7 papers
Random-Subspace Frank--Wolfe over Strongly Convex Sets
Pierre-Louis Poirion, Sebastian Pokutta, Akiko Takeda
Frank--Wolfe methods avoid projections, but over curved feasible regions the full-space linear minimization oracle (LMO) can itself become the computational bottleneck. We introduc…
Randomized Subspace Nesterov Accelerated Gradient
Gaku Omiya, Pierre-Louis Poirion, Akiko Takeda
Randomized-subspace methods reduce the cost of first-order optimization by using only low-dimensional projected-gradient information, a feature that is attractive in forward-mode a…
Inexact subgradient algorithm with a non-asymptotic convergence guarantee for copositive programming problems
Mitsuhiro Nishijima, Pierre-Louis Poirion, Akiko Takeda
In this paper, we propose a subgradient algorithm with a non-asymptotic convergence guarantee to solve copositive programming problems. The subproblem to be solved at each iteratio…
Fairness in Robust Unit Commitment Problem Considering Suppression of Renewable Energy
Ichiro Toyoshima, Pierre-Louis Poirion, Tomohide Yamazaki +4
Power company operators make power generation plans one day in advance, in what is known as the Unit Commitment (UC) problem. UC is exposed to uncertainties, such as unknown electr…
Convergence Analysis of Randomized Subspace Normalized SGD under Heavy-Tailed Noise
Gaku Omiya, Pierre-Louis Poirion, Akiko Takeda
Randomized subspace methods reduce per-iteration cost; however, in nonconvex optimization, most analyses are expectation-based, and high-probability bounds remain scarce even under…
Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method
Andi Han, Pierre-Louis Poirion, Akiko Takeda
Optimization with orthogonality constraints frequently arises in various fields such as machine learning. Riemannian optimization offers a powerful framework for solving these prob…