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.
Keywords for this software
References in zbMATH (referenced in 369 articles , 1 standard article )
Showing results 1 to 20 of 369.
Sorted by year (- Gillis, Nicolas; Sharma, Punit: A semi-analytical approach for the positive semidefinite Procrustes problem (2018)
- Johnson, Scott C.; Chakrabarty, Ankush; Hu, Jianghai; Żak, Stanisław H.; DeCarlo, Raymond A.: Dual-mode robust fault estimation for switched linear systems with state jumps (2018)
- Kaiser, Eurika; Morzyński, Marek; Daviller, Guillaume; Kutz, J.Nathan; Brunton, Bingni W.; Brunton, Steven L.: Sparsity enabled cluster reduced-order models for control (2018)
- Anqi Fu, Balasubramanian Narasimhan, Stephen Boyd: CVXR: An R Package for Disciplined Convex Optimization (2017) arXiv
- Bai, Yanqin; Wei, Yudan; Li, Qian: An optimal trade-off model for portfolio selection with sensitivity of parameters (2017)
- Beck, Amir; Pan, Dror: A branch and bound algorithm for nonconvex quadratic optimization with ball and linear constraints (2017)
- Bhowmick, Parijat; Patra, Sourav: On LTI output strictly negative-imaginary systems (2017)
- Bhowmick, Parijat; Patra, Sourav: An observer-based control scheme using negative-imaginary theory (2017)
- Bongini, Mattia; Fornasier, Massimo; Hansen, Markus; Maggioni, Mauro: Inferring interaction rules from observations of evolutive systems. I: The variational approach. (2017)
- Borcea, Liliana; Kocyigit, Ilker: Imaging in random media with convex optimization (2017)
- Cai, Yun; Li, Song: Convergence and stability of iteratively reweighted least squares for low-rank matrix recovery (2017)
- Chao, Hsiao-Han; Vandenberghe, Lieven: Semidefinite representations of gauge functions for structured low-rank matrix decomposition (2017)
- Chen, Bilian; He, Simai; Li, Zhening; Zhang, Shuzhong: On new classes of nonnegative symmetric tensors (2017)
- Cheng, Xiaoming; Li, Huifeng; Zhang, Ran: Autonomous trajectory planning for space vehicles with a Newton-Kantorovich/convex programming approach (2017)
- Chuong, T.D.; Jeyakumar, V.: A generalized Farkas lemma with a numerical certificate and linear semi-infinite programs with SDP duals (2017)
- Cox, Bruce; Juditsky, Anatoli; Nemirovski, Arkadi: Decomposition techniques for bilinear saddle point problems and variational inequalities with affine monotone operators (2017)
- Deori, Luca; Garatti, Simone; Prandini, Maria: Trading performance for state constraint feasibility in stochastic constrained control: a randomized approach (2017)
- Ding, Chao; Qi, Hou-Duo: Convex Euclidean distance embedding for collaborative position localization with NLOS mitigation (2017)
- Dumitrescu, Bogdan: Positive trigonometric polynomials and signal processing applications (2017)
- Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)