12 papers
DOFFO_TR: a Decentralized Objective Function-Free Optimization method with Trust-Region
Stefania Bellavia, Greta Malaspina, Benedetta Morini
In this paper, we propose a novel objective function-free trust-region method designed to solve optimization problems over decentralized networks. Unlike traditional approaches tha…
From Consensus-Based Optimization to Evolution Strategies: Proof of Global Convergence
Massimo Fornasier, Hui Huang, Jona Klemenc +1
Consensus-based optimization (CBO) is a powerful and versatile zero-order multi-particle method designed to provably solve high-dimensional global optimization problems, including…
A variable dimension sketching strategy for nonlinear least-squares
Stefania Bellavia, Greta Malaspina, Benedetta Morini
We present a stochastic inexact Gauss-Newton method for the solution of nonlinear least-squares. To reduce the computational cost with respect to the classical method, at each iter…
A discrete Consensus-Based Global Optimization Method with Noisy Objective Function
Stefania Bellavia, Greta Malaspina
Consensus based optimization is a derivative-free particles-based method for the solution of global optimization problems. Several versions of the method have been proposed in the…
Parallel Inexact Levenberg-Marquardt Method for Nearly-Separable Nonlinear Least Squares
Lidija Fodor, Dusan Jakovetic, Natasa Krejic +1
Motivated by localization problems such as cadastral maps refinements, we consider a generic Nonlinear Least Squares (NLS) problem of minimizing an aggregate squared fit across all…
Inexact Gauss-Newton methods with matrix approximation by sampling for nonlinear least-squares and systems
Stefania Bellavia, Greta Malaspina, Benedetta Morini
We develop and analyze stochastic inexact Gauss-Newton methods for nonlinear least-squares problems and for nonlinear systems ofequations. Random models are formed using suitable s…
Distributed Inexact Newton Method with Adaptive Step Sizes
Dusan Jakovetic, Natasa Krejic, Greta Malaspina
We consider two formulations for distributed optimization wherein agents in a generic connected network solve a problem of common interest: distributed personalized optimizatio…
Splitted Levenberg-Marquardt Method for Large-Scale Sparse Problems
Natasa Krejic, Greta Malaspina, Lense Swaenen
We consider large-scale nonlinear least squares problems with sparse residuals, each of them depending on a small number of variables. A decoupling procedure which results in a spl…
A Hybrid Compartmental Model with a Case Study of COVID-19 in Great Britain and Israel
Greta Malaspina, Stevo Racković, Filipa Valdeira
Given the severe impact of COVID-19 on several societal levels, it is of crucial importance to model the impact of restriction measures on the pandemic evolution, so that governmen…
Linear Convergence Rate Analysis of a Class of Exact First-Order Distributed Methods for Weight-Balanced Time-Varying Networks and Uncoordinated Step Sizes
Greta Malaspina, Dusan Jakovetic, Natasa Krejic
We analyze a class of exact distributed first order methods under a general setting on the underlying network and step-sizes. In more detail, we allow simultaneously for time-varyi…
Distributed Fixed Point Method for Solving Systems of Linear Algebraic Equations
Dusan Jakovetic, Natasa Krejic, Natasa Krklec Jerinkic +2
We present a class of iterative fully distributed fixed point methods to solve a system of linear equations, such that each agent in the network holds one of the equations of the s…
Subsampled Nonmonotone Spectral Gradient Methods
Stefania Bellavia, Nataša Krklec Jerinkić, Greta Malaspina
This paper deals with subsampled spectral gradient methods for minimizing finite sum. Subsample function and gradient approximations are employed in order to reduce the overall com…