11 citations · 25 across the 6 of their papers we have counts for
6 papers
Blind Deconvolution with Non-smooth Regularization via Bregman Proximal DCAs
Shota Takahashi, Mirai Tanaka, Shiro Ikeda
Blind deconvolution is a technique to recover an original signal without knowing a convolving filter. It is naturally formulated as a minimization of a quartic objective function u…
A Gradient Method for Multilevel Optimization
Ryo Sato, Mirai Tanaka, Akiko Takeda
Although application examples of multilevel optimization have already been discussed since the 1990s, the development of solution methods was almost limited to bilevel cases due to…
New Bregman proximal type algorithms for solving DC optimization problems
Shota Takahashi, Mituhiro Fukuda, Mirai Tanaka
Difference of Convex (DC) optimization problems have objective functions that are differences between two convex functions. Representative ways of solving these problems are the pr…
Semi-flat minima and saddle points by embedding neural networks to overparameterization
Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake +1
We theoretically study the landscape of the training error for neural networks in overparameterized cases. We consider three basic methods for embedding a network into a wider one…
Extension of the LP-Newton method to SOCPs via semi-infinite representation
Takayuki Okuno, Mirai Tanaka
The LP-Newton method solves the linear programming problem (LP) by repeatedly projecting a current point onto a certain relevant polytope. In this paper, we extend the algorithmic…
Efficient Preconditioning for Noisy Separable NMFs by Successive Projection Based Low-Rank Approximations
Tomohiko Mizutani, Mirai Tanaka
The successive projection algorithm (SPA) can quickly solve a nonnegative matrix factorization problem under a separability assumption. Even if noise is added to the problem, SPA i…