8 papers
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…
Optimal reinsurance and investment via stochastic projected gradient method based on Malliavin calculus
Yuta Otsuki, Shotaro Yagishita
This paper proposes a new approach using the stochastic projected gradient method and Malliavin calculus for optimal reinsurance and investment strategies. Unlike traditional metho…
Fast algorithm for sparse least trimmed squares via trimmed-regularized reformulation
Shotaro Yagishita
The least trimmed squares (LTS) is a reasonable formulation of robust regression whereas it suffers from high computational cost due to the nonconvexity and nonsmoothness of its ob…