The minimax optimization over Riemannian manifolds (possibly nonconvex constraints) has
been actively applied to solve many problems, such as robust dimensionality reduction and …

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 focus on the decentralized optimization problem over the Stiefel manifold,
which is defined on a connected network of agents. The objective is an average of local …

The conjugate gradient method is a crucial first-order optimization method that generally
converges faster than the steepest descent method, and its computational cost is much …

Due to the rapid growth of smart agents such as weakly connected computational nodes and
sensors, developing decentralized algorithms that can perform computations on local agents …

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 …

Principal Subspace Analysis (PSA)—and its sibling, Principal Component Analysis (PCA)—
is one of the most popular approaches for dimensionality reduction in signal processing and …

We consider the problem of distributed principal component analysis (PCA) where the data
samples are dispersed across different agents. Despite the rich literature on this problem …