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 636 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. Aliyev, Nicat; Mehrmann, Volker; Mengi, Emre: Approximation of stability radii for large-scale dissipative Hamiltonian systems (2020)
  3. Al-Matouq, Ali; Vincent, Tyrone: A convex optimization framework for the identification of homogeneous reaction systems (2020)
  4. AlMomani, Abd AlRahman R.; Sun, Jie; Bollt, Erik: How entropic regression beats the outliers problem in nonlinear system identification (2020)
  5. Bhowmick, Parijat; Patra, Sourav: Solution to negative-imaginary control problem for uncertain LTI systems with multi-objective performance (2020)
  6. Budninskiy, Max; Abdelaziz, Ameera; Tong, Yiying; Desbrun, Mathieu: Laplacian-optimized diffusion for semi-supervised learning (2020)
  7. Cen, Xiaoli; Xia, Yong; Gao, Runxuan; Yang, Tianzhi: On Chebyshev center of the intersection of two ellipsoids (2020)
  8. Chun, Il Yong; Adcock, Ben: Uniform recovery from subgaussian multi-sensor measurements (2020)
  9. Chuong, Thai Doan: Semidefinite program duals for separable polynomial programs involving box constraints (2020)
  10. Gouveia, João; Pong, Ting Kei; Saee, Mina: Inner approximating the completely positive cone via the cone of scaled diagonally dominant matrices (2020)
  11. Iwen, Mark A.; Preskitt, Brian; Saab, Rayan; Viswanathan, Aditya: Phase retrieval from local measurements: improved robustness via eigenvector-based angular synchronization (2020)
  12. Jiao, Liguo; Lee, Jae Hyoung; Zhou, Yuying: A hybrid approach for finding efficient solutions in vector optimization with SOS-convex polynomials (2020)
  13. Maldonado, Sebastián; López, Julio; Vairetti, Carla: Profit-based churn prediction based on minimax probability machines (2020)
  14. Mohan, Shravan; Mithun, I. M.; Bhikkaji, Bharath: Optimal input design for system identification using spectral decomposition (2020)
  15. Nagahara, Masaaki; Chatterjee, Debasish; Challapalli, Niharika; Vidyasagar, Mathukumalli: CLOT norm minimization for continuous hands-off control (2020)
  16. Nayak, Rupaj Kumar; Mohanty, Nirmalya Kumar: Solution of Boolean quadratic programming problems by two augmented Lagrangian algorithms based on a continuous relaxation (2020)
  17. Paulson, Joel A.; Buehler, Edward A.; Braatz, Richard D.; Mesbah, Ali: Stochastic model predictive control with joint chance constraints (2020)
  18. Schürmann, Bastian; Vignali, Riccardo; Prandini, Maria; Althoff, Matthias: Set-based control for disturbed piecewise affine systems with state and actuation constraints (2020)
  19. Sun, Defeng; Toh, Kim-Chuan; Yuan, Yancheng; Zhao, Xin-Yuan: SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0) (2020)
  20. Xia, Yong; Wang, Longfei; Wang, Xiaohui: Globally minimizing the sum of a convex-concave fraction and a convex function based on wave-curve bounds (2020)

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