Accession Number:

ADA231372

Title:

Interior-Point Methods for Convex Programming

Descriptive Note:

Technical rept.

Corporate Author:

STANFORD UNIV CA SYSTEMS OPTIMIZATION LAB

Personal Author(s):

Report Date:

1990-11-01

Pagination or Media Count:

29.0

Abstract:

This work is concerned with generalized convex programming problems, where the objective and also the constraints belong to a certain class of convex functions. It examines the relationship of two conditions for generalized convex programming--self concordance and a relative Lipschitz condition--and gives an outline for a short and simple analysis of an interior-point method for generalized convex programming. It generalizes ellipsoidal approximations for the feasible set, and in the special case of a nondegenerate linear program it establishes a uniform bound on the condition number of the matrices occurring when the iterates remain near the path of centers.

Subject Categories:

  • Operations Research

Distribution Statement:

APPROVED FOR PUBLIC RELEASE