6 papers
Dynamic Competition of Fast and Collisional Neutrino Flavor Instabilities with Collisional Damping in Spatially Inhomogeneous Systems
Shota Takahashi, Hiroki Nagakura, Masamichi Zaizen +2
Neutrino flavor evolution in dense astrophysical environments such as core-collapse supernova (CCSN) is influenced by collective effects. While the Fast Flavor Instability (FFI) an…
Adaptive Conditional Gradient Sliding: Projection-Free and Line-Search-Free Acceleration
Shota Takahashi
We study convex optimization problems over a compact convex set where projections are expensive but a linear minimization oracle (LMO) is available. We propose the adaptive conditi…
Approximate Bregman proximal gradient algorithm with variable metric Armijo--Wolfe line search
Kiwamu Fujiki, Shota Takahashi, Akiko Takeda
We propose a variant of the approximate Bregman proximal gradient (ABPG) algorithm for minimizing the sum of a smooth nonconvex function and a nonsmooth convex function. ABPG is kn…
Fast Frank--Wolfe Algorithms with Adaptive Bregman Step-Size for Weakly Convex Functions
Shota Takahashi, Sebastian Pokutta, Akiko Takeda
We propose Frank--Wolfe (FW) algorithms with an adaptive Bregman step-size strategy for smooth adaptable (also called: relatively smooth) (weakly-) convex functions. This means tha…
Majorization-minimization Bregman proximal gradient algorithms for NMF with the Kullback--Leibler divergence
Shota Takahashi, Mirai Tanaka, Shiro Ikeda
Nonnegative matrix factorization (NMF) is a popular method in machine learning and signal processing to decompose a given nonnegative matrix into two nonnegative matrices. In this…
Approximate Bregman Proximal Gradient Algorithm for Relatively Smooth Nonconvex Optimization
Shota Takahashi, Akiko Takeda
In this paper, we propose the approximate Bregman proximal gradient algorithm (ABPG) for solving composite nonconvex optimization problems. ABPG employs a new distance that approxi…