2 citations · 3 across the 8 of their papers we have counts for
9 papers
Direct-search methods for decentralized blackbox optimization
El Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev +1
Derivative-free optimization algorithms are particularly useful for tackling blackbox optimization problems where the objective function arises from complex and expensive procedure…
A Stochastic Iteratively Regularized Gauss-Newton Method
El Houcine Bergou, Neil K. Chada, Youssef Diouane
This work focuses on developing and motivating a stochastic version of a wellknown inverse problem methodology. Specifically, we consider the iteratively regularized Gauss-Newton m…
Inexact Direct-Search Methods for Bilevel Optimization Problems
Youssef Diouane, Vyacheslav Kungurtsev, Francesco Rinaldi +1
In this work, we introduce new direct search schemes for the solution of bilevel optimization (BO) problems. Our methods rely on a fixed accuracy black box oracle for the lower-lev…
An outer approximation bi-level framework for mixed categorical structural optimization problems
Pierre-Jean Barjhoux, Youssef Diouane, Stéphane Grihon +1
In this paper, mixed categorical structural optimization problems are investigated. The aim is to minimize the weight of a truss structure with respect to cross-section areas, mate…
TREGO: a Trust-Region Framework for Efficient Global Optimization
Youssef Diouane, Victor Picheny, Rodolphe Le Riche +1
Efficient Global Optimization (EGO) is the canonical form of Bayesian optimization that has been successfully applied to solve global optimization of expensive-to-evaluate black-bo…
Convergence and Complexity Analysis of a Levenberg-Marquardt Algorithm for Inverse Problems
E. Bergou, Y. Diouane, V. Kungurtsev
The Levenberg-Marquardt algorithm is one of the most popular algorithms for finding the solution of nonlinear least squares problems. Across different modified variations of the ba…
Complexity iteration analysis for strongly convex multi-objective optimization using a Newton path-following procedure
E. Bergou, Y. Diouane, V. Kungurtsev
In this note we consider the iteration complexity of solving strongly convex multi objective optimization. We discuss the precise meaning of this problem, and indicate it is loosel…
A Subsampling Line-Search Method with Second-Order Results
El-houcine Bergou, Youssef Diouane, Vladimir Kunc +2
In many contemporary optimization problems such as those arising in machine learning, it can be computationally challenging or even infeasible to evaluate an entire function or its…
A stochastic Levenberg-Marquardt method using random models with complexity results
E. Bergou, Y. Diouane, V. Kungurtsev +1
Globally convergent variants of the Gauss-Newton algorithm are often the methods of choice to tackle nonlinear least-squares problems. Among such frameworks, Levenberg-Marquardt an…