Work

Decomposed quasiconvex optimization with application to generalized cone problems

Year: 2024

Type: article

Abstract: We propose a gradient-based method to solve quasiconvex optimization problems through decomposed optimization and prove local superlinear convergence under mild regularity assumptions at the optimal solution. A practical implementation further provides global convergence while maintaining the fast local convergence. In numerical examples from generalized cone programming, the proposed method reduced the number of iterations to 18 to 50% compared to bisection.

Source: Optimization Letters

DOI: 10.1007/s11590-024-02174-1

Author: Torbjørn Cunis

Institutions: University of Stuttgart, University of Michigan

Language: English

Topic: Advanced Optimization Algorithms Research

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,990

Funder: Universität Stuttgart