based caching policies for general arrival processes (2018)
  15. Filip, Silviu-Ioan; Nakatsukasa, Yuji; Trefethen, Lloyd N.; Beckermann, Bernhard: Rational minimax approximation via adaptive barycentric representations (2018)
  16. Flinth, Axel; Kutyniok, Gitta: PROMP: A sparse recovery approach to lattice-valued signals (2018)
  17. Foucart, Simon; Lasserre, Jean B.: Determining projection constants of univariate polynomial spaces (2018)
  18. Gillis, Nicolas; Sharma, Punit: A semi-analytical approach for the positive semidefinite Procrustes problem (2018)
  19. Halická, Margaréta; Trnovská, Mária: The Russell measure model: computational aspects, duality, and profit efficiency (2018)
  20. Hegde, Arun; Li, Wenyu; Oreluk, James; Packard, Andrew; Frenklach, Michael: Consistency analysis for massively inconsistent datasets in bound-to-bound data collaboration (2018)

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