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 278 articles , 1 standard article )

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  1. Bai, Yanqin; Wei, Yudan; Li, Qian: An optimal trade-off model for portfolio selection with sensitivity of parameters (2017)
  2. Bongini, Mattia; Fornasier, Massimo; Hansen, Markus; Maggioni, Mauro: Inferring interaction rules from observations of evolutive systems. I: The variational approach. (2017)
  3. Borcea, Liliana; Kocyigit, Ilker: Imaging in random media with convex optimization (2017)
  4. Chen, Bilian; He, Simai; Li, Zhening; Zhang, Shuzhong: On new classes of nonnegative symmetric tensors (2017)
  5. Chuong, T.D.; Jeyakumar, V.: A generalized Farkas lemma with a numerical certificate and linear semi-infinite programs with SDP duals (2017)
  6. Cox, Bruce; Juditsky, Anatoli; Nemirovski, Arkadi: Decomposition techniques for bilinear saddle point problems and variational inequalities with affine monotone operators (2017)
  7. Ding, Chao; Qi, Hou-Duo: Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation (2017)
  8. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  9. Fawzi, Hamza; Saunderson, James: Lieb’s concavity theorem, matrix geometric means, and semidefinite optimization (2017)
  10. Gao, Lucy L.; Zhou, Julie: D-optimal designs based on the second-order least squares estimator (2017)
  11. Gotoh, Jun-ya; Uryasev, Stan: Support vector machines based on convex risk functions and general norms (2017)
  12. Iwen, Mark; Viswanathan, Aditya; Wang, Yang: Robust sparse phase retrieval made easy (2017)
  13. Liu, Zisheng; Li, Jicheng; Li, Wenbo; Dai, Pingfan: A modified greedy analysis pursuit algorithm for the cosparse analysis model (2017)
  14. Nakatsukasa, Yuji; Soma, Tasuku; Uschmajew, André: Finding a low-rank basis in a matrix subspace (2017)
  15. Sabach, Shoham; Shtern, Shimrit: A first order method for solving convex bilevel optimization problems (2017)
  16. Zhao, Yun-Bin; Luo, Zhi-Quan: Constructing new weighted $l_1$-algorithms for the sparsest points of polyhedral sets (2017)
  17. Zhou, Jing; Fang, Shu-Cherng; Xing, Wenxun: Conic approximation to quadratic optimization with linear complementarity constraints (2017)
  18. Abusag, Nadia M.; Chappell, David J.: On sparse reconstructions in near-field acoustic holography using the method of superposition (2016)
  19. Archibald, Rick; Gelb, Anne; Platte, Rodrigo B.: Image reconstruction from undersampled Fourier data using the polynomial annihilation transform (2016)
  20. Bai, Yanqin; Yan, Xin: Conic relaxations for semi-supervised support vector machines (2016)

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