Work

Nonsmooth Convex Optimization using the Specular Gradient Method with Root-Linear Convergence

Year: 2024

Type: preprint

Abstract: We propose the specular gradient method for one-dimensional convex optimization. Assuming that the minimum is attained and a suitable initial distance bound holds, we establish R-linear convergence using normalized steps of geometrically decreasing length. Neither strong convexity nor differentiability is required.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2412.20747

Authors: Jung, Kiyuob, Oh, Jehan

Institutions: Michigan State University, Kyungpook National University

Language: English

Topic: Optimization and Variational Analysis

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea

Awards: G1345427392, G2058730565, G4702862879, G791892431, G7995353031

OpenAlex ID: W4405956736

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