The proximal point algorithm, which is a well-known tool for finding minima of convex
functions, is generalized from the classical Hilbert space framework into a nonlinear setting …

This paper considers optimization problems on Riemannian manifolds and analyzes the
iteration-complexity for gradient and subgradient methods on manifolds with nonnegative …

This paper presents line search algorithms for finding extrema of locally Lipschitz functions
defined on Riemannian manifolds. To this end we generalize the so-called Wolfe conditions …

In this article, we study a class of nonsmooth multiobjective semi-infinite programming
problems defined on Hadamard manifolds [in short,(NMSIP)]. We present Abadie constraint …

This paper presents a descent direction method for finding extrema of locally Lipschitz
functions defined on Riemannian manifolds. To this end we define a set-valued mapping …

This article is concerned with nonsmooth multiobjective semi-infinite programming problems
with vanishing constraints in the setting of Hadamard manifolds (abbreviated …

In this paper, we present a new approach to the proximal point method in the Riemannian
context. In particular, without requiring any restrictive assumptions about the sign of the …

In this article, we present the proximal point method for finding minima of a special class of
nonconvex function on a Hadamard manifold. The well definedness of the sequence …

The concept of pseudomonotone vector field on Hadamard manifold is introduced. A variant
of Korpelevich's method for solving the variational inequality problem is extended from …

This paper presents a Riemannian trust region algorithm for unconstrained optimization
problems with locally Lipschitz objective functions defined on complete Riemannian …