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 269 articles , 1 standard article )

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  1. Chen, Bilian; He, Simai; Li, Zhening; Zhang, Shuzhong: On new classes of nonnegative symmetric tensors (2017)
  2. Chuong, T.D.; Jeyakumar, V.: A generalized Farkas lemma with a numerical certificate and linear semi-infinite programs with SDP duals (2017)
  3. Fawzi, Hamza; Saunderson, James: Lieb’s concavity theorem, matrix geometric means, and semidefinite optimization (2017)
  4. Gao, Lucy L.; Zhou, Julie: D-optimal designs based on the second-order least squares estimator (2017)
  5. Gotoh, Jun-ya; Uryasev, Stan: Support vector machines based on convex risk functions and general norms (2017)
  6. Iwen, Mark; Viswanathan, Aditya; Wang, Yang: Robust sparse phase retrieval made easy (2017)
  7. Liu, Zisheng; Li, Jicheng; Li, Wenbo; Dai, Pingfan: A modified greedy analysis pursuit algorithm for the cosparse analysis model (2017)
  8. Nakatsukasa, Yuji; Soma, Tasuku; Uschmajew, André: Finding a low-rank basis in a matrix subspace (2017)
  9. Zhao, Yun-Bin; Luo, Zhi-Quan: Constructing new weighted $l_1$-algorithms for the sparsest points of polyhedral sets (2017)
  10. Abusag, Nadia M.; Chappell, David J.: On sparse reconstructions in near-field acoustic holography using the method of superposition (2016)
  11. Archibald, Rick; Gelb, Anne; Platte, Rodrigo B.: Image reconstruction from undersampled Fourier data using the polynomial annihilation transform (2016)
  12. Bai, Yanqin; Yan, Xin: Conic relaxations for semi-supervised support vector machines (2016)
  13. Bako, Laurent; Ohlsson, Henrik: Analysis of a nonsmooth optimization approach to robust estimation (2016)
  14. Baratchart, Laurent; Chevillard, Sylvain; Qian, Tao: Minimax principle and lower bounds in $H^2$-rational approximation (2016)
  15. Beck, Amir; Sabach, Shoham; Teboulle, Marc: An alternating semiproximal method for nonconvex regularized structured total least squares problems (2016)
  16. Bendory, Tamir; Dekel, Shai; Feuer, Arie: Robust recovery of stream of pulses using convex optimization (2016)
  17. Berta, Mario; Fawzi, Omar; Scholz, Volkher B.: Quantum bilinear optimization (2016)
  18. Bojarovska, Irena; Flinth, Axel: Phase retrieval from Gabor measurements (2016)
  19. Bunin, Gene A.: Extended reverse-convex programming: an approximate enumeration approach to global optimization (2016)
  20. Burger, Martin; Papafitsoros, Konstantinos; Papoutsellis, Evangelos; Schönlieb, Carola-Bibiane: Infimal convolution regularisation functionals of BV and $\mathrmL^p$ spaces. I: The finite $p$ case (2016)

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