We consider a distributed non-convex optimization where a network of agents aims at
minimizing a global function over the Stiefel manifold. The global function is represented as …

We consider the problem of decentralized nonconvex optimization over a compact
submanifold, where each local agent's objective function defined by the local dataset is …

In this paper, we tackle a significant challenge in PCA: heterogeneity. When data are
collected from different sources with heterogeneous trends while still sharing some …

Federated learning (FL) has found many important applications in smart-phone-APP based
machine learning applications. Although many algorithms have been studied for FL, to the …

We focus on a class of non-smooth optimization problems over the Stiefel manifold in the
decentralized setting, where a connected network of $ n $ agents cooperatively minimize a …

With a computationally efficient approximation of the second-order information, natural
gradient methods have been successful in solving large-scale structured optimization …

We study decentralized smooth optimization problems over compact submanifolds.
Recasting it as a composite optimization problem, we propose a decentralized Douglas …

Recently, decentralized optimization over the Stiefel manifold has attracted tremendous
attentions due to its wide range of applications in various fields. Existing methods rely on the …

Certain consensus seeking multi-agent systems can be formulated as gradient descent
flows of a disagreement function. We study how known pathologies of gradient descent …

Many classical and modern machine learning algorithms require solving optimization tasks
under orthogonal constraints. Solving these tasks often require calculating retraction-based …