Year: 2025
Type: article
Abstract: In this paper, we develop the extended proximal gradient algorithm with Nesterov’s second acceleration (EAPGs) for constrained difference-of-convex (DC) optimization problems. EAPGs has two key links to existing methods: it extends APGs (for unconstrained DC problems) by adopting the constraint handling idea from Auslender’s ESQM, and serves as a variant of ESQMe with extrapolation replaced by Nesterov’s second acceleration. Under basic assumptions, we establish the subsequential convergence of EAPGs. By introducing a restart technique and leveraging the Kurdyka–Łojasiewicz (KL) property of a suitable potential function, we further prove its global convergence, analyze its convergence rate, and do so under weaker conditions than those for APGs. Additionally, we propose EAPGsr by adding practical restart criteria to EAPGs. Numerical experiments verify the criteria’s efficiency and show that EAPGsr performs well against state-of-the-art methods for constrained and unconstrained DC problems.
Source: Axioms
Authors: Ziye Liu, Huitao Ke, Chunguang Liu
Institution: Jinan University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G3350741551
OpenAlex ID: W7117135540
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