9 citations · 29 across the 7 of their papers we have counts for
8 papers
A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition
Masaru Ito, Zhaosong Lu, Chuan He
In this paper we consider a composite optimization problem that minimizes the sum of a weakly smooth function and a convex function with either a bounded domain or a uniformly conv…
Automorphisms of rank-one generated hyperbolicity cones and their derivative relaxations
Masaru Ito, Bruno F. Lourenço
A hyperbolicity cone is said to be rank-one generated (ROG) if all its extreme rays have rank one, where the rank is computed with respect to the underlying hyperbolic polynomial.…
Nearly optimal first-order methods for convex optimization under gradient norm measure: An adaptive regularization approach
Masaru Ito, Mituhiro Fukuda
In the development of first-order methods for smooth (resp., composite) convex optimization problems, where smooth functions with Lipschitz continuous gradients are minimized, the…
The automorphism group and the non-self-duality of -cones
Masaru Ito, Bruno F. Lourenço
In this paper, we determine the automorphism group of the -cones () in dimension greater than two. In particular, we show that the automorphism group of those -cones…
The -cones in dimension are not homogeneous when
Masaru Ito, Bruno F. Lourenço
Using the T-algebra machinery we show that, up to linear isomorphism, the only strictly convex homogeneous cones in with are the 2-cones, also known as Lorentz c…
A bound on the Carathéodory number
Masaru Ito, Bruno F. Lourenço
The Carathéodory number k(K) of a pointed closed convex cone K is the minimum among all the k for which every element of K can be written as a nonnegative linear combination of at…
New results on subgradient methods for strongly convex optimization problems with a unified analysis
Masaru Ito
We develop subgradient- and gradient-based methods for minimizing strongly convex functions under a notion which generalizes the standard Euclidean strong convexity. We propose a u…
A Family of Subgradient-Based Methods for Convex Optimization Problems in a Unifying Framework
Masaru Ito, Mituhiro Fukuda
We propose a new family of subgradient- and gradient-based methods which converges with optimal complexity for convex optimization problems whose feasible region is simple enough.…