34 citations · 79 across the 4 of their papers we have counts for
4 papers
A globally convergent fast iterative shrinkage-thresholding algorithm with a new momentum factor for single and multi-objective convex optimization
Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita
Convex-composite optimization, which minimizes an objective function represented by the sum of a differentiable function and a convex one, is widely used in machine learning and si…
An accelerated proximal gradient method for multiobjective optimization
Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita
This paper presents an accelerated proximal gradient method for multiobjective optimization, in which each objective function is the sum of a continuously differentiable, convex fu…
New merit functions for multiobjective optimization and their properties
Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita
A merit (gap) function is a map that returns zero at the solutions of problems and strictly positive values otherwise. Its minimization is equivalent to the original problem by def…
Convergence rates analysis of a multiobjective proximal gradient method
Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita
Many descent algorithms for multiobjective optimization have been developed in the last two decades. Tanabe et al. (Comput Optim Appl 72(2):339--361, 2019) proposed a proximal grad…