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ORCIDNobuo Yamashita
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Journal Articles
- 2024
[j30]Shumpei Ariizumi, Yuya Yamakawa
, Nobuo Yamashita:
Convergence properties of Levenberg-Marquardt methods with generalized regularization terms. Appl. Math. Comput. 463: 128365 (2024)- 2023
- [j29]Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita:
An accelerated proximal gradient method for multiobjective optimization. Comput. Optim. Appl. 86(2): 421-455 (2023) - [j28]Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita:
Convergence rates analysis of a multiobjective proximal gradient method. Optim. Lett. 17(2): 333-350 (2023) - [j27]Atsushi Hori, Yuya Yamakawa, Nobuo Yamashita:
Distributionally robust expected residual minimization for stochastic variational inequality problems. Optim. Methods Softw. 38(4): 756-780 (2023) - [j26]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): 1296-1310 (2023) - 2022
- [j25]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): 61-88 (2022) - 2021
- [j24]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) - 2019
- [j23]Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita:
Proximal gradient methods for multiobjective optimization and their applications. Comput. Optim. Appl. 72(2): 339-361 (2019) - [j22]Kanako Mita, Ellen H. Fukuda, Nobuo Yamashita:
Nonmonotone line searches for unconstrained multiobjective optimization problems. J. Glob. Optim. 75(1): 63-90 (2019) - [j21]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): 883-904 (2019) - 2018
- [j20]Shota Yamanaka, Nobuo Yamashita:
Duality of nonconvex optimization with positively homogeneous functions. Comput. Optim. Appl. 71(2): 435-456 (2018) - 2017
- [j19]Kenji Ueda, Nobuo Yamashita:
Erratum to: A regularized Newton method without line search for unconstrained optimization. Comput. Optim. Appl. 66(1): 219-222 (2017) - 2016
- [j18]Xiaoqin Hua, Nobuo Yamashita:
Block coordinate proximal gradient methods with variable Bregman functions for nonsmooth separable optimization. Math. Program. 160(1-2): 1-32 (2016) - [j17]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): 1305-1324 (2016) - 2015
- [j16]Xiaoqin Hua, Nobuo Yamashita:
Iteration Complexity of a Block Coordinate Gradient Descent Method for Convex Optimization. SIAM J. Optim. 25(3): 1298-1313 (2015) - 2014
- [j15]Xiaojun Chen, Nobuo Yamashita:
Preface. Comput. Optim. Appl. 59(1-2): 1-4 (2014) - [j14]Kenji Ueda, Nobuo Yamashita:
A regularized Newton method without line search for unconstrained optimization. Comput. Optim. Appl. 59(1-2): 321-351 (2014) - 2012
- [j13]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): 450-467 (2012) - 2010
- [j12]Kenji Ueda, Nobuo Yamashita:
On a Global Complexity Bound of the Levenberg-Marquardt Method. J. Optim. Theory Appl. 147(3): 443-453 (2010) - 2008
- [j11]Nobuo Yamashita:
Sparse quasi-Newton updates with positive definite matrix completion. Math. Program. 115(1): 1-30 (2008) - 2005
- [j10]Shunsuke Hayashi, Nobuo Yamashita, Masao Fukushima:
A Combined Smoothing and Regularization Method for Monotone Second-Order Cone Complementarity Problems. SIAM J. Optim. 15(2): 593-615 (2005) - 2004
- [j9]Dong-Hui Li, Masao Fukushima, Liqun Qi, Nobuo Yamashita:
Regularized Newton Methods for Convex Minimization Problems with Singular Solutions. Comput. Optim. Appl. 28(2): 131-147 (2004) - [j8]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): 377-397 (2004) - [j7]Nobuo Yamashita, Zhi-Quan Luo:
A nonlinear complementarity approach to multiuser power control for digital subscriber lines. Optim. Methods Softw. 19(5): 633-652 (2004) - 2003
- [j6]Hiroyuki Moriyama, Nobuo Yamashita, Masao Fukushima:
The Incremental Gauss-Newton Algorithm with Adaptive Stepsize Rule. Comput. Optim. Appl. 26(2): 107-141 (2003) - 2002
- [j5]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): 743-753 (2002) - [j4]Hiroshige Dan, Nobuo Yamashita, Masao Fukushima:
Convergence Properties of the Inexact Levenberg-Marquardt Method under Local Error Bound Conditions. Optim. Methods Softw. 17(4): 605-626 (2002) - 2000
- [j3]Nobuo Yamashita, Masao Fukushima:
The Proximal Point Algorithm with Genuine Superlinear Convergence for the Monotone Complementarity Problem. SIAM J. Optim. 11(2): 364-379 (2000) - 1996
- [j2]Nobuo Yamashita, Masao Fukushima:
Modified Newton methods for solving a semismooth reformulation of monotone complementarity problems. Math. Program. 76: 469-491 (1996) - [j1]Paul Tseng, Nobuo Yamashita, Masao Fukushima:
Equivalence of Complementarity Problems to Differentiable Minimization: A Unified Approach. SIAM J. Optim. 6(2): 446-460 (1996)
Informal and Other Publications
- 2023
- [i5]Keigo Habara, Ellen Hidemi Fukuda, Nobuo Yamashita:
Convergence analysis and acceleration of the smoothing methods for solving extensive-form games. CoRR abs/2303.11046 (2023) - 2022
- [i4]Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita:
An accelerated proximal gradient method for multiobjective optimization. CoRR abs/2202.10994 (2022) - [i3]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 abs/2205.05262 (2022) - [i2]Hardik Tankaria, Nobuo Yamashita:
A Stochastic Variance Reduced Gradient using Barzilai-Borwein Techniques as Second Order Information. CoRR abs/2208.11075 (2022) - 2021
- [i1]Hardik Tankaria, Shinji Sugimoto, Nobuo Yamashita:
A Regularized Limited Memory BFGS method for Large-Scale Unconstrained Optimization and its Efficient Implementations. CoRR abs/2101.04413 (2021)
Coauthor Index
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