34 citations · 82 across the 15 of their papers we have counts for
15 papers
Extension of the safeguarding stepsize interval in Adaptive Gradient Descent
Saneatsu Kagawa, Nobuo Yamashita
In this paper, inspired by the idea of Adaptive Gradient Descent (AdGD) by Malitsky and Mishchenko, we propose a method that adaptively provides a stepsize interval, which represen…
Convergence analysis and acceleration of the smoothing methods for solving extensive-form games
Keigo Habara, Ellen Hidemi Fukuda, Nobuo Yamashita
The extensive-form game has been studied considerably in recent years. It can represent games with multiple decision points and incomplete information, and hence it is helpful in f…
A Stochastic Variance Reduced Gradient using Barzilai-Borwein Techniques as Second Order Information
Hardik Tankaria, Nobuo Yamashita
In this paper, we consider to improve the stochastic variance reduce gradient (SVRG) method via incorporating the curvature information of the objective function. We propose to red…
Monotonicity for Multiobjective Accelerated Proximal Gradient Methods
Yuki Nishimura, Ellen H. Fukuda, Nobuo Yamashita
Accelerated proximal gradient methods, which are also called fast iterative shrinkage-thresholding algorithms (FISTA) are known to be efficient for many applications. Recently, Tan…
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…
Distributionally Robust Expected Residual Minimization for Stochastic Variational Inequality Problems
Atsushi Hori, Yuya Yamakawa, Nobuo Yamashita
The stochastic variational inequality problem (SVIP) is an equilibrium model that includes random variables and has been widely applied in various fields such as economics and engi…
An equivalent nonlinear optimization model with triangular low-rank factorization for semidefinite programs
Yuya Yamakawa, Tetsuya Ikegami, Ellen H. Fukuda +1
In this paper, we propose a new nonlinear optimization model to solve semidefinite optimization problems (SDPs), providing some properties related to local optimal solutions. The p…
A Regularized Limited Memory BFGS method for Large-Scale Unconstrained Optimization and its Efficient Implementations
Hardik Tankaria, Shinji Sugimoto, Nobuo Yamashita
The limited memory BFGS (L-BFGS) method is one of the popular methods for solving large-scale unconstrained optimization. Since the standard L-BFGS method uses a line search to gua…
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…
Alternating Direction Method of Multipliers with Variable Metric Indefinite Proximal Terms for Convex Optimization
Yan Gu, Nobuo Yamashita
This paper studies a proximal alternating direction method of multipliers (ADMM) with variable metric indefinite proximal terms for linearly constrained convex optimization problem…
An Alternating Direction Method of Multipliers with the BFGS Update for Structured Convex Quadratic Optimization
Yan Gu, Nobuo Yamashita
The alternating direction method of multipliers (ADMM) is an effective method for solving wide fields of convex problems. At each iteration, the classical ADMM solves two subproble…
Duality of nonconvex optimization with positively homogeneous functions
Shota Yamanaka, Nobuo Yamashita
We consider an optimization problem with positively homogeneous functions in its objective and constraint functions. Examples of such positively homogeneous functions include the a…
Duality of optimization problems with gauge functions
Shota Yamanaka, Nobuo Yamashita
Recently, Yamanaka and Yamashita proposed the so-called positively homogeneous optimization problem, which includes many important problems, such as the absolute-value and the gaug…