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 520 articles , 1 standard article )

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  1. Adachi, Satoru; Nakatsukasa, Yuji: Eigenvalue-based algorithm and analysis for nonconvex QCQP with one constraint (2019)
  2. Adcock, Ben; Gelb, Anne; Song, Guohui; Sui, Yi: Joint sparse recovery based on variances (2019)
  3. Aswani, Anil; Kaminsky, Philip; Mintz, Yonatan; Flowers, Elena; Fukuoka, Yoshimi: Behavioral modeling in weight loss interventions (2019)
  4. Beck, Amir; Guttmann-Beck, Nili: FOM -- a MATLAB toolbox of first-order methods for solving convex optimization problems (2019)
  5. Cai, Jian-Feng; Wang, Tianming; Wei, Ke: Fast and provable algorithms for spectrally sparse signal reconstruction via low-rank Hankel matrix completion (2019)
  6. Chen, Xiaojun; Kelley, C. T.: Convergence of the EDIIS algorithm for nonlinear equations (2019)
  7. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv
  8. Hashagen, Anna-Lena K.; Wolf, Michael M.: Universality and optimality in the information-disturbance tradeoff (2019)
  9. Ikeda, Takuya; Nagahara, Masaaki: Time-optimal hands-off control for linear time-invariant systems (2019)
  10. Ivanenko, Yevhen; Gustafsson, Mats; Jonsson, B. L. G.; Luger, Annemarie; Nilsson, Börje; Nordebo, Sven; Toft, Joachim: Passive approximation and optimization using B-splines (2019)
  11. Li, Yuanxin; Chi, Yuejie: Stable separation and super-resolution of mixture models (2019)
  12. Luo, Hezhi; Bai, Xiaodi; Peng, Jiming: Enhancing semidefinite relaxation for quadratically constrained quadratic programming via penalty methods (2019)
  13. Manohar, Krithika; Kaiser, Eurika; Brunton, Steven L.; Kutz, J. Nathan: Optimized sampling for multiscale dynamics (2019)
  14. Singh, Vikas Vikram; Lisser, Abdel: A second-order cone programming formulation for two player zero-sum games with chance constraints (2019)
  15. Slawski, Martin; Ben-David, Emanuel: Linear regression with sparsely permuted data (2019)
  16. Vinod, Abraham P.; Gleason, Joseph D.; Oishi, Meeko M. K.: SReachTools: a MATLAB stochastic reachability toolbox (2019)
  17. Vinod, Abraham P.; Gleason, Joseph D.; Oishi, Meeko M. K.: Demo abstract: SReachTools: a MATLAB stochastic reachability toolbox. (2019)
  18. Wu, Baiyi; Li, Duan; Jiang, Rujun: Quadratic convex reformulation for quadratic programming with linear on-off constraints (2019)
  19. Ahiyevich, U. M.; Parsegov, S. E.; Shcherbakov, P. S.: Upper bounds on peaks in discrete-time linear systems (2018)
  20. Ankenman, Jerrod; Leeb, William: Mixed Hölder matrix discovery via wavelet shrinkage and Calderón-Zygmund decompositions (2018)

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