Paper Info
Title
Solving Nonconvex Nonlinear Programs With Reverse Convex Constraints By Sequential Linear Programming
Abstract
The sequential linear programming (SLP) method for solving nonlinear problems was introduced in the 1960s. Many papers that attempted to use SLP reported poor performance and convergence issues. We found that nonlinear programs with reverse convex constraints, which are the most difficult nonlinear programs with many local optima, are solved (heuristically) very well by SLP. We proved that for this type of problems, the solutions to the sequence of the linear programming problems converge to a local optimum. Since the final solution depends on the starting solution, we propose to apply SLP in a multistart approach starting from randomly generated solutions. This multistart SLP is very easy to implement. We recommend that the research community reconsiders the application of SLP for this type of problems.
Year
DOI
Venue
2020
10.1111/itor.12736
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
Keywords
DocType
Volume
nonconvex optimization, heuristic procedures, sequential linear programs
Journal
27
Issue
ISSN
Citations 
3
0969-6016
1
PageRank 
References 
Authors
0.48
0
2
Name
Order
Citations
PageRank
Zvi Drezner11195140.69
Pawel Kalczynski210.48