6 papers · 1 filter
Adaptive Normalized Gradient Method for -smooth Problems: Estimating Problem Parameters as a Region
Rikiya Okazaki, Shotaro Yagishita, Hideitsu Hino
We propose a parameter-free and efficient gradient descent algorithm for -smooth optimization. This notion of smoothness, introduced by Zhang et al. (2020), allows the s…
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…
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…
Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions
Shotaro Yagishita
The generalized conditional gradient method is a popular algorithm for solving composite problems whose objective function is the sum of a smooth function and a nonsmooth convex fu…
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…
Proximal Diagonal Newton Methods for Composite Optimization Problems
Shotaro Yagishita, Shummin Nakayama
This paper proposes new proximal Newton-type methods with a diagonal metric for solving composite optimization problems whose objective function is the sum of a twice continuously…