TY - JOUR
T1 - An Implementable Proximal Extragradient Method for Structured Fractional Programming
AU - Hao, Jiajun
AU - He, Hongjin
AU - Hou, Liangshao
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Funding Information:
The authors would like to thank the two referees for their close reading and insightful comments, which greatly help us improve the quality of this paper. H. He was supported in part by National Natural Science Foundation of China (NSFC, No. 12371303), Zhejiang Provincial Natural Science Foundation of China (No. LZ24A010001), and Ningbo Natural Science Foundation (No. 2023J014). L. Hou was supported in part by the NSFC Young Scientists Fund (No. 12301406).
PY - 2025/11
Y1 - 2025/11
N2 - A class of structured fractional programming is studied, where the
numerator of the objective function consists of the sum of a nonsmooth
function and a smooth function, while the denominator is a convex
function. To solve this class of problems, the implementable proximal
extragradient algorithm (IPEM) and its variant with linesearch (IPEM-L)
are proposed. First, the fractional structure is handled using
Dinkelbach’s method. Then, the extended extragradient method is applied
to solve the resulting subproblems. By incorporating parameter updates,
the proposed algorithms are formulated. A practical linesearch is
further introduced to enhance efficiency of the IPEM. Under certain
assumptions, both subsequential and whole sequence convergence are
established, with the latter relying on the Kurdyka-Łojasiewicz (KŁ)
property. Finally, numerical experiments on some synthetic and real
datasets demonstrate the competitiveness of the proposed algorithms.
AB - A class of structured fractional programming is studied, where the
numerator of the objective function consists of the sum of a nonsmooth
function and a smooth function, while the denominator is a convex
function. To solve this class of problems, the implementable proximal
extragradient algorithm (IPEM) and its variant with linesearch (IPEM-L)
are proposed. First, the fractional structure is handled using
Dinkelbach’s method. Then, the extended extragradient method is applied
to solve the resulting subproblems. By incorporating parameter updates,
the proposed algorithms are formulated. A practical linesearch is
further introduced to enhance efficiency of the IPEM. Under certain
assumptions, both subsequential and whole sequence convergence are
established, with the latter relying on the Kurdyka-Łojasiewicz (KŁ)
property. Finally, numerical experiments on some synthetic and real
datasets demonstrate the competitiveness of the proposed algorithms.
KW - Structured fractional programming
KW - Extragradient method
KW - Kurdyka-Ł ojasiewicz property
KW - Proximal gradient method
KW - Matrix completion
UR - https://www.scopus.com/pages/publications/105012303358
U2 - 10.1007/s10957-025-02799-x
DO - 10.1007/s10957-025-02799-x
M3 - Journal article
AN - SCOPUS:105012303358
SN - 0022-3239
VL - 207
JO - Journal of Optimization Theory and Applications
JF - Journal of Optimization Theory and Applications
IS - 2
M1 - 35
ER -