235 citations · 345 across the 30 of their papers we have counts for
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Acceleration of Cooperative Least Mean Square via Chebyshev Periodical Successive Over-Relaxation
Tadashi Wadayama, Satoshi Takabe
A distributed algorithm for least mean square (LMS) can be used in distributed signal estimation and in distributed training for multivariate regression models. The convergence spe…
Convergence Acceleration via Chebyshev Step: Plausible Interpretation of Deep-Unfolded Gradient Descent
Satoshi Takabe, Tadashi Wadayama
Deep unfolding is a promising deep-learning technique, whose network architecture is based on expanding the recursive structure of existing iterative algorithms. Although convergen…
Deep Unfolded Multicast Beamforming
Satoshi Takabe, Tadashi Wadayama
Multicast beamforming is a promising technique for multicast communication. Providing an efficient and powerful beamforming design algorithm is a crucial issue because multicast be…
Theoretical Interpretation of Learned Step Size in Deep-Unfolded Gradient Descent
Satoshi Takabe, Tadashi Wadayama
Deep unfolding is a promising deep-learning technique in which an iterative algorithm is unrolled to a deep network architecture with trainable parameters. In the case of gradient…
Chebyshev Inertial Landweber Algorithm for Linear Inverse Problems
Tadashi Wadayama, Satoshi Takabe
The Landweber algorithm defined on complex/real Hilbert spaces is a gradient descent algorithm for linear inverse problems. Our contribution is to present a novel method for accele…
Chebyshev Inertial Iteration for Accelerating Fixed-Point Iterations
Tadashi Wadayama, Satoshi Takabe
A novel method which is called the Chebyshev inertial iteration for accelerating the convergence speed of fixed-point iterations is presented. The Chebyshev inertial iteration can…