Variational inference (VI) seeks to approximate a target distribution $\pi $ by an element of a
tractable family of distributions. Of key interest in statistics and machine learning is Gaussian …

We consider a class of nonsmooth optimization problems over the Stiefel manifold, in which
the objective function is weakly convex in the ambient Euclidean space. Such problems are …

We consider a class of Riemannian optimization problems where the objective is the sum of
a smooth function and a nonsmooth function, considered in the ambient space. This class of …

In this paper, we study the generalized subdifferentials and the Riemannian gradient
subconsistency that are the basis for non-Lipschitz optimization on embedded submanifolds …

We consider stochastic zeroth-order optimization over Riemannian submanifolds embedded
in Euclidean space, where the task is to solve Riemannian optimization problems with only …

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 …

We extend and analyze the trust region method for solving smooth and unconstrained
multicriteria optimization problems on Riemannian manifolds. At each iteration of this …

Spectral clustering is one of the fundamental unsupervised learning methods and is widely
used in data analysis. Sparse spectral clustering (SSC) imposes sparsity to the spectral …

We consider optimization problems over the Stiefel manifold whose objective function is the
summation of a smooth function and a nonsmooth function. Existing methods for solving this …

The problem of clustering points on a union of subspaces finds numerous applications in
machine learning and computer vision, and it has been extensively studied in the past two …