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Hideaki Iiduka
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2020 – today
- 2024
- [j37]Hiroyuki Sakai
, Hideaki Iiduka:
Modified Memoryless Spectral-Scaling Broyden Family on Riemannian Manifolds. J. Optim. Theory Appl. 202(2): 834-853 (2024) - [j36]Hideaki Iiduka:
Theoretical analysis of Adam using hyperparameters close to one without Lipschitz smoothness. Numer. Algorithms 95(1): 383-421 (2024) - [i14]Naoki Sato, Hideaki Iiduka:
Role of Momentum in Smoothing Objective Function in Implicit Graduated Optimization. CoRR abs/2402.02325 (2024) - [i13]Kento Imaizumi, Hideaki Iiduka:
Iteration and Stochastic First-order Oracle Complexities of Stochastic Gradient Descent using Constant and Decaying Learning Rates. CoRR abs/2402.15344 (2024) - [i12]Hikaru Umeda, Hideaki Iiduka:
Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent. CoRR abs/2409.08770 (2024) - [i11]Hinata Harada, Hideaki Iiduka:
Convergence of Sharpness-Aware Minimization Algorithms using Increasing Batch Size and Decaying Learning Rate. CoRR abs/2409.09984 (2024) - 2023
- [j35]Hiroyuki Sakai, Hiroyuki Sato, Hideaki Iiduka:
Global convergence of Hager-Zhang type Riemannian conjugate gradient method. Appl. Math. Comput. 441: 127685 (2023) - [j34]Hideaki Iiduka
ϵ-Approximation of Adaptive Leaning Rate Optimization Algorithms for Constrained Nonconvex Stochastic Optimization. IEEE Trans. Neural Networks Learn. Syst. 34(10): 8108-8115 (2023) - [c2]Hiroki Naganuma, Hideaki Iiduka:
Conjugate Gradient Method for Generative Adversarial Networks. AISTATS 2023: 4381-4408 - [c1]Naoki Sato, Hideaki Iiduka:
Existence and Estimation of Critical Batch Size for Training Generative Adversarial Networks with Two Time-Scale Update Rule. ICML 2023: 30080-30104 - [i10]Hiroyuki Sakai, Hideaki Iiduka:
Modified memoryless spectral-scaling Broyden family on Riemannian manifolds. CoRR abs/2307.08986 (2023) - [i9]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 abs/2307.13831 (2023) - [i8]Naoki Sato, Hideaki Iiduka:
Using Stochastic Gradient Descent to Smooth Nonconvex Functions: Analysis of Implicit Graduated Optimization with Optimal Noise Scheduling. CoRR abs/2311.08745 (2023) - 2022
- [j33]Hideaki Iiduka
Riemannian stochastic fixed point optimization algorithm. Numer. Algorithms 90(4): 1493-1517 (2022) - [j32]Hiroyuki Sakai
Riemannian Adaptive Optimization Algorithm and its Application to Natural Language Processing. IEEE Trans. Cybern. 52(8): 7328-7339 (2022) - [j31]Hideaki Iiduka
Appropriate Learning Rates of Adaptive Learning Rate Optimization Algorithms for Training Deep Neural Networks. IEEE Trans. Cybern. 52(12): 13250-13261 (2022) - [i7]Naoki Sato, Hideaki Iiduka:
Using Constant Learning Rate of Two Time-Scale Update Rule for Training Generative Adversarial Networks. CoRR abs/2201.11989 (2022) - [i6]Hiroki Naganuma
Conjugate Gradient Method for Generative Adversarial Networks. CoRR abs/2203.14495 (2022) - [i5]Hideaki Iiduka:
Theoretical analysis of Adam using hyperparameters close to one without Lipschitz smoothness. CoRR abs/2206.13290 (2022) - [i4]Hideaki Iiduka:
Critical Bach Size Minimizes Stochastic First-Order Oracle Complexity of Deep Learning Optimizer using Hyperparameters Close to One. CoRR abs/2208.09814 (2022) - 2021
- [j30]Yini Zhu
Unified Algorithm Framework for Nonconvex Stochastic Optimization in Deep Neural Networks. IEEE Access 9: 143807-143823 (2021) - [j29]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): 479-505 (2021) - [j28]Hiroyuki Sakai
Sufficient Descent Riemannian Conjugate Gradient Methods. J. Optim. Theory Appl. 190(1): 130-150 (2021) - [i3]Hideaki Iiduka:
The Number of Steps Needed for Nonconvex Optimization of a Deep Learning Optimizer is a Rational Function of Batch Size. CoRR abs/2108.11713 (2021) - [i2]Hideaki Iiduka:
Minimization of Stochastic First-order Oracle Complexity of Adaptive Methods for Nonconvex Optimization. CoRR abs/2112.07163 (2021) - 2020
- [j27]Hiroyuki Sakai
Hybrid Riemannian conjugate gradient methods with global convergence properties. Comput. Optim. Appl. 77(3): 811-830 (2020) - [j26]Kazuhiro Hishinuma
Fixed point quasiconvex subgradient method. Eur. J. Oper. Res. 282(2): 428-437 (2020) - [j25]Hideaki Iiduka
Stochastic Fixed Point Optimization Algorithm for Classifier Ensemble. IEEE Trans. Cybern. 50(10): 4370-4380 (2020) - [i1]Yu Kobayashi, Hideaki Iiduka:
Conjugate-gradient-based Adam for stochastic optimization and its application to deep learning. CoRR abs/2003.00231 (2020)
2010 – 2019
- 2019
- [j24]Kazuhiro Hishinuma, Hideaki Iiduka:
Incremental and Parallel Machine Learning Algorithms With Automated Learning Rate Adjustments. Frontiers Robotics AI 6: 77 (2019) - [j23]Hideaki Iiduka
Two stochastic optimization algorithms for convex optimization with fixed point constraints. Optim. Methods Softw. 34(4): 731-757 (2019) - [j22]Hideaki Iiduka
Distributed Optimization for Network Resource Allocation With Nonsmooth Utility Functions. IEEE Trans. Control. Netw. Syst. 6(4): 1354-1365 (2019) - 2018
- [j21]Yoichi Hayashi
Optimality and convergence for convex ensemble learning with sparsity and diversity based on fixed point optimization. Neurocomputing 273: 367-372 (2018) - 2016
- [j20]Hideaki Iiduka
Proximal point algorithms for nonsmooth convex optimization with fixed point constraints. Eur. J. Oper. Res. 253(2): 503-513 (2016) - [j19]Yoichi Hayashi, Yuki Tanaka, Tomohiro Takagi, Takamichi Saito, Hideaki Iiduka
Recursive-Rule Extraction Algorithm With J48graft And Applications To Generating Credit Scores. J. Artif. Intell. Soft Comput. Res. 6(1): 35 (2016) - [j18]Hideaki Iiduka
Convergence analysis of iterative methods for nonsmooth convex optimization over fixed point sets of quasi-nonexpansive mappings. Math. Program. 159(1-2): 509-538 (2016) - [j17]Hideaki Iiduka
Incremental subgradient method for nonsmooth convex optimization with fixed point constraints. Optim. Methods Softw. 31(5): 931-951 (2016) - 2015
- [j16]Masato Uchida
Modeling User Behavior in P2P Data Storage System. IEICE Trans. Commun. 98-B(1): 33-41 (2015) - [j15]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: 225-236 (2015) - [j14]Hideaki Iiduka
Acceleration method for convex optimization over the fixed point set of a nonexpansive mapping. Math. Program. 149(1-2): 131-165 (2015) - 2014
- [j13]Hideaki Iiduka
Acceleration Method Combining Broadcast and Incremental Distributed Optimization Algorithms. SIAM J. Optim. 24(4): 1840-1863 (2014) - 2013
- [j12]Hideaki Iiduka
Fixed Point Optimization Algorithms for Distributed Optimization in Networked Systems. SIAM J. Optim. 23(1): 1-26 (2013) - 2012
- [j11]Hideaki Iiduka
Fixed point optimization algorithm and its application to network bandwidth allocation. J. Comput. Appl. Math. 236(7): 1733-1742 (2012) - [j10]Hideaki Iiduka
Fixed point optimization algorithm and its application to power control in CDMA data networks. Math. Program. 133(1-2): 227-242 (2012) - [j9]Hideaki Iiduka
Conjugate gradient methods using value of objective function for unconstrained optimization. Optim. Lett. 6(5): 941-955 (2012) - [j8]Hideaki Iiduka
Computational Method for Solving a Stochastic Linear-Quadratic Control Problem Given an Unsolvable Stochastic Algebraic Riccati Equation. SIAM J. Control. Optim. 50(4): 2173-2192 (2012) - [j7]Hideaki Iiduka
Iterative Algorithm for Triple-Hierarchical Constrained Nonconvex Optimization Problem and Its Application to Network Bandwidth Allocation. SIAM J. Optim. 22(3): 862-878 (2012) - 2011
- [j6]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): 6315-6327 (2011) - [j5]Hideaki Iiduka
Fixed Point Optimization Algorithms for Network Bandwidth Allocation Problems with Compoundable Constraints. IEEE Commun. Lett. 15(6): 596-598 (2011) - [j4]Hideaki Iiduka
Iterative Algorithm for Solving Triple-Hierarchical Constrained Optimization Problem. J. Optim. Theory Appl. 148(3): 580-592 (2011) - [j3]Hideaki Iiduka
Decentralized Algorithm for Centralized Variational Inequalities in Network Resource Allocation. J. Optim. Theory Appl. 151(3): 525-540 (2011)
2000 – 2009
- 2009
- [j2]Hideaki Iiduka
An Ergodic Algorithm for the Power-Control Games for CDMA Data Networks. J. Math. Model. Algorithms 8(1): 1-18 (2009) - [j1]Hideaki Iiduka
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): 1881-1893 (2009)
Coauthor Index
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