11 citations · 27 across the 8 of their papers we have counts for
8 papers
Exploiting cone approximations in an augmented Lagrangian method for conic optimization
Mituhiro Fukuda, Walter Gómez, Gabriel Haeser +1
We propose an algorithm for general nonlinear conic programming which does not require the knowledge of the full cone, but rather a simpler, more tractable, approximation of it. We…
Exact SDP relaxations for quadratic programs with bipartite graph structures
Godai Azuma, Mituhiro Fukuda, Sunyoung Kim +1
For nonconvex quadratically constrained quadratic programs (QCQPs), we first show that, under certain feasibility conditions, the standard semidefinite (SDP) relaxation is exact fo…
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…
Exact SDP relaxations of quadratically constrained quadratic programs with forest structures
Godai Azuma, Mituhiro Fukuda, Sunyoung Kim +1
We study the exactness of the semidefinite programming (SDP) relaxation of quadratically constrained quadratic programs (QCQPs). With the aggregate sparsity matrix from the data ma…
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…
A dual spectral projected gradient method for log-determinant semidefinite problems
Takashi Nakagaki, Mituhiro Fukuda, Sunyoung Kim +1
We extend the result on the spectral projected gradient method by Birgin et al. in 2000 to a log-determinant semidefinite problem (SDP) with linear constraints and propose a spectr…
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.…
Variational approach for the electronic structure calculation on the second-order reduced density matrices and the -representability problem
Maho Nakata, Mituhiro Fukuda, Katsuki Fujisawa
The reduced-density-matrix method is an promising candidate for the next generation electronic structure calculation method; it is equivalent to solve the Schrödinger equation for…