Year: 2014
Type: preprint
Abstract: A known first order method to find a feasible solution to a conic problem is an adapted von Neumann algorithm. We improve the distance reduction step there by projecting onto the convex hull of previously generated points using a primal active set quadratic programming (QP) algorithm. The convergence theory is improved when the QPs are as large as possible. For problems in R^2, we analyze our algorithm by epigraphs and the monotonicity of subdifferentials. Logically, the larger the set to project onto, the better the performance per iteration, and this is indeed seen in our numerical experiments.
Source: arXiv (Cornell University)
Author: Pang, C. H. Jeffrey
Language: English
Subfield: Numerical Analysis
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2270199145