- Referenced in 1429 articles
- solvers are significantly faster than classical convex optimization algorithms, it should be kept in mind...
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- also solve much more complex convex optimization problems, including many involving nondifferentiable functions, such ... entropy maximization, determinant maximization, and many other convex programs. As of version 2.0, CVX also...
- Referenced in 2583 articles
- optimized can be expressed as a linear function or a convex quadratic function. The variables...
- Referenced in 345 articles
- algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates ... best known results under the online convex optimization framework. Empirical results demonstrate that Adam works...
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- UNLocboX is a matlab convex optimization toolbox part of the UnlocX project. It composed...
- Referenced in 184 articles
- diffracted patterns by solving a simple convex optimization problem inspired by the recent literature...
- Referenced in 179 articles
- linear, quadratic, and nonlinear smooth optimization problems, both convex and nonconvex. It is also effective ... MPECs), and mixed-integer programming (MIPs), particular convex mixed integer, nonlinear problems (MINLP). KNITRO ... KNITRO will remain the leader in nonlinear optimization...
- Referenced in 50 articles
- CVXOPT; Python Software for Convex Optimization. CVXOPT is a free software package for convex optimization ... make the development of software for convex optimization applications straightforward by building on Python...
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- optimization of non-linear or non-convex continuous optimization problems. They belong to the class...
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- insights for the analysis of non-convex optimization schemes that may have implications for computational...
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- Python-Embedded Modeling Language for Convex Optimization. CVXPY is a domain-specific language for convex ... allows the user to express convex optimization problems in a natural syntax that follows ... CVXPY makes it easy to combine convex optimization with high-level features of Python such...
- Referenced in 70 articles
- Algorithm 813: SPG -- software for convex-constrained optimization: Fortran 77 software implementing the SPG method ... gradient algorithm for solving large-scale convex-constrained optimization problems. It combines the classical projected...
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- optimization codes that use linear convex polygons. In topology optimization, polygonal discretizations have been shown...
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- package for solving large scale convex optimization problems. The code is an implementation...
- Referenced in 39 articles
- CVXGEN: a code generator for embedded convex optimization. CVXGEN is a software tool that takes ... high level description of a convex optimization problem family, and automatically generates custom C code ... families that can be transformed, using disciplined convex programming techniques, to convex quadratic programs...
- Referenced in 300 articles
- Nonlinear (convex & nonconvex/Global), Quadratic, Quadratically Constrained, Second Order Cone, Stochastic, and Integer optimization models faster...
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- constrained optimization, beginning with a substantial treatment of linear programming, and proceeding to convex analysis ... flows, integer programming, quadratic programming, and convex optimization. The book is carefully written. Specific examples...
- Referenced in 937 articles
- free MATLAB toolbox for rapid prototyping of optimization problems. The package initially aimed ... second order cone programming, semidefinite programming, non-convex semidefinite programming, mixed integer programming, multi-parametric...
- Referenced in 30 articles
- iPiasco: inertial proximal algorithm for strongly convex optimization. In this paper, we present a forward ... inertial term for solving a strongly convex optimization problem of a certain type. The strongly ... smooth convex and a smooth convex function. This additional knowledge is used for deriving ... provably optimal worst-case rate of convergence for smooth strongly convex functions. We demonstrate...
- Referenced in 38 articles
- 1/k2) is optimal for smooth convex optimization via the first-order black-box methods...