4 citations · 4 across the 8 of their papers we have counts for
8 papers
A general framework of Riemannian adaptive optimization methods with a convergence analysis
Hiroyuki Sakai, Hideaki Iiduka
This paper proposes a general framework of Riemannian adaptive optimization methods. The framework encapsulates several stochastic optimization algorithms on Riemannian manifolds a…
Convergence of Riemannian Stochastic Gradient Descent on Hadamard Manifold
Hiroyuki Sakai, Hideaki Iiduka
Novel convergence analyses are presented of Riemannian stochastic gradient descent (RSGD) on a Hadamard manifold. RSGD is the most basic Riemannian stochastic optimization algorith…
Modified memoryless spectral-scaling Broyden family on Riemannian manifolds
Hiroyuki Sakai, Hideaki Iiduka
This paper presents modified memoryless quasi-Newton methods based on the spectral-scaling Broyden family on Riemannian manifolds. The method involves adding one parameter to the s…
Global Convergence of Hager-Zhang type Riemannian Conjugate Gradient Method
Hiroyuki Sakai, Hiroyuki Sato, Hideaki Iiduka
This paper presents the Hager-Zhang (HZ)-type Riemannian conjugate gradient method that uses the exponential retraction. We also present global convergence analyses of our proposed…
Riemannian Stochastic Fixed Point Optimization Algorithm
Hideaki Iiduka, Hiroyuki Sakai
This paper considers a stochastic optimization problem over the fixed point sets of quasinonexpansive mappings on Riemannian manifolds. The problem enables us to consider Riemannia…
Sufficient Descent Riemannian Conjugate Gradient Method
Hiroyuki Sakai, Hideaki Iiduka
This paper considers sufficient descent Riemannian conjugate gradient methods with line search algorithms. We propose two kinds of sufficient descent nonlinear conjugate gradient m…
Riemannian Adaptive Optimization Algorithm and Its Application to Natural Language Processing
Hiroyuki Sakai, Hideaki Iiduka
This paper proposes a Riemannian adaptive optimization algorithm to optimize the parameters of deep neural networks. The algorithm is an extension of both AMSGrad in Euclidean spac…
Hybrid Riemannian Conjugate Gradient Methods with Global Convergence Properties
Hiroyuki Sakai, Hideaki Iiduka
This paper presents new Riemannian conjugate gradient methods and global convergence analyses under the strong Wolfe conditions. The main idea of the new methods is to combine the…