28 citations · 29 across the 5 of their papers we have counts for
4 papers · 1 filter
A globally convergent fast iterative shrinkage-thresholding algorithm with a new momentum factor for single and multi-objective convex optimization
Hiroki Tanabe, Ellen H. Fukuda, Nobuo Yamashita
Convex-composite optimization, which minimizes an objective function represented by the sum of a differentiable function and a convex one, is widely used in machine learning and si…
A revised sequential quadratic semidefinite programming method for nonlinear semidefinite optimization
Kosuke Okabe, Yuya Yamakawa, Ellen H. Fukuda
In 2020, Yamakawa and Okuno proposed a stabilized sequential quadratic semidefinite programming (SQSDP) method for solving, in particular, degenerate nonlinear semidefinite optimiz…
An equivalent nonlinear optimization model with triangular low-rank factorization for semidefinite programs
Yuya Yamakawa, Tetsuya Ikegami, Ellen H. Fukuda +1
In this paper, we propose a new nonlinear optimization model to solve semidefinite optimization problems (SDPs), providing some properties related to local optimal solutions. The p…
A barrier-type method for multiobjective optimization
Ellen H. Fukuda, L. M. Grana Drummond, Fernanda M. P. Raupp
For solving constrained multicriteria problems, we introduce the multiobjective barrier method (MBM), which extends the scalar-valued internal penalty method. This multiobjective v…