On Generating Test Problems for Nonlinear Programming Algorithms

1986 ◽  
Vol 7 (3) ◽  
pp. 769-798 ◽  
Author(s):  
Richard H. Bartels ◽  
Nezam Mahdavi-Amiri
Keyword(s):  
Nonlinear Programming ◽  
Test Problems ◽  
Nonlinear Programming Algorithms ◽  
Programming Algorithms

Cluster Analysis for the Cognitive Selection of Nonlinear Programming Algorithms

1990 ◽  
Author(s):  
Jin-xian Ma ◽  
Shi-huai Xie ◽  
Yong Chen
Keyword(s):  
Cluster Analysis ◽  
Nonlinear Programming ◽  
Expert System ◽  
Mechanical Design ◽  
Test Problems ◽  
Comparative Performance ◽  
Nonlinear Programming Algorithms ◽  
Cognitive Selection ◽  
Programming Algorithms ◽  
Selection Of

Abstract In recent years, cluster analysis has played an increasingly important role in statistical pattern recognition. Hoeltzel and Chieng have shown an example on cognitive selection of nonlinear programming algorithms in a mechanical design expert system. In this paper, an improved dynamic clustering of 3000 samples came from a comparative performance evaluation of six typical nonlinear programming softwares with randomly generated test problems has been made. Explanations resulting from the cluster analysis have been used to build rules to form the knowledge base of an optimization expert system.

The Utility of Nonlinear Programming Algorithms: A Comparative Study—Part II

1980 ◽  
Vol 102 (3) ◽  
pp. 547-551 ◽  
Author(s):  
E. Sandgren ◽  
K. M. Ragsdell
Keyword(s):  
Nonlinear Programming ◽  
Comparative Study ◽  
Engineering Design ◽  
Linear Approximation ◽  
Computational Time ◽  
Approximation Methods ◽  
Algorithm Performance ◽  
Nonlinear Programming Algorithms ◽  
Performance Curves ◽  
Programming Algorithms

A comprehensive comparative study of nonlinear programming algorithms, as applied to problems in engineering design, is presented. Linear approximation methods, interior penalty and exterior penalty methods were tested on a set of thirty problems and are rated on their ability to solve problems within a reasonable amount of computational time. In this paper, we give and discuss numerical results and algorithm performance curves.

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