From Perspective Maps to Epigraphical Projections

• 成果类型: Article
• 署名作者: Friedlander, Michael P.; Goodwin, Ariel; Hoheisel, Tim
• 署名单位: University of British Columbia; McGill University
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X
• DOI: 10.1287/moor.2022.1317
• 发表日期: 2023
• 页码: 1711-1740
• 关键词: optimality conditions; infimal convolution; convex-analysis; Newton method; subregularity; calculus

摘要

The projection onto the epigraph or a level set of a closed proper convex function can be achieved by finding a root of a scalar equation that involves the proximal operator as a function of the proximal parameter. This paper develops the variational analysis of this scalar equation. The approach is based on a study of the variational-analytic properties of general convex optimization problems that are (partial) infimal projections of the sum of the function in question and the perspective map of a convex kernel. When the kernel is the Euclidean norm squared, the solution map corresponds to the proximal map, and thus, the variational properties derived for the general case apply to the proximal case. Properties of the value function and the corresponding solution map-including local Lipschitz continuity, directional differentiability, and semismoothness-are derived. An SC1 optimization framework for computing epigraphical and level-set projections is, thus, established. Numerical experiments on one-norm projection illustrate the effectiveness of the approach as compared with specialized algorithms.