Hideaki Iiduka

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 38

Top Topics

Top Venues

IEEE Trans. Cybern.

J. Optim. Theory Appl.

Numer. Algorithms

Publications

Naoki Sato, Hideaki Iiduka
Role of Momentum in Smoothing Objective Function in Implicit Graduated Optimization. CoRR (2024)
Hiroyuki Sakai, Hideaki Iiduka
Modified Memoryless Spectral-Scaling Broyden Family on Riemannian Manifolds. J. Optim. Theory Appl. 202 (2) (2024)
Kento Imaizumi, Hideaki Iiduka
Iteration and Stochastic First-order Oracle Complexities of Stochastic Gradient Descent using Constant and Decaying Learning Rates. CoRR (2024)
Hideaki Iiduka
Theoretical analysis of Adam using hyperparameters close to one without Lipschitz smoothness. Numer. Algorithms 95 (1) (2024)
Hideaki Iiduka
ϵ-Approximation of Adaptive Leaning Rate Optimization Algorithms for Constrained Nonconvex Stochastic Optimization. IEEE Trans. Neural Networks Learn. Syst. 34 (10) (2023)
Hiroyuki Sakai, Hideaki Iiduka
Modified memoryless spectral-scaling Broyden family on Riemannian manifolds. CoRR (2023)
Hiroyuki Sakai, Hiroyuki Sato, Hideaki Iiduka
Global convergence of Hager-Zhang type Riemannian conjugate gradient method. Appl. Math. Comput. 441 (2023)
Naoki Sato, Hideaki Iiduka
Existence and Estimation of Critical Batch Size for Training Generative Adversarial Networks with Two Time-Scale Update Rule. ICML (2023)
Hiroki Naganuma, Hideaki Iiduka
Conjugate Gradient Method for Generative Adversarial Networks. AISTATS (2023)
Yuki Tsukada, Hideaki Iiduka
Relationship between Batch Size and Number of Steps Needed for Nonconvex Optimization of Stochastic Gradient Descent using Armijo Line Search. CoRR (2023)
Naoki Sato, Hideaki Iiduka
Using Stochastic Gradient Descent to Smooth Nonconvex Functions: Analysis of Implicit Graduated Optimization with Optimal Noise Scheduling. CoRR (2023)
Hideaki Iiduka, Hiroyuki Sakai
Riemannian stochastic fixed point optimization algorithm. Numer. Algorithms 90 (4) (2022)
Naoki Sato, Hideaki Iiduka
Using Constant Learning Rate of Two Time-Scale Update Rule for Training Generative Adversarial Networks. CoRR (2022)
Hiroyuki Sakai, Hideaki Iiduka
Riemannian Adaptive Optimization Algorithm and its Application to Natural Language Processing. IEEE Trans. Cybern. 52 (8) (2022)
Hiroki Naganuma, Hideaki Iiduka
Conjugate Gradient Method for Generative Adversarial Networks. CoRR (2022)
Hideaki Iiduka
Appropriate Learning Rates of Adaptive Learning Rate Optimization Algorithms for Training Deep Neural Networks. IEEE Trans. Cybern. 52 (12) (2022)
Hideaki Iiduka
Critical Bach Size Minimizes Stochastic First-Order Oracle Complexity of Deep Learning Optimizer using Hyperparameters Close to One. CoRR (2022)
Hideaki Iiduka
Theoretical analysis of Adam using hyperparameters close to one without Lipschitz smoothness. CoRR (2022)
Hideaki Iiduka
Minimization of Stochastic First-order Oracle Complexity of Adaptive Methods for Nonconvex Optimization. CoRR (2021)
Hideaki Iiduka
Inexact stochastic subgradient projection method for stochastic equilibrium problems with nonmonotone bifunctions: application to expected risk minimization in machine learning. J. Glob. Optim. 80 (2) (2021)
Yini Zhu, Hideaki Iiduka
Unified Algorithm Framework for Nonconvex Stochastic Optimization in Deep Neural Networks. IEEE Access 9 (2021)
Hiroyuki Sakai, Hideaki Iiduka
Sufficient Descent Riemannian Conjugate Gradient Methods. J. Optim. Theory Appl. 190 (1) (2021)
Hideaki Iiduka
The Number of Steps Needed for Nonconvex Optimization of a Deep Learning Optimizer is a Rational Function of Batch Size. CoRR (2021)
Yu Kobayashi, Hideaki Iiduka
Conjugate-gradient-based Adam for stochastic optimization and its application to deep learning. CoRR (2020)
Kazuhiro Hishinuma, Hideaki Iiduka
Fixed point quasiconvex subgradient method. Eur. J. Oper. Res. 282 (2) (2020)
Hideaki Iiduka
Stochastic Fixed Point Optimization Algorithm for Classifier Ensemble. IEEE Trans. Cybern. 50 (10) (2020)
Hiroyuki Sakai, Hideaki Iiduka
Hybrid Riemannian conjugate gradient methods with global convergence properties. Comput. Optim. Appl. 77 (3) (2020)
Kazuhiro Hishinuma, Hideaki Iiduka
Incremental and Parallel Machine Learning Algorithms With Automated Learning Rate Adjustments. Frontiers Robotics AI 6 (2019)
Hideaki Iiduka
Distributed Optimization for Network Resource Allocation With Nonsmooth Utility Functions. IEEE Trans. Control. Netw. Syst. 6 (4) (2019)
Hideaki Iiduka
Two stochastic optimization algorithms for convex optimization with fixed point constraints. Optim. Methods Softw. 34 (4) (2019)
Yoichi Hayashi, Hideaki Iiduka
Optimality and convergence for convex ensemble learning with sparsity and diversity based on fixed point optimization. Neurocomputing 273 (2018)
Yoichi Hayashi, Yuki Tanaka, Tomohiro Takagi, Takamichi Saito, Hideaki Iiduka, Hiroaki Kikuchi, Guido Bologna, Sushmita Mitra
Recursive-Rule Extraction Algorithm With J48graft And Applications To Generating Credit Scores. J. Artif. Intell. Soft Comput. Res. 6 (1) (2016)
Hideaki Iiduka
Incremental subgradient method for nonsmooth convex optimization with fixed point constraints. Optim. Methods Softw. 31 (5) (2016)
Hideaki Iiduka
Convergence analysis of iterative methods for nonsmooth convex optimization over fixed point sets of quasi-nonexpansive mappings. Math. Program. 159 (1-2) (2016)
Hideaki Iiduka
Proximal point algorithms for nonsmooth convex optimization with fixed point constraints. Eur. J. Oper. Res. 253 (2) (2016)
Masato Uchida, Hideaki Iiduka, Isao Sugino
Modeling User Behavior in P2P Data Storage System. IEICE Trans. Commun. (1) (2015)
Hideaki Iiduka
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. 282 (2015)
Hideaki Iiduka
Acceleration method for convex optimization over the fixed point set of a nonexpansive mapping. Math. Program. 149 (1-2) (2015)
Hideaki Iiduka, Kazuhiro Hishinuma
Acceleration Method Combining Broadcast and Incremental Distributed Optimization Algorithms. SIAM J. Optim. 24 (4) (2014)
Hideaki Iiduka
Fixed Point Optimization Algorithms for Distributed Optimization in Networked Systems. SIAM J. Optim. 23 (1) (2013)
Hideaki Iiduka
Fixed point optimization algorithm and its application to power control in CDMA data networks. Math. Program. 133 (1-2) (2012)
Hideaki Iiduka
Iterative Algorithm for Triple-Hierarchical Constrained Nonconvex Optimization Problem and Its Application to Network Bandwidth Allocation. SIAM J. Optim. 22 (3) (2012)
Hideaki Iiduka, Yasushi Narushima
Conjugate gradient methods using value of objective function for unconstrained optimization. Optim. Lett. 6 (5) (2012)
Hideaki Iiduka
Fixed point optimization algorithm and its application to network bandwidth allocation. J. Comput. Appl. Math. 236 (7) (2012)
Hideaki Iiduka, Isao Yamada
Computational Method for Solving a Stochastic Linear-Quadratic Control Problem Given an Unsolvable Stochastic Algebraic Riccati Equation. SIAM J. Control. Optim. 50 (4) (2012)
Hideaki Iiduka
Decentralized Algorithm for Centralized Variational Inequalities in Network Resource Allocation. J. Optim. Theory Appl. 151 (3) (2011)
Hideaki Iiduka
Three-term conjugate gradient method for the convex optimization problem over the fixed point set of a nonexpansive mapping. Appl. Math. Comput. 217 (13) (2011)
Hideaki Iiduka, Masato Uchida
Fixed Point Optimization Algorithms for Network Bandwidth Allocation Problems with Compoundable Constraints. IEEE Commun. Lett. 15 (6) (2011)
Hideaki Iiduka
Iterative Algorithm for Solving Triple-Hierarchical Constrained Optimization Problem. J. Optim. Theory Appl. 148 (3) (2011)
Hideaki Iiduka, Isao Yamada
An Ergodic Algorithm for the Power-Control Games for CDMA Data Networks. J. Math. Model. Algorithms 8 (1) (2009)
Hideaki Iiduka, Isao Yamada
A Use of Conjugate Gradient Direction for the Convex Optimization Problem over the Fixed Point Set of a Nonexpansive Mapping. SIAM J. Optim. 19 (4) (2009)