Nobuo Yamashita

Publication Activity (10 Years)

Years Active: 1996-2024
Publications (10 Years): 20

Top Topics

Constrained Optimization

Nonlinear Optimization Problems

Gradient Method

Convergence Rate

Top Venues

Comput. Optim. Appl.

Optim. Methods Softw.

Publications

Shumpei Ariizumi, Yuya Yamakawa, Nobuo Yamashita
Convergence properties of Levenberg-Marquardt methods with generalized regularization terms. Appl. Math. Comput. 463 (2024)
Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita
An accelerated proximal gradient method for multiobjective optimization. Comput. Optim. Appl. 86 (2) (2023)
Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita
Convergence rates analysis of a multiobjective proximal gradient method. Optim. Lett. 17 (2) (2023)
Yuya Yamakawa, Tetsuya Ikegami, Ellen H. Fukuda, Nobuo Yamashita
An equivalent nonlinear optimization model with triangular low-rank factorization for semidefinite programs. Optim. Methods Softw. 38 (6) (2023)
Keigo Habara, Ellen Hidemi Fukuda, Nobuo Yamashita
Convergence analysis and acceleration of the smoothing methods for solving extensive-form games. CoRR (2023)
Atsushi Hori, Yuya Yamakawa, Nobuo Yamashita
Distributionally robust expected residual minimization for stochastic variational inequality problems. Optim. Methods Softw. 38 (4) (2023)
Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita
An accelerated proximal gradient method for multiobjective optimization. CoRR (2022)
Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita
A globally convergent fast iterative shrinkage-thresholding algorithm with a new momentum factor for single and multi-objective convex optimization. CoRR (2022)
Hardik Tankaria, Nobuo Yamashita
A Stochastic Variance Reduced Gradient using Barzilai-Borwein Techniques as Second Order Information. CoRR (2022)
Hardik Tankaria, Shinji Sugimoto, Nobuo Yamashita
A regularized limited memory BFGS method for large-scale unconstrained optimization and its efficient implementations. Comput. Optim. Appl. 82 (1) (2022)
Yan Gu, Nobuo Yamashita
An alternating direction method of multipliers with the BFGS update for structured convex quadratic optimization. Comput. Appl. Math. 40 (3) (2021)
Hardik Tankaria, Shinji Sugimoto, Nobuo Yamashita
A Regularized Limited Memory BFGS method for Large-Scale Unconstrained Optimization and its Efficient Implementations. CoRR (2021)
Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita
Proximal gradient methods for multiobjective optimization and their applications. Comput. Optim. Appl. 72 (2) (2019)
Kanako Mita, Ellen H. Fukuda, Nobuo Yamashita
Nonmonotone line searches for unconstrained multiobjective optimization problems. J. Glob. Optim. 75 (1) (2019)
Siti Nor Habibah Binti Hassan, Tomohiro Niimi, Nobuo Yamashita
Augmented Lagrangian Method with Alternating Constraints for Nonlinear Optimization Problems. J. Optim. Theory Appl. 181 (3) (2019)
Shota Yamanaka, Nobuo Yamashita
Duality of nonconvex optimization with positively homogeneous functions. Comput. Optim. Appl. 71 (2) (2018)
Kenji Ueda, Nobuo Yamashita
Erratum to: A regularized Newton method without line search for unconstrained optimization. Comput. Optim. Appl. 66 (1) (2017)
Takayuki Okuno, Shunsuke Hayashi, Nobuo Yamashita, Kensuke Gomoto
An exchange method with refined subproblems for convex semi-infinite programming problems. Optim. Methods Softw. 31 (6) (2016)
Xiaoqin Hua, Nobuo Yamashita
Block coordinate proximal gradient methods with variable Bregman functions for nonsmooth separable optimization. Math. Program. 160 (1-2) (2016)
Xiaoqin Hua, Nobuo Yamashita
Iteration Complexity of a Block Coordinate Gradient Descent Method for Convex Optimization. SIAM J. Optim. 25 (3) (2015)
Kenji Ueda, Nobuo Yamashita
A regularized Newton method without line search for unconstrained optimization. Comput. Optim. Appl. 59 (1-2) (2014)
Xiaojun Chen, Nobuo Yamashita
Preface. Comput. Optim. Appl. 59 (1-2) (2014)
Kenji Ueda, Nobuo Yamashita
Global Complexity Bound Analysis of the Levenberg-Marquardt Method for Nonsmooth Equations and Its Application to the Nonlinear Complementarity Problem. J. Optim. Theory Appl. 152 (2) (2012)
Kenji Ueda, Nobuo Yamashita
On a Global Complexity Bound of the Levenberg-Marquardt Method. J. Optim. Theory Appl. 147 (3) (2010)
Nobuo Yamashita
Sparse quasi-Newton updates with positive definite matrix completion. Math. Program. 115 (1) (2008)
Shunsuke Hayashi, Nobuo Yamashita, Masao Fukushima
A Combined Smoothing and Regularization Method for Monotone Second-Order Cone Complementarity Problems. SIAM J. Optim. 15 (2) (2005)
Nobuo Yamashita, Hiroshige Dan, Masao Fukushima
On the identification of degenerate indices in the nonlinear complementarity problem with the proximal point algorithm. Math. Program. 99 (2) (2004)
Nobuo Yamashita, Zhi-Quan Luo
A nonlinear complementarity approach to multiuser power control for digital subscriber lines. Optim. Methods Softw. 19 (5) (2004)
Dong-Hui Li, Masao Fukushima, Liqun Qi, Nobuo Yamashita
Regularized Newton Methods for Convex Minimization Problems with Singular Solutions. Comput. Optim. Appl. 28 (2) (2004)
Hiroyuki Moriyama, Nobuo Yamashita, Masao Fukushima
The Incremental Gauss-Newton Algorithm with Adaptive Stepsize Rule. Comput. Optim. Appl. 26 (2) (2003)
Hiroshige Dan, Nobuo Yamashita, Masao Fukushima
Convergence Properties of the Inexact Levenberg-Marquardt Method under Local Error Bound Conditions. Optim. Methods Softw. 17 (4) (2002)
Hiroshige Dan, Nobuo Yamashita, Masao Fukushima
A Superlinearly Convergent Algorithm for the Monotone Nonlinear Complementarity Problem Without Uniqueness and Nondegeneracy Conditions. Math. Oper. Res. 27 (4) (2002)
Nobuo Yamashita, Masao Fukushima
The Proximal Point Algorithm with Genuine Superlinear Convergence for the Monotone Complementarity Problem. SIAM J. Optim. 11 (2) (2000)
Paul Tseng, Nobuo Yamashita, Masao Fukushima
Equivalence of Complementarity Problems to Differentiable Minimization: A Unified Approach. SIAM J. Optim. 6 (2) (1996)
Nobuo Yamashita, Masao Fukushima
Modified Newton methods for solving a semismooth reformulation of monotone complementarity problems. Math. Program. 76 (1996)