Modified Newton methods for solving a semismooth reformulation of monotone complementarity problems. - Nobuo Yamashita; Masao Fukushima (Math. Program., 1996)

Equivalence of Complementarity Problems to Differentiable Minimization: A Unified Approach. - Paul Tseng; Nobuo Yamashita; Masao Fukushima (SIAM J. Optim., 1996)

The Proximal Point Algorithm with Genuine Superlinear Convergence for the Monotone Complementarity Problem. - Nobuo Yamashita; Masao Fukushima (SIAM J. Optim., 2000)

A Superlinearly Convergent Algorithm for the Monotone Nonlinear Complementarity Problem Without Uniqueness and Nondegeneracy Conditions. - Hiroshige Dan; Nobuo Yamashita; Masao Fukushima (Math. Oper. Res., 2002)

Convergence Properties of the Inexact Levenberg-Marquardt Method under Local Error Bound Conditions. - Hiroshige Dan; Nobuo Yamashita; Masao Fukushima (Optim. Methods Softw., 2002)

The Incremental Gauss-Newton Algorithm with Adaptive Stepsize Rule. - Hiroyuki Moriyama; Nobuo Yamashita; Masao Fukushima (Comput. Optim. Appl., 2003)

Regularized Newton Methods for Convex Minimization Problems with Singular Solutions. - Dong-Hui Li; Masao Fukushima; Liqun Qi 001; Nobuo Yamashita (Comput. Optim. Appl., 2004)

On the identification of degenerate indices in the nonlinear complementarity problem with the proximal point algorithm. - Nobuo Yamashita; Hiroshige Dan; Masao Fukushima (Math. Program., 2004)

A nonlinear complementarity approach to multiuser power control for digital subscriber lines. - Nobuo Yamashita; Zhi-Quan Luo (Optim. Methods Softw., 2004)

A Combined Smoothing and Regularization Method for Monotone Second-Order Cone Complementarity Problems. - Shunsuke Hayashi; Nobuo Yamashita; Masao Fukushima (SIAM J. Optim., 2005)

Sparse quasi-Newton updates with positive definite matrix completion. - Nobuo Yamashita (Math. Program., 2008)

On a Global Complexity Bound of the Levenberg-Marquardt Method. - Kenji Ueda; Nobuo Yamashita (J. Optim. Theory Appl., 2010)

Global Complexity Bound Analysis of the Levenberg-Marquardt Method for Nonsmooth Equations and Its Application to the Nonlinear Complementarity Problem. - Kenji Ueda; Nobuo Yamashita (J. Optim. Theory Appl., 2012)

Preface. - Xiaojun Chen 001; Nobuo Yamashita (Comput. Optim. Appl., 2014)

A regularized Newton method without line search for unconstrained optimization. - Kenji Ueda; Nobuo Yamashita (Comput. Optim. Appl., 2014)

Iteration Complexity of a Block Coordinate Gradient Descent Method for Convex Optimization. - Xiaoqin Hua 001; Nobuo Yamashita (SIAM J. Optim., 2015)

Block coordinate proximal gradient methods with variable Bregman functions for nonsmooth separable optimization. - Xiaoqin Hua 001; Nobuo Yamashita (Math. Program., 2016)

An exchange method with refined subproblems for convex semi-infinite programming problems. - Takayuki Okuno; Shunsuke Hayashi; Nobuo Yamashita; Kensuke Gomoto (Optim. Methods Softw., 2016)

Erratum to: A regularized Newton method without line search for unconstrained optimization. - Kenji Ueda; Nobuo Yamashita (Comput. Optim. Appl., 2017)

Duality of nonconvex optimization with positively homogeneous functions. - Shota Yamanaka; Nobuo Yamashita (Comput. Optim. Appl., 2018)

Proximal gradient methods for multiobjective optimization and their applications. - Hiroki Tanabe; Ellen H. Fukuda; Nobuo Yamashita (Comput. Optim. Appl., 2019)

Nonmonotone line searches for unconstrained multiobjective optimization problems. - Kanako Mita; Ellen H. Fukuda; Nobuo Yamashita (J. Glob. Optim., 2019)

Augmented Lagrangian Method with Alternating Constraints for Nonlinear Optimization Problems. - Siti Nor Habibah Binti Hassan; Tomohiro Niimi; Nobuo Yamashita (J. Optim. Theory Appl., 2019)

An alternating direction method of multipliers with the BFGS update for structured convex quadratic optimization. - Yan Gu; Nobuo Yamashita (Comput. Appl. Math., 2021)

A regularized limited memory BFGS method for large-scale unconstrained optimization and its efficient implementations. - Hardik Tankaria; Shinji Sugimoto; Nobuo Yamashita (Comput. Optim. Appl., 2022)

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 (CoRR, 2022)

A Stochastic Variance Reduced Gradient using Barzilai-Borwein Techniques as Second Order Information. - Hardik Tankaria; Nobuo Yamashita (CoRR, 2022)

Convergence analysis and acceleration of the smoothing methods for solving extensive-form games. - Keigo Habara; Ellen Hidemi Fukuda; Nobuo Yamashita (CoRR, 2023)

Convergence rates analysis of a multiobjective proximal gradient method. - Hiroki Tanabe; Ellen H. Fukuda; Nobuo Yamashita (Optim. Lett., 2023)

Distributionally robust expected residual minimization for stochastic variational inequality problems. - Atsushi Hori; Yuya Yamakawa; Nobuo Yamashita (Optim. Methods Softw., 2023)

An accelerated proximal gradient method for multiobjective optimization. - Hiroki Tanabe; Ellen H. Fukuda; Nobuo Yamashita (Comput. Optim. Appl., 2023)

An equivalent nonlinear optimization model with triangular low-rank factorization for semidefinite programs. - Yuya Yamakawa; Tetsuya Ikegami; Ellen H. Fukuda; Nobuo Yamashita (Optim. Methods Softw., 2023)

Convergence properties of Levenberg-Marquardt methods with generalized regularization terms. - Shumpei Ariizumi; Yuya Yamakawa; Nobuo Yamashita (Appl. Math. Comput., 2024)

Convergence analysis of a regularized Newton method with generalized regularization terms for unconstrained convex optimization problems. - Yuya Yamakawa; Nobuo Yamashita (Appl. Math. Comput., 2025)