Paper Info
Title
Computational experience with a modified potential reduction algorithm for linear programming
Abstract
We study the performance of a homogeneous and self-dual interior point solver for linear programming LP that is equipped with a continuously differentiable potential function. Our work is motivated by the apparent gap between the theoretical complexity results and long-step practical implementations in interior point algorithms. The potential function described here ensures a global linear polynomial-time convergence while providing the flexibility to integrate heuristics for generating the search directions and step length computations. Computational results on standard test problems show that LP problems are solved as efficiently in terms of the number of iterations as Mosek6.
Year
DOI
Venue
2012
10.1080/10556788.2011.634911
Optimization Methods and Software
Keywords
Field
DocType
computational result,apparent gap,self-dual interior point solver,interior point algorithm,computational experience,potential function,modified potential reduction algorithm,global linear polynomial-time convergence,long-step practical implementation,differentiable potential function,lp problem,linear programming lp,interior point method,interior point methods,computer experiment,interior point,polynomial time,linear program,linear programming
Linear-fractional programming,Convergence (routing),Mathematical optimization,Algorithm,Heuristics,Linear programming,Solver,Smoothness,Interior point method,Mathematics,Algebraic interior
Journal
Volume
Issue
ISSN
27
4-5
1055-6788
Citations 
PageRank 
References 
4
0.41
13
Authors
2
Name
Order
Citations
PageRank
Sanjay Mehrotra152177.18
Kuo-Ling Huang2804.95