27 citations · 45 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
A Line-Search Algorithm Inspired by the Adaptive Cubic Regularization Framework and Complexity Analysis
El houcine Bergou, Youssef Diouane, Serge Gratton
Adaptive regularized framework using cubics has emerged as an alternative to line-search and trust-region algorithms for smooth nonconvex optimization, with an optimal complexity a…