1 citations · 1 across the 7 of their papers we have counts for
10 papers
Deflation-Based Spectral Preconditioning for the Conjugate Gradient Method on Perturbed Systems
Youssef Diouane, Selime Gürol, Oussama Mouhtal +1
We consider sequences of large symmetric positive-definite linear systems in which spectral information from a reference matrix is reused to accelerate the solution of a nearby…
Complexity analysis of trust-region methods under -smoothness
Youssef Diouane, Mohamed L. Habiboullah, Awa Khouna +1
Generalized smoothness assumptions have attracted growing attention in recent years, motivated in part by machine learning problems in which the gradient of the objective function…
A Probabilistic U-Net Approach to Downscaling Climate Simulations
Maryam Alipourhajiagha, Pierre-Louis Lemaire, Youssef Diouane +1
Climate models are limited by heavy computational costs, often producing outputs at coarse spatial resolutions, while many climate change impact studies require finer scales. Stati…
An Efficient Scaled spectral preconditioner for sequences of symmetric positive definite linear systems
Youssef Diouane, Selime Gürol, Oussama Mouhtal +1
We explore a scaled spectral preconditioner for the efficient solution of sequences of symmetric and positive-definite linear systems. We design the scaled preconditioner not only…
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…
A unified error analysis for randomized low-rank approximation with application to data assimilation
Alexandre Scotto Di Perrotolo, Youssef Diouane, Selime Gürol +1
Randomized algorithms have proven to perform well on a large class of numerical linear algebra problems. Their theoretical analysis is critical to provide guarantees on their behav…
Impact of correlated observation errors on the convergence of the conjugate gradient algorithm in variational data assimilation
Olivier Goux, Selime Gürol, Anthony T. Weaver +2
An important class of nonlinear weighted least-squares problems arises from the assimilation of observations in atmospheric and ocean models. In variational data assimilation, inve…
A general error analysis for randomized low-rank approximation methods
Youssef Diouane, Selime Gürol, Alexandre Scotto Di Perrotolo +1
We propose a general error analysis related to the low-rank approximation of a given real matrix in both the spectral and Frobenius norms. First, we derive deterministic error boun…