An Ergodic Algorithm for the Power-Control Games for CDMA Data Networks. - Hideaki Iiduka; Isao Yamada (J. Math. Model. Algorithms, 2009)

A Use of Conjugate Gradient Direction for the Convex Optimization Problem over the Fixed Point Set of a Nonexpansive Mapping. - Hideaki Iiduka; Isao Yamada (SIAM J. Optim., 2009)

Three-term conjugate gradient method for the convex optimization problem over the fixed point set of a nonexpansive mapping. - Hideaki Iiduka (Appl. Math. Comput., 2011)

Fixed Point Optimization Algorithms for Network Bandwidth Allocation Problems with Compoundable Constraints. - Hideaki Iiduka; Masato Uchida (IEEE Commun. Lett., 2011)

Iterative Algorithm for Solving Triple-Hierarchical Constrained Optimization Problem. - Hideaki Iiduka (J. Optim. Theory Appl., 2011)

Decentralized Algorithm for Centralized Variational Inequalities in Network Resource Allocation. - Hideaki Iiduka (J. Optim. Theory Appl., 2011)

Fixed point optimization algorithm and its application to network bandwidth allocation. - Hideaki Iiduka (J. Comput. Appl. Math., 2012)

Fixed point optimization algorithm and its application to power control in CDMA data networks. - Hideaki Iiduka (Math. Program., 2012)

Conjugate gradient methods using value of objective function for unconstrained optimization. - Hideaki Iiduka; Yasushi Narushima (Optim. Lett., 2012)

Computational Method for Solving a Stochastic Linear-Quadratic Control Problem Given an Unsolvable Stochastic Algebraic Riccati Equation. - Hideaki Iiduka; Isao Yamada (SIAM J. Control. Optim., 2012)

Iterative Algorithm for Triple-Hierarchical Constrained Nonconvex Optimization Problem and Its Application to Network Bandwidth Allocation. - Hideaki Iiduka (SIAM J. Optim., 2012)

Fixed Point Optimization Algorithms for Distributed Optimization in Networked Systems. - Hideaki Iiduka (SIAM J. Optim., 2013)

Acceleration Method Combining Broadcast and Incremental Distributed Optimization Algorithms. - Hideaki Iiduka; Kazuhiro Hishinuma (SIAM J. Optim., 2014)

Modeling User Behavior in P2P Data Storage System. - Masato Uchida; Hideaki Iiduka; Isao Sugino (IEICE Trans. Commun., 2015)

Convex optimization over fixed point sets of quasi-nonexpansive and nonexpansive mappings in utility-based bandwidth allocation problems with operational constraints. - Hideaki Iiduka (J. Comput. Appl. Math., 2015)

Acceleration method for convex optimization over the fixed point set of a nonexpansive mapping. - Hideaki Iiduka (Math. Program., 2015)

Proximal point algorithms for nonsmooth convex optimization with fixed point constraints. - Hideaki Iiduka (Eur. J. Oper. Res., 2016)

Recursive-Rule Extraction Algorithm With J48graft And Applications To Generating Credit Scores. - Yoichi Hayashi; Yuki Tanaka; Tomohiro Takagi; Takamichi Saito; Hideaki Iiduka; Hiroaki Kikuchi; Guido Bologna; Sushmita Mitra (J. Artif. Intell. Soft Comput. Res., 2016)

Convergence analysis of iterative methods for nonsmooth convex optimization over fixed point sets of quasi-nonexpansive mappings. - Hideaki Iiduka (Math. Program., 2016)

Incremental subgradient method for nonsmooth convex optimization with fixed point constraints. - Hideaki Iiduka (Optim. Methods Softw., 2016)

Optimality and convergence for convex ensemble learning with sparsity and diversity based on fixed point optimization. - Yoichi Hayashi; Hideaki Iiduka (Neurocomputing, 2018)

Incremental and Parallel Machine Learning Algorithms With Automated Learning Rate Adjustments. - Kazuhiro Hishinuma; Hideaki Iiduka (Frontiers Robotics AI, 2019)

Two stochastic optimization algorithms for convex optimization with fixed point constraints. - Hideaki Iiduka (Optim. Methods Softw., 2019)

Distributed Optimization for Network Resource Allocation With Nonsmooth Utility Functions. - Hideaki Iiduka (IEEE Trans. Control. Netw. Syst., 2019)

Hybrid Riemannian conjugate gradient methods with global convergence properties. - Hiroyuki Sakai 007; Hideaki Iiduka (Comput. Optim. Appl., 2020)

Conjugate-gradient-based Adam for stochastic optimization and its application to deep learning. - Yu Kobayashi; Hideaki Iiduka (CoRR, 2020)

Fixed point quasiconvex subgradient method. - Kazuhiro Hishinuma; Hideaki Iiduka (Eur. J. Oper. Res., 2020)

Stochastic Fixed Point Optimization Algorithm for Classifier Ensemble. - Hideaki Iiduka (IEEE Trans. Cybern., 2020)

Unified Algorithm Framework for Nonconvex Stochastic Optimization in Deep Neural Networks. - Yini Zhu; Hideaki Iiduka (IEEE Access, 2021)

The Number of Steps Needed for Nonconvex Optimization of a Deep Learning Optimizer is a Rational Function of Batch Size. - Hideaki Iiduka (CoRR, 2021)

Minimization of Stochastic First-order Oracle Complexity of Adaptive Methods for Nonconvex Optimization. - Hideaki Iiduka (CoRR, 2021)

Inexact stochastic subgradient projection method for stochastic equilibrium problems with nonmonotone bifunctions: application to expected risk minimization in machine learning. - Hideaki Iiduka (J. Glob. Optim., 2021)

Sufficient Descent Riemannian Conjugate Gradient Methods. - Hiroyuki Sakai 007; Hideaki Iiduka (J. Optim. Theory Appl., 2021)

Using Constant Learning Rate of Two Time-Scale Update Rule for Training Generative Adversarial Networks. - Naoki Sato; Hideaki Iiduka (CoRR, 2022)

Critical Bach Size Minimizes Stochastic First-Order Oracle Complexity of Deep Learning Optimizer using Hyperparameters Close to One. - Hideaki Iiduka (CoRR, 2022)

Riemannian stochastic fixed point optimization algorithm. - Hideaki Iiduka; Hiroyuki Sakai 007 (Numer. Algorithms, 2022)

Appropriate Learning Rates of Adaptive Learning Rate Optimization Algorithms for Training Deep Neural Networks. - Hideaki Iiduka (IEEE Trans. Cybern., 2022)

Riemannian Adaptive Optimization Algorithm and its Application to Natural Language Processing. - Hiroyuki Sakai 007; Hideaki Iiduka (IEEE Trans. Cybern., 2022)

Conjugate Gradient Method for Generative Adversarial Networks. - Hiroki Naganuma; Hideaki Iiduka (AISTATS, 2023)

Existence and Estimation of Critical Batch Size for Training Generative Adversarial Networks with Two Time-Scale Update Rule. - Naoki Sato; Hideaki Iiduka (ICML, 2023)

Global convergence of Hager-Zhang type Riemannian conjugate gradient method. - Hiroyuki Sakai 007; Hiroyuki Sato; Hideaki Iiduka (Appl. Math. Comput., 2023)

Relationship between Batch Size and Number of Steps Needed for Nonconvex Optimization of Stochastic Gradient Descent using Armijo Line Search. - Yuki Tsukada; Hideaki Iiduka (CoRR, 2023)

Using Stochastic Gradient Descent to Smooth Nonconvex Functions: Analysis of Implicit Graduated Optimization with Optimal Noise Scheduling. - Naoki Sato; Hideaki Iiduka (CoRR, 2023)

ϵ-Approximation of Adaptive Leaning Rate Optimization Algorithms for Constrained Nonconvex Stochastic Optimization. - Hideaki Iiduka (IEEE Trans. Neural Networks Learn. Syst., 2023)

Iteration and Stochastic First-order Oracle Complexities of Stochastic Gradient Descent using Constant and Decaying Learning Rates. - Kento Imaizumi; Hideaki Iiduka (CoRR, 2024)

Convergence of Sharpness-Aware Minimization Algorithms using Increasing Batch Size and Decaying Learning Rate. - Hinata Harada; Hideaki Iiduka (CoRR, 2024)

Scaled Conjugate Gradient Method for Nonconvex Optimization in Deep Neural Networks. - Naoki Sato; Koshiro Izumi; Hideaki Iiduka (CoRR, 2024)

Theoretical analysis of Adam using hyperparameters close to one without Lipschitz smoothness. - Hideaki Iiduka (Numer. Algorithms, 2024)

Modified Memoryless Spectral-Scaling Broyden Family on Riemannian Manifolds. - Hiroyuki Sakai 007; Hideaki Iiduka (J. Optim. Theory Appl., 2024)

Explicit and Implicit Graduated Optimization in Deep Neural Networks. - Naoki Sato; Hideaki Iiduka (AAAI, 2025)

A general framework of Riemannian adaptive optimization methods with a convergence analysis. - Hiroyuki Sakai 007; Hideaki Iiduka (Trans. Mach. Learn. Res., 2025)

Relationship between Batch Size and Number of Steps Needed for Nonconvex Optimization of Stochastic Gradient Descent using Armijo-Line-Search Learning Rate. - Yuki Tsukada; Hideaki Iiduka (Trans. Mach. Learn. Res., 2025)

Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent. - Hikaru Umeda; Hideaki Iiduka (Trans. Mach. Learn. Res., 2025)

Increasing Batch Size Improves Convergence of Stochastic Gradient Descent with Momentum. - Keisuke Kamo; Hideaki Iiduka (CoRR, 2025)

Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size. - Kanata Oowada; Hideaki Iiduka (CoRR, 2025)

Both Asymptotic and Non-Asymptotic Convergence of Quasi-Hyperbolic Momentum using Increasing Batch Size. - Kento Imaizumi; Hideaki Iiduka (CoRR, 2025)

Analysis of Muon's Convergence and Critical Batch Size. - Naoki Sato; Hiroki Naganuma; Hideaki Iiduka (CoRR, 2025)

Accelerating SGDM via Learning Rate and Batch Size Schedules: A Lyapunov-Based Analysis. - Yuichi Kondo; Hideaki Iiduka (CoRR, 2025)

Optimal Growth Schedules for Batch Size and Learning Rate in SGD that Reduce SFO Complexity. - Hikaru Umeda; Hideaki Iiduka (CoRR, 2025)

Adaptive Batch Size and Learning Rate Scheduler for Stochastic Gradient Descent Based on Minimization of Stochastic First-order Oracle Complexity. - Hikaru Umeda; Hideaki Iiduka (CoRR, 2025)