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 636 articles , 1 standard article )
Showing results 1 to 20 of 636.
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- Chun, Il Yong; Adcock, Ben: Uniform recovery from subgaussian multi-sensor measurements (2020)
- Chuong, Thai Doan: Semidefinite program duals for separable polynomial programs involving box constraints (2020)
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- 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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- Paulson, Joel A.; Buehler, Edward A.; Braatz, Richard D.; Mesbah, Ali: Stochastic model predictive control with joint chance constraints (2020)
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- 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)