From the 1 of 7 linked papers with an AI index.
1 citations · 1 across the 2 of their papers we have counts for
7 papers
An Inexact Modified Quasi-Newton Method for Nonsmooth Regularized Optimization
Nathan Allaire, Sébastien Le Digabel, Dominique Orban
The paper proposes iR2N, a modified proximal quasi‑Newton algorithm that handles nonsmooth regularized problems with inexact evaluations of the smooth part and proximal operators,…
A Spectral Preconditioner for the Conjugate Gradient Method with Iteration Budget
Youssef Diouane, Selime Gürol, Oussama Mouhtal +1
We study the solution of large symmetric positive-definite linear systems in a matrix-free setting with a limited iteration budget. We focus on the preconditioned conjugate gradien…
Nonsmooth exact penalty methods for equality-constrained optimization: complexity and implementation
Youssef Diouane, Maxence Gollier, Dominique Orban
Penalty methods are a well known class of algorithms for constrained optimization. They transform a constrained problem into a sequence of unconstrained \emph{penalized} problems i…
A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization
Youssef Diouane, Mohamed Laghdaf Habiboullah, Dominique Orban
We develop R2N, a modified quasi-Newton method for minimizing the sum of a function and a lower semi-continuous prox-bounded . Both and may be noncon…
Complexity of trust-region methods in the presence of unbounded Hessian approximations
Youssef Diouane, Mohamed Laghdaf Habiboullah, Dominique Orban
We extend traditional complexity analyses of trust-region methods for unconstrained, possibly nonconvex, optimization. Whereas most complexity analyses assume uniform boundedness o…
Complexity of trust-region methods with unbounded Hessian approximations for smooth and nonsmooth optimization
Geoffroy Leconte, Dominique Orban
We develop a worst-case evaluation complexity bound for trust-region methods in the presence of unbounded Hessian approximations. We use the algorithm of arXiv:2103.15993v3 as a mo…