Implementation of reduced gradient with bisection algorithms for non-convex optimization problem via stochastic perturbation
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Publication:1751057
DOI10.1007/s11075-017-0366-1zbMath1406.90097OpenAlexW2676080692MaRDI QIDQ1751057
Publication date: 23 May 2018
Published in: Numerical Algorithms (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11075-017-0366-1
linear constraintsnon-convex optimizationlarge-scale problemstochastic perturbationbisection algorithmreduced gradient algorithm
Related Items (3)
Stochastic perturbation of subgradient algorithm for nonconvex deep neural networks ⋮ Unnamed Item ⋮ Reduced subgradient bundle method for linearly constrained non-smooth non-convex problems
Cites Work
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- An extension of the Fletcher-Reeves method to linear equality constrained optimization problem
- Stochastic perturbation of reduced gradient \& GRG methods for nonconvex programming problems
- Global optimization through a stochastic perturbation of the Polak-Ribière conjugate gradient method
- A novel Frank-Wolfe algorithm. Analysis and applications to large-scale SVM training
- A continuous approach to combinatorial optimization: application of water system pump operations
- Modification, implementation and comparison of three algorithms for globally solving linearly constrained concave minimization problems
- Test examples for nonlinear programming codes
- A collection of test problems for constrained global optimization algorithms
- Computation of optimal feedforward and feedback control by a modified reduced gradient method
- A reduced subgradient algorithm
- Stopping Rules for a Random Optimization Method
- Dealing with degeneracy in reduced gradient algorithms
- The Tunneling Algorithm for the Global Minimization of Functions
- Linearly constrained minimax optimization
- Correction to "Optimal random perturbations for stochastic approximation using a simultaneous perturbation gradient approximation"
- On Modified Factorizations for Large-Scale Linearly Constrained Optimization
- A Reduced Gradient Algorithm for Nonlinear Network Problems
- Nonlinear Programming
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