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 606 articles , 1 standard article )

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  1. Bhowmick, Parijat; Patra, Sourav: Solution to negative-imaginary control problem for uncertain LTI systems with multi-objective performance (2020)
  2. Cen, Xiaoli; Xia, Yong; Gao, Runxuan; Yang, Tianzhi: On Chebyshev center of the intersection of two ellipsoids (2020)
  3. Iwen, Mark A.; Preskitt, Brian; Saab, Rayan; Viswanathan, Aditya: Phase retrieval from local measurements: improved robustness via eigenvector-based angular synchronization (2020)
  4. Paulson, Joel A.; Buehler, Edward A.; Braatz, Richard D.; Mesbah, Ali: Stochastic model predictive control with joint chance constraints (2020)
  5. Sun, Defeng; Toh, Kim-Chuan; Yuan, Yancheng; Zhao, Xin-Yuan: SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0) (2020)
  6. Yang, Jiao; Hao, Caiyong; Zheng, Zhi: Localization of mixed near-field and far-field multi-band sources based on sparse representation (2020)
  7. Zhao, Yun-Bin: Optimal (k)-thresholding algorithms for sparse optimization problems (2020)
  8. Abdallah, L.; Haddou, M.; Migot, T.: A sub-additive DC approach to the complementarity problem (2019)
  9. Adachi, Satoru; Nakatsukasa, Yuji: Eigenvalue-based algorithm and analysis for nonconvex QCQP with one constraint (2019)
  10. Adcock, Ben; Bao, Anyi; Brugiapaglia, Simone: Correcting for unknown errors in sparse high-dimensional function approximation (2019)
  11. Adcock, Ben; Gelb, Anne; Song, Guohui; Sui, Yi: Joint sparse recovery based on variances (2019)
  12. Agrawal, Akshay; Diamond, Steven; Boyd, Stephen: Disciplined geometric programming (2019)
  13. Ahmadi, Mohamadreza; Valmorbida, Giorgio; Gayme, Dennice; Papachristodoulou, Antonis: A framework for input-output analysis of wall-bounded shear flows (2019)
  14. Ansary Karbasy, Saeid; Salahi, Maziar: A hybrid algorithm for the two-trust-region subproblem (2019)
  15. Aswani, Anil; Kaminsky, Philip; Mintz, Yonatan; Flowers, Elena; Fukuoka, Yoshimi: Behavioral modeling in weight loss interventions (2019)
  16. Bahmani, Sohail: Estimation from nonlinear observations via convex programming with application to bilinear regression (2019)
  17. Bai, Jushan; Ng, Serena: Rank regularized estimation of approximate factor models (2019)
  18. Beck, Amir; Guttmann-Beck, Nili: FOM -- a MATLAB toolbox of first-order methods for solving convex optimization problems (2019)
  19. Bohra, Pakshal; Garg, Deepak; Gurumoorthy, Karthik S.; Rajwade, Ajit: Variance-stabilization-based compressive inversion under Poisson or Poisson-Gaussian noise with analytical bounds (2019)
  20. Bomze, Immanuel M.; Cheng, Jianqiang; Dickinson, Peter J. C.; Lisser, Abdel; Liu, Jia: Notoriously hard (mixed-)binary QPs: empirical evidence on new completely positive approaches (2019)

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