Test Problem Generator by Neural Network for Algorithms that Try Solving Nonlinear Programming Problems Globally

Authors:
Degang Liu;Xiang-Sun Zhang
Affiliations:
Academy of Mathematics and System sciences , Institute of Applied Mathematics , Chinese Academy of Sciences , Beijing 100080, China (e-mail: dliu@math2.amt.ac.cn);Academy of Mathematics and System sciences , Institute of Applied Mathematics , Chinese Academy of Sciences , Beijing 100080, China (e-mail: xszhang@amath2.amt.ac.cn)
Venue:
Journal of Global Optimization
Year:
2000

Citing 9
Cited 0

Methods for global concave minimization: A bibliographic survey

SIAM Review
More test examples for nonlinear programming codes

More test examples for nonlinear programming codes
New computer methods for global optimization

New computer methods for global optimization
Global optimization

Global optimization
A collection of test problems for constrained global optimization algorithms

A collection of test problems for constrained global optimization algorithms
Randomly Generated Test Problems for Positive Definite Quadratic Programming

ACM Transactions on Mathematical Software (TOMS)
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Neural Networks for Optimization and Signal Processing

Neural Networks for Optimization and Signal Processing
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)

Introduction to Global Optimization (Nonconvex Optimization and Its Applications)

Quantified Score

Hi-index	0.00

Visualization

Abstract

A test problem generator, by means of neural networks nonlinear function approximation capability, is given in this paper which provides test problems, with many predetermined local minima and a global minimum, to evaluate nonlinear programming algorithms that are designed to solve the problem globally.