Interior-Point Methods for Convex Programming
STANFORD UNIV CA SYSTEMS OPTIMIZATION LAB
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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.
- Operations Research