Title
Convergence of an Infeasible Interior-Point Algorithm from Arbitrary Positive Starting Points
Abstract
An important advantage of infeasible interior-point methods over feasible interior-point methods is their ability to be warm-started from approximate solutions. It is therefore important for the convergence theory of these algorithms not to depend on the ability to alter the starting point. In a recent paper [SIAM J. Optim., 4 (1994), pp. 208-227], Zhang proves a global linear convergence rate for an infeasible interior-point method for the horizontal linear complementarity problem, which unfortunately places a restriction on the starting point. It is easy to meet the restriction by altering the starting point, but this may take the point farther away from the solution, thus removing the advantage of warm-starting the algorithm. In this paper we extend Zhang's results to apply to arbitrary strictly positive starting points. We also show how the extended results can be used to prove convergence for an algorithm for box-constrained linear complementarity problems.
Year
DOI
Venue
1996
10.1137/0806018
SIAM JOURNAL ON OPTIMIZATION
Keywords
Field
DocType
infeasible interior-point methods,linear complementarity,global convergence
Convergence (routing),Mathematical optimization,Algorithm,Rate of convergence,Symbolic convergence theory,Interior point method,Mathematics,Zhàng
Journal
Volume
Issue
ISSN
6
2
1052-6234
Citations 
PageRank 
References 
4
1.61
6
Authors
2
Name
Order
Citations
PageRank
Stephen C. Billups120840.10
Michael C. Ferris21115142.21