On Low-Rank Convex-Convex Quadratic Fractional Programming
Optimization and Control
2023-01-27 v1
Abstract
We present an efficient algorithm for solving fractional programming problems whose objective functions are the ratio of a low-rank quadratic to a positive definite quadratic with convex constraints. The proposed algorithm for these convex-convex problems is based on the Shen-Yu Quadratic Transform which finds stationary points of concave-convex sum-of-ratios problems. We further use elements of the algorithm proposed in [arXiv:1802.10192] and the classic Dinkelbach approach to ensure convergence. We show that our algorithm performs better than previous algorithms for low-rank problems.
Cite
@article{arxiv.2301.11269,
title = {On Low-Rank Convex-Convex Quadratic Fractional Programming},
author = {Ilya Krishtal and Brendan Miller},
journal= {arXiv preprint arXiv:2301.11269},
year = {2023}
}