5 citations · 10 across the 15 of their papers we have counts for
15 papers · 1 filter
Retraction based Direct Search Methods for Derivative Free Riemannian Optimization
Vyacheslav Kungurtsev, Francesco Rinaldi, Damiano Zeffiro
Direct search methods represent a robust and reliable class of algorithms for solving black-box optimization problems. In this paper, we explore the application of those strategies…
Decentralized Asynchronous Non-convex Stochastic Optimization on Directed Graphs
Vyacheslav Kungurtsev, Mahdi Morafah, Tara Javidi +1
Distributed Optimization is an increasingly important subject area with the rise of multi-agent control and optimization. We consider a decentralized stochastic optimization proble…
Regularized quasi-monotone method for stochastic optimization
Vyacheslav Kungurtsev, Vladimir Shikhman
We adapt the quasi-monotone method from [2] for composite convex minimization in the stochastic setting. For the proposed numerical scheme we derive the optimal convergence rate in…
Asynchronous Optimization over Graphs: Linear Convergence under Error Bound Conditions
Loris Cannelli, Francisco Facchinei, Gesualdo Scutari +1
We consider convex and nonconvex constrained optimization with a partially separable objective function: agents minimize the sum of local objective functions, each of which is know…
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…