In this paper, we study min-max optimization problems on Riemannian manifolds. We
introduce a Riemannian Hamiltonian function, minimization of which serves as a proxy for …

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 …

We propose Riemannian preconditioned algorithms for the tensor completion problem via
tensor ring decomposition. A new Riemannian metric is developed on the product space of …

This paper presents the Hager–Zhang (HZ)-type Riemannian conjugate gradient method
that uses the exponential retraction. We also present global convergence analyses of our …

We investigate massive multiple-input multiple-output (MIMO) uplink (UL) transmission for
multiple low-earth-orbit (LEO) satellite communication (SATCOM). The signal and channel …

Principal component analysis (PCA) along with its extensions to manifolds and outlier
contaminated data have been indispensable in computer vision and machine learning. In …

Hyperbolic spaces have been quite popular in the recent past for representing hierarchically
organized data. Further, several classification algorithms for data in these spaces have been …

The symplectic Stiefel manifold is a Riemannian manifold that is a generalization of the
symplectic group. In this letter, we propose novel conjugate gradient methods on the …

We present Zeroth-order Riemannian Averaging Stochastic Approximation (Zo-RASA)
algorithms for stochastic optimization on Riemannian manifolds. We show that Zo-RASA …

The ascent of geometry-based models and methodologies, exemplified by geometric deep
learning and manifold numerical optimization algorithms, has inaugurated a novel domain …