A neural network approach to solve geometric programs with joint probabilistic constraints

Solving Constrained Pseudoconvex Optimization Problems with deep learning-based neurodynamic optimization

“…This approach was first introduced by Hopfield & Tank (1985) to solve the traveling salesman problem. Since then, neurodynamic optimization has been applied to a wide range of optimization problems, including linear and quadratic programming problems (Xia & Wang, 2000), general convex programming problems (Xia & Feng, 2007), biconvex optimization problems (Che & Wang, 2018), global optimization problems (Che & Wang, 2019), and stochastic optimization problems (Tassouli & Lisser, 2023). These methods typically use the Lyapunov stability theorem to prove that the constructed ODE system has a global convergence property.…”