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 734 articles , 1 standard article )

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  1. Ammari, Habib; Bretin, Elie; Millien, Pierre; Seppecher, Laurent: A direct linear inversion for discontinuous elastic parameters recovery from internal displacement information only (2021)
  2. Anderson, Tor; Martínez, Sonia: Distributed resource allocation with binary decisions via Newton-like neural network dynamics (2021)
  3. Bhowmick, Parijat; Lanzon, Alexander: Applying negative imaginary systems theory to non-square systems with polytopic uncertainty (2021)
  4. Cheng, Sheng; Martins, Nuno C.: An optimality gap test for a semidefinite relaxation of a quadratic program with two quadratic constraints (2021)
  5. de Brito, Daniel U.; Durlofsky, Louis J.: Field development optimization using a sequence of surrogate treatments (2021)
  6. De Persis, Claudio; Tesi, Pietro: Low-complexity learning of linear quadratic regulators from noisy data (2021)
  7. Genzel, Martin; Kutyniok, Gitta; März, Maximilian: (\ell^1)-analysis minimization and generalized (co-)sparsity: when does recovery succeed? (2021)
  8. Hu, Bin; Seiler, Peter; Lessard, Laurent: Analysis of biased stochastic gradient descent using sequential semidefinite programs (2021)
  9. Jiao, Liguo; Lee, Jae Hyoung: Finding efficient solutions in robust multiple objective optimization with SOS-convex polynomial data (2021)
  10. Kim, Donghwan; Fessler, Jeffrey A.: Optimizing the efficiency of first-order methods for decreasing the gradient of smooth convex functions (2021)
  11. Kotsalis, Georgios; Lan, Guanghui; Nemirovski, Arkadi S.: Convex optimization for finite-horizon robust covariance control of linear stochastic systems (2021)
  12. Łagosz, S.; Śliwiński, P.; Wachel, P.: Identification of Wiener-Hammerstein systems by (\ell_1)-constrained Volterra series (2021)
  13. Li, Wenyu; Hegde, Arun; Oreluk, James; Packard, Andrew; Frenklach, Michael: Representing model discrepancy in bound-to-bound data collaboration (2021)
  14. Polyak, B. T.; Khlebnikov, M. V.; Shcherbakov, P. S.: Linear matrix inequalities in control systems with uncertainty (2021)
  15. Silva, Guilherme Fróes; Donaire, Alejandro; McFadyen, Aaron; Ford, Jason J.: String stable integral control design for vehicle platoons with disturbances (2021)
  16. Woolnough, D.; Jeyakumar, V.; Li, G.: Exact conic programming reformulations of two-stage adjustable robust linear programs with new quadratic decision rules (2021)
  17. Xu, Yangyang: Iteration complexity of inexact augmented Lagrangian methods for constrained convex programming (2021)
  18. Adriaens, Florian; De Bie, Tijl; Gionis, Aristides; Lijffijt, Jefrey; Matakos, Antonis; Rozenshtein, Polina: Relaxing the strong triadic closure problem for edge strength inference (2020)
  19. Agrawal, Akshay; Boyd, Stephen: Disciplined quasiconvex programming (2020)
  20. Ahmadi, Amir Ali; Hall, Georgina: On the complexity of detecting convexity over a box (2020)

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