On the complexity of the steepest-descent with exact linesearches

Nous comparons à travers les tests numériques e¤ectués, l’algorithme Wolfe Epsilon Steepest Des- cent algorithm et l’Algorithme Armijo Epsilon Steepest descent, l’algorithme

Regarding smooth multiobjective optimization, D´ esid´ eri [7, 8] has extended the gradient algorithm using a common descent vector built from the convex hull spanned by each

Hence, we conclude from Theorem 4.2 that direct search based on probabilistic descent exhibits a worst case complexity bound in number of iterations of the order of 1/ρ[ϕ(κǫ)]

In this section, we summarize results concerning a generalized steepest descent algorithm for asymmetric systems of equations, which we view as unconstrained

Evaluation complexity analysis for nonconvex smooth optimization problems has recently been a very ac- tive area of research and has covered both standard methods for the

This short note considers and resolves the apparent contradiction between known worst-case complexity results for first and second-order methods for solving unconstrained

In summary, by combining the results of Sections 2 and 3, we conclude that the steepest descent iterates will normally approach the solution along a path that will give a small

The (optimal) function/gradient evaluations worst-case complexity analysis available for the Adaptive Regularizations algorithms with Cubics (ARC) for nonconvex smooth