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 667 articles , 1 standard article )

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  1. Adriaens, Florian; De Bie, Tijl; Gionis, Aristides; Lijffijt, Jefrey; Matakos, Antonis; Rozenshtein, Polina: Relaxing the strong triadic closure problem for edge strength inference (2020)
  2. Ahmadi, Amir Ali; Hall, Georgina: On the complexity of detecting convexity over a box (2020)
  3. Aliyev, Nicat; Mehrmann, Volker; Mengi, Emre: Approximation of stability radii for large-scale dissipative Hamiltonian systems (2020)
  4. Al-Matouq, Ali; Vincent, Tyrone: A convex optimization framework for the identification of homogeneous reaction systems (2020)
  5. AlMomani, Abd AlRahman R.; Sun, Jie; Bollt, Erik: How entropic regression beats the outliers problem in nonlinear system identification (2020)
  6. Altıntan, Derya; Koeppl, Heinz: Hybrid master equation for jump-diffusion approximation of biomolecular reaction networks (2020)
  7. Bhawal, Chayan; Pal, Debasattam; Belur, Madhu N.: Closed-form solutions of singular KYP lemma: strongly passive systems, and fast lossless trajectories (2020)
  8. Bhowmick, Parijat; Patra, Sourav: Solution to negative-imaginary control problem for uncertain LTI systems with multi-objective performance (2020)
  9. Budninskiy, Max; Abdelaziz, Ameera; Tong, Yiying; Desbrun, Mathieu: Laplacian-optimized diffusion for semi-supervised learning (2020)
  10. Ceccon, Francesco; Siirola, John D.; Misener, Ruth: SUSPECT: MINLP special structure detector for Pyomo (2020)
  11. Cen, Xiaoli; Xia, Yong; Gao, Runxuan; Yang, Tianzhi: On Chebyshev center of the intersection of two ellipsoids (2020)
  12. Chen, Ximing; Ogura, Masaki; Preciado, Victor M.: Bounds on the spectral radius of digraphs from subgraph counts (2020)
  13. Chun, Il Yong; Adcock, Ben: Uniform recovery from subgaussian multi-sensor measurements (2020)
  14. Chuong, Thai Doan: Semidefinite program duals for separable polynomial programs involving box constraints (2020)
  15. Coey, Chris; Lubin, Miles; Vielma, Juan Pablo: Outer approximation with conic certificates for mixed-integer convex problems (2020)
  16. Córdova, Lucía; He, Yifei; Kruczenski, Martin; Vieira, Pedro: The O(N) S-matrix monolith (2020)
  17. de Klerk, Etienne; Kuhn, Daniel; Postek, Krzysztof: Distributionally robust optimization with polynomial densities: theory, models and algorithms (2020)
  18. Gouveia, João; Pong, Ting Kei; Saee, Mina: Inner approximating the completely positive cone via the cone of scaled diagonally dominant matrices (2020)
  19. Iwen, Mark A.; Preskitt, Brian; Saab, Rayan; Viswanathan, Aditya: Phase retrieval from local measurements: improved robustness via eigenvector-based angular synchronization (2020)
  20. Jiao, Liguo; Lee, Jae Hyoung; Zhou, Yuying: A hybrid approach for finding efficient solutions in vector optimization with SOS-convex polynomials (2020)

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