Solving convex programming problems with equality constaints by neural networks

DOI10.1016/S0898-1221(98)00172-2zbMath0941.90060OpenAlexW2022203442MaRDI QIDQ1125028

S. H. Smith

Publication date: 29 November 1999

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0898-1221(98)00172-2

zbMATH Keywords

convex programmingartificial neural networkspenalty functionHopfield networks

Mathematics Subject Classification ID

Convex programming (90C25) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Existence and stability of equilibria of the continuous-time Hopfield neural network, Finite-time synchronization of coupled Cohen-Grossberg neural networks with and without coupling delays, Solving complementarity and variational inequalities problems using neural networks, Convergence analysis of a differential equation approach for solving nonlinear programming problems, Novel global exponential stability criterion for recurrent neural networks with time-varying delay, Solving nonlinear complementarity problems with neural networks: A reformulation method approach, Numerical solution for linear and quadratic programming problems using a recurrent neural network, A smoothed NR neural network for solving nonlinear convex programs with second-order cone constraints, Primal-dual solution for the linear programming problems using neural networks, Numerical solutions for constrained quadratic problems using high-performance neural networks

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• ``Neural computation of decisions in optimization problems
• Delay structure conditions for identifiability of closed loop systems
• A new neural network for solving linear programming problems
• A deterministic annealing neural network for convex programming
• An approach to nonlinear programming
• Unifying the Tank and Hopfield linear programming circuit and the canonical nonlinear programming circuit of Chua and Lin
• An inexact approach to solving linear semi-infinite programming problems
• Complete stability of autonomous reciprocal nonlinear networks
• Neural networks for constrained optimization problems
• Analysis and design of a recurrent neural network for linear programming
• An Unconstrained Convex Programming Approach to Linear Semi-Infinite Programming
• Neural networks and physical systems with emergent collective computational abilities.
• Neurons with graded response have collective computational properties like those of two-state neurons.