We are interested in the asymptotic behavior of the trajectories of the famous steepest descent evolution equation on Riemannian manifolds. It is shown how the convexity of the objective function helps in establishing the convergence as time goes to infinity of the trajectories towards points that minimize that function. Some numerical illustrations are given for Rosenbrock's function
We study the convergence of the Riemannian steepest descent algorithm on the Grassmann manifold for ...
We consider the minimization of a cost function $f$ on a manifold $M$ using Riemannian gradient desc...
Motivated by energy based analyses for descent methods in the Euclidean setting, we investigate a ge...
Abstract In this paper, we present a steepest descent method with Armijo’s rule for multicriteria op...
This unique monograph discusses the interaction between Riemannian geometry, convex programming, num...
This paper extends the full convergence of the steepest descent algorithm with a gen-eralized Armijo...
Given data points p0,..., pN on a Riemannian manifold M and time instants 0 = t0 < t1 <... <...
International audienceGiven data points p0,. .. , pN on a manifold M and time instants 0 = t0 < t1 <...
The result that for quadratic functions the classical steepest descent algorithm in R-d converges lo...
Artículo de publicación ISIIn view of solving theoretically constrained minimization problems, we in...
The steepest descent method has a rich history and is one of the simplest and best known methods for...
AbstractThis paper extends the full convergence of the steepest descent method with a generalized Ar...
The paper introduces an algorithm to compute steepest descent paths on multivariate piecewise-linear...
Abstract. The subgradient method is generalized to the context of Riemannian manifolds. The motivati...
ABSTRACT: We review the history of the nonlinear steepest descent method for the asymptotic evaluati...
We study the convergence of the Riemannian steepest descent algorithm on the Grassmann manifold for ...
We consider the minimization of a cost function $f$ on a manifold $M$ using Riemannian gradient desc...
Motivated by energy based analyses for descent methods in the Euclidean setting, we investigate a ge...
Abstract In this paper, we present a steepest descent method with Armijo’s rule for multicriteria op...
This unique monograph discusses the interaction between Riemannian geometry, convex programming, num...
This paper extends the full convergence of the steepest descent algorithm with a gen-eralized Armijo...
Given data points p0,..., pN on a Riemannian manifold M and time instants 0 = t0 < t1 <... <...
International audienceGiven data points p0,. .. , pN on a manifold M and time instants 0 = t0 < t1 <...
The result that for quadratic functions the classical steepest descent algorithm in R-d converges lo...
Artículo de publicación ISIIn view of solving theoretically constrained minimization problems, we in...
The steepest descent method has a rich history and is one of the simplest and best known methods for...
AbstractThis paper extends the full convergence of the steepest descent method with a generalized Ar...
The paper introduces an algorithm to compute steepest descent paths on multivariate piecewise-linear...
Abstract. The subgradient method is generalized to the context of Riemannian manifolds. The motivati...
ABSTRACT: We review the history of the nonlinear steepest descent method for the asymptotic evaluati...
We study the convergence of the Riemannian steepest descent algorithm on the Grassmann manifold for ...
We consider the minimization of a cost function $f$ on a manifold $M$ using Riemannian gradient desc...
Motivated by energy based analyses for descent methods in the Euclidean setting, we investigate a ge...