4 papers · 1 filter
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…
The Adaptive Complexity of Finding a Stationary Point
Huanjian Zhou, Andi Han, Akiko Takeda +1
In large-scale applications, such as machine learning, it is desirable to design non-convex optimization algorithms with a high degree of parallelization. In this work, we study th…
Riemannian coordinate descent algorithms on matrix manifolds
Andi Han, Pratik Jawanpuria, Bamdev Mishra
Many machine learning applications are naturally formulated as optimization problems on Riemannian manifolds. The main idea behind Riemannian optimization is to maintain the feasib…
A Framework for Bilevel Optimization on Riemannian Manifolds
Andi Han, Bamdev Mishra, Pratik Jawanpuria +1
Bilevel optimization has gained prominence in various applications. In this study, we introduce a framework for solving bilevel optimization problems, where the variables in both t…