This article deals with a class of constrained nonsmooth multiobjective programming
problems (NMOPP) in the setting of Hadamard manifolds. The generalized Guignard …

We further research on the accelerated optimization phenomenon on Riemannian manifolds
by introducing accelerated global first-order methods for the optimization of $ L $-smooth …

Contemporary advances in the field of deep learning have embarked upon an exploration of
the underlying geometric properties of data, thus encouraging the investigation of …

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

A fundamental problem arising in many areas of machine learning is the evaluation of the
likelihood of a given observation under different nominal distributions. Frequently, these …

Proximal bundle methods are among the most successful approaches for convex and
nonconvex optimization problems in linear spaces and it is natural to extend these methods …

This paper introduces a new notion of a Fenchel conjugate, which generalizes the classical
Fenchel conjugation to functions defined on Riemannian manifolds. We investigate its …

In this work, we consider the bilevel optimization problem on Riemannian manifolds. We
inspect the calculation of the hypergradient of such problems on general manifolds and thus …

In this paper we introduce a Fenchel-type conjugate, given as the supremum of convex
functions, via Busemann functions. It is known that Busemann functions are smooth convex …

The subgradient method for convex optimization problems on complete Riemannian
manifolds with lower bounded sectional curvature is analysed in this paper. Iteration …