3 papers
math.OC2026
A proximal gradient method with adaptive backtracking for weakly smooth multiobjective optimization
Yuki Miyazaki, Masaru Ito, Shotaro Yagishita
In this paper, we propose a proximal gradient method with adaptive linesearch for multiobjective optimization problems whose objective functions are weakly smooth, i.e., they have…
math.OC2026
Proximal gradient-type method with generalized distance and convergence analysis without global descent lemma
Shotaro Yagishita, Masaru Ito
We consider solving nonconvex composite optimization problems in which the sum of a smooth function and a nonsmooth function is minimized. Many of convergence analyses of proximal…
math.OC2025
Simple linesearch-free first-order methods for nonconvex optimization
Shotaro Yagishita, Masaru Ito
This paper presents an auto-conditioned proximal gradient method for nonconvex optimization. The method determines the stepsize using an estimation of local curvature and does not…