Hideaki Iiduka

70 papers A* 2A 1Journal 67

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Improved Convergence Rates of Muon Optimizer for Nonconvex Optimization. CoRR Shuntaro Nagashima, Hideaki Iiduka
2026	J	`jnl`	Lipschitz Multiscale Deep Equilibrium Models: A Theoretically Guaranteed and Accelerated Approach. CoRR Naoki Sato, Hideaki Iiduka
2026	J	`jnl`	Muon Converges under Heavy-Tailed Noise: Nonconvex Hölder-Smooth Empirical Risk Minimization. CoRR Hideaki Iiduka
2025	J	`jnl`	A general framework of Riemannian adaptive optimization methods with a convergence analysis. Trans. Mach. Learn. Res. Hiroyuki Sakai, Hideaki Iiduka
2025	J	`jnl`	Accelerating SGDM via Learning Rate and Batch Size Schedules: A Lyapunov-Based Analysis. CoRR Yuichi Kondo, Hideaki Iiduka
2025	J	`jnl`	Adaptive Batch Size and Learning Rate Scheduler for Stochastic Gradient Descent Based on Minimization of Stochastic First-order Oracle Complexity. CoRR Hikaru Umeda, Hideaki Iiduka
2025	J	`jnl`	Analysis of Muon's Convergence and Critical Batch Size. CoRR Naoki Sato, Hiroki Naganuma, Hideaki Iiduka
2025	J	`jnl`	Both Asymptotic and Non-Asymptotic Convergence of Quasi-Hyperbolic Momentum using Increasing Batch Size. CoRR Kento Imaizumi, Hideaki Iiduka
2025	J	`jnl`	Convergence Analysis of SGD under Expected Smoothness. CoRR Yuta Kawamoto, Hideaki Iiduka
2025	A*	`conf`	Explicit and Implicit Graduated Optimization in Deep Neural Networks. AAAI Naoki Sato, Hideaki Iiduka
2025	J	`jnl`	Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size. CoRR Kanata Oowada, Hideaki Iiduka
2025	J	`jnl`	Increasing Batch Size Improves Convergence of Stochastic Gradient Descent with Momentum. CoRR Keisuke Kamo, Hideaki Iiduka
2025	J	`jnl`	Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent. Trans. Mach. Learn. Res. Hikaru Umeda, Hideaki Iiduka
2025	J	`jnl`	Optimal Growth Schedules for Batch Size and Learning Rate in SGD that Reduce SFO Complexity. CoRR Hikaru Umeda, Hideaki Iiduka
2025	J	`jnl`	Relationship between Batch Size and Number of Steps Needed for Nonconvex Optimization of Stochastic Gradient Descent using Armijo-Line-Search Learning Rate. Trans. Mach. Learn. Res. Yuki Tsukada, Hideaki Iiduka
2024	J	`jnl`	Convergence of Sharpness-Aware Minimization Algorithms using Increasing Batch Size and Decaying Learning Rate. CoRR Hinata Harada, Hideaki Iiduka
2024	J	`jnl`	Explicit and Implicit Graduated Optimization in Deep Neural Networks. CoRR Naoki Sato, Hideaki Iiduka
2024	J	`jnl`	Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent. CoRR Hikaru Umeda, Hideaki Iiduka
2024	J	`jnl`	Iteration and Stochastic First-order Oracle Complexities of Stochastic Gradient Descent using Constant and Decaying Learning Rates. CoRR Kento Imaizumi, Hideaki Iiduka
2024	J	`jnl`	Modified Memoryless Spectral-Scaling Broyden Family on Riemannian Manifolds. J. Optim. Theory Appl. Hiroyuki Sakai, Hideaki Iiduka
2024	J	`jnl`	Scaled Conjugate Gradient Method for Nonconvex Optimization in Deep Neural Networks. CoRR Naoki Sato, Koshiro Izumi, Hideaki Iiduka
2024	J	`jnl`	Scaled Conjugate Gradient Method for Nonconvex Optimization in Deep Neural Networks. J. Mach. Learn. Res. Naoki Sato, Koshiro Izumi, Hideaki Iiduka
2024	J	`jnl`	Theoretical analysis of Adam using hyperparameters close to one without Lipschitz smoothness. Numer. Algorithms Hideaki Iiduka
2023	A	`conf`	Conjugate Gradient Method for Generative Adversarial Networks. AISTATS Hiroki Naganuma, Hideaki Iiduka
2023	A*	`conf`	Existence and Estimation of Critical Batch Size for Training Generative Adversarial Networks with Two Time-Scale Update Rule. ICML Naoki Sato, Hideaki Iiduka
2023	J	`jnl`	Global convergence of Hager-Zhang type Riemannian conjugate gradient method. Appl. Math. Comput. Hiroyuki Sakai, Hiroyuki Sato, Hideaki Iiduka
2023	J	`jnl`	Modified memoryless spectral-scaling Broyden family on Riemannian manifolds. CoRR Hiroyuki Sakai, Hideaki Iiduka
2023	J	`jnl`	Relationship between Batch Size and Number of Steps Needed for Nonconvex Optimization of Stochastic Gradient Descent using Armijo Line Search. CoRR Yuki Tsukada, Hideaki Iiduka
2023	J	`jnl`	Using Stochastic Gradient Descent to Smooth Nonconvex Functions: Analysis of Implicit Graduated Optimization with Optimal Noise Scheduling. CoRR Naoki Sato, Hideaki Iiduka
2023	J	`jnl`	ϵ-Approximation of Adaptive Leaning Rate Optimization Algorithms for Constrained Nonconvex Stochastic Optimization. IEEE Trans. Neural Networks Learn. Syst. Hideaki Iiduka
2022	J	`jnl`	Appropriate Learning Rates of Adaptive Learning Rate Optimization Algorithms for Training Deep Neural Networks. IEEE Trans. Cybern. Hideaki Iiduka
2022	J	`jnl`	Conjugate Gradient Method for Generative Adversarial Networks. CoRR Hiroki Naganuma, Hideaki Iiduka
2022	J	`jnl`	Critical Bach Size Minimizes Stochastic First-Order Oracle Complexity of Deep Learning Optimizer using Hyperparameters Close to One. CoRR Hideaki Iiduka
2022	J	`jnl`	Riemannian Adaptive Optimization Algorithm and its Application to Natural Language Processing. IEEE Trans. Cybern. Hiroyuki Sakai, Hideaki Iiduka
2022	J	`jnl`	Riemannian stochastic fixed point optimization algorithm. Numer. Algorithms Hideaki Iiduka, Hiroyuki Sakai
2022	J	`jnl`	Theoretical analysis of Adam using hyperparameters close to one without Lipschitz smoothness. CoRR Hideaki Iiduka
2022	J	`jnl`	Using Constant Learning Rate of Two Time-Scale Update Rule for Training Generative Adversarial Networks. CoRR Naoki Sato, Hideaki Iiduka
2021	J	`jnl`	Inexact stochastic subgradient projection method for stochastic equilibrium problems with nonmonotone bifunctions: application to expected risk minimization in machine learning. J. Glob. Optim. Hideaki Iiduka
2021	J	`jnl`	Minimization of Stochastic First-order Oracle Complexity of Adaptive Methods for Nonconvex Optimization. CoRR Hideaki Iiduka
2021	J	`jnl`	Sufficient Descent Riemannian Conjugate Gradient Methods. J. Optim. Theory Appl. Hiroyuki Sakai, Hideaki Iiduka
2021	J	`jnl`	The Number of Steps Needed for Nonconvex Optimization of a Deep Learning Optimizer is a Rational Function of Batch Size. CoRR Hideaki Iiduka
2021	J	`jnl`	Unified Algorithm Framework for Nonconvex Stochastic Optimization in Deep Neural Networks. IEEE Access Yini Zhu, Hideaki Iiduka
2020	J	`jnl`	Conjugate-gradient-based Adam for stochastic optimization and its application to deep learning. CoRR Yu Kobayashi, Hideaki Iiduka
2020	J	`jnl`	Fixed point quasiconvex subgradient method. Eur. J. Oper. Res. Kazuhiro Hishinuma, Hideaki Iiduka
2020	J	`jnl`	Hybrid Riemannian conjugate gradient methods with global convergence properties. Comput. Optim. Appl. Hiroyuki Sakai, Hideaki Iiduka
2020	J	`jnl`	Stochastic Fixed Point Optimization Algorithm for Classifier Ensemble. IEEE Trans. Cybern. Hideaki Iiduka
2019	J	`jnl`	Distributed Optimization for Network Resource Allocation With Nonsmooth Utility Functions. IEEE Trans. Control. Netw. Syst. Hideaki Iiduka
2019	J	`jnl`	Incremental and Parallel Machine Learning Algorithms With Automated Learning Rate Adjustments. Frontiers Robotics AI Kazuhiro Hishinuma, Hideaki Iiduka
2019	J	`jnl`	Two stochastic optimization algorithms for convex optimization with fixed point constraints. Optim. Methods Softw. Hideaki Iiduka
2018	J	`jnl`	Optimality and convergence for convex ensemble learning with sparsity and diversity based on fixed point optimization. Neurocomputing Yoichi Hayashi, Hideaki Iiduka
2016	J	`jnl`	Convergence analysis of iterative methods for nonsmooth convex optimization over fixed point sets of quasi-nonexpansive mappings. Math. Program. Hideaki Iiduka
2016	J	`jnl`	Incremental subgradient method for nonsmooth convex optimization with fixed point constraints. Optim. Methods Softw. Hideaki Iiduka
2016	J	`jnl`	Proximal point algorithms for nonsmooth convex optimization with fixed point constraints. Eur. J. Oper. Res. Hideaki Iiduka
2016	J	`jnl`	Recursive-Rule Extraction Algorithm With J48graft And Applications To Generating Credit Scores. J. Artif. Intell. Soft Comput. Res. Yoichi Hayashi, Yuki Tanaka, Tomohiro Takagi, Takamichi Saito, Hideaki Iiduka, Hiroaki Kikuchi, Guido Bologna, Sushmita Mitra
2015	J	`jnl`	Acceleration method for convex optimization over the fixed point set of a nonexpansive mapping. Math. Program. Hideaki Iiduka
2015	J	`jnl`	Convex optimization over fixed point sets of quasi-nonexpansive and nonexpansive mappings in utility-based bandwidth allocation problems with operational constraints. J. Comput. Appl. Math. Hideaki Iiduka
2015	J	`jnl`	Modeling User Behavior in P2P Data Storage System. IEICE Trans. Commun. Masato Uchida, Hideaki Iiduka, Isao Sugino
2014	J	`jnl`	Acceleration Method Combining Broadcast and Incremental Distributed Optimization Algorithms. SIAM J. Optim. Hideaki Iiduka, Kazuhiro Hishinuma
2013	J	`jnl`	Fixed Point Optimization Algorithms for Distributed Optimization in Networked Systems. SIAM J. Optim. Hideaki Iiduka
2012	J	`jnl`	Computational Method for Solving a Stochastic Linear-Quadratic Control Problem Given an Unsolvable Stochastic Algebraic Riccati Equation. SIAM J. Control. Optim. Hideaki Iiduka, Isao Yamada
2012	J	`jnl`	Conjugate gradient methods using value of objective function for unconstrained optimization. Optim. Lett. Hideaki Iiduka, Yasushi Narushima
2012	J	`jnl`	Fixed point optimization algorithm and its application to network bandwidth allocation. J. Comput. Appl. Math. Hideaki Iiduka
2012	J	`jnl`	Fixed point optimization algorithm and its application to power control in CDMA data networks. Math. Program. Hideaki Iiduka
2012	J	`jnl`	Iterative Algorithm for Triple-Hierarchical Constrained Nonconvex Optimization Problem and Its Application to Network Bandwidth Allocation. SIAM J. Optim. Hideaki Iiduka
2011	J	`jnl`	Decentralized Algorithm for Centralized Variational Inequalities in Network Resource Allocation. J. Optim. Theory Appl. Hideaki Iiduka
2011	J	`jnl`	Fixed Point Optimization Algorithms for Network Bandwidth Allocation Problems with Compoundable Constraints. IEEE Commun. Lett. Hideaki Iiduka, Masato Uchida
2011	J	`jnl`	Iterative Algorithm for Solving Triple-Hierarchical Constrained Optimization Problem. J. Optim. Theory Appl. Hideaki Iiduka
2011	J	`jnl`	Three-term conjugate gradient method for the convex optimization problem over the fixed point set of a nonexpansive mapping. Appl. Math. Comput. Hideaki Iiduka
2009	J	`jnl`	A Use of Conjugate Gradient Direction for the Convex Optimization Problem over the Fixed Point Set of a Nonexpansive Mapping. SIAM J. Optim. Hideaki Iiduka, Isao Yamada
2009	J	`jnl`	An Ergodic Algorithm for the Power-Control Games for CDMA Data Networks. J. Math. Model. Algorithms Hideaki Iiduka, Isao Yamada