Optimization on Riemannian manifolds-the result of smooth geometry and optimization
merging into one elegant modern framework-spans many areas of science and engineering …

Conjugate gradient methods are important first-order optimization algorithms both in
Euclidean spaces and on Riemannian manifolds. However, while various types of conjugate …

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 consider the problem of minimizing a non-convex function over a smooth manifold M.
We propose a novel algorithm, the Orthogonal Directions Constrained Gradient Method …

Hybrid analog–digital (HAD) architecture is a promising means to realize large-scale arrays
owing to the judicious trade-off between system performance and hardware complexity. This …

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 …

Background In many applications of bioinformatics, data stem from distinct heterogeneous
sources. One of the well-known examples is the identification of drug–target interactions …

Abstract Recently, the approximate Karush–Kuhn–Tucker (AKKT) conditions, also called the
sequential optimality conditions, have been proposed for nonlinear optimization in …