24 citations · 33 across the 18 of their papers we have counts for
22 papers
AS-BOX: Additional Sampling Method for Weighted Sum Problems with Box Constraints
Nataša Krejić, Nataša Krklec Jerinkić, Tijana Ostojić +1
A class of optimization problems characterized by a weighted finite-sum objective function subject to box constraints is considered. We propose a novel stochastic optimization meth…
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…
A non-monotone trust-region method with noisy oracles and additional sampling
Natasa Krejic, Natasa Krklec Jerinkic, Angeles Martinez +1
In this work, we introduce a novel stochastic second-order method, within the framework of a non-monotone trust-region approach, for solving the unconstrained, nonlinear, and non-c…
SLiSeS: Subsampled Line Search Spectral Gradient Method for Finite Sums
Stefania Bellavia, Nataša Krejić, Nataša Krklec Jerinkić +1
The spectral gradient method is known to be a powerful low-cost tool for solving large-scale optimization problems. In this paper, our goal is to exploit its advantages in the stoc…
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 Hessian inversion-free exact second order method for distributed consensus optimization
Dusan Jakovetic, Natasa Krejic, Natasa Krklec Jerinkic
We consider a standard distributed consensus optimization problem where a set of agents connected over an undirected network minimize the sum of their individual local strongly con…
Spectral Projected Subgradient Method for Nonsmooth Convex Optimization Problems
Natasa Krejic, Natasa Krklec Jerinkic, Tijana Ostojic
We consider constrained optimization problems with a nonsmooth objective function in the form of mathematical expectation. The Sample Average Approximation (SAA) is used to estimat…
A harmonic framework for stepsize selection in gradient methods
Giulia Ferrandi, Michiel E. Hochstenbach, Natasa Krejic
We study the use of inverse harmonic Rayleigh quotients with target for the stepsize selection in gradient methods for nonlinear unconstrained optimization problems. This provides…
A stochastic first-order trust-region method with inexact restoration for finite-sum minimization
Stefania Bellavia, Natasa Krejic, Benedetta Morini +1
We propose a stochastic first-order trust-region method with inexact function and gradient evaluations for solving finite-sum minimization problems. Using a suitable reformulation…
An inexact restoration-nonsmooth algorithm with variable accuracy for stochastic nonsmooth convex optimization problems in machine learning and stochastic linear complementarity problems
Natasa Krejic, Natasa Krklec Jerinkic, Tijana Ostojic
We study unconstrained optimization problems with nonsmooth and convex objective function in the form of a mathematical expectation. The proposed method approximates the expected o…
EFIX: Exact Fixed Point Methods for Distributed Optimization
Dusan Jakovetic, Natasa Krejic, Natasa Krklec Jerinkic
We consider strongly convex distributed consensus optimization over connected networks. EFIX, the proposed method, is derived using quadratic penalty approach. In more detail, we u…
Detection of Iterative Adversarial Attacks via Counter Attack
Matthias Rottmann, Kira Maag, Mathis Peyron +2
Deep neural networks (DNNs) have proven to be powerful tools for processing unstructured data. However for high-dimensional data, like images, they are inherently vulnerable to adv…
Economic inexact restoration for derivative-free expensive function minimization and applications
Ernesto G. Birgin, Natasa Krejić, José Mario Martínez
The Inexact Restoration approach has proved to be an adequate tool for handling the problem of minimizing an expensive function within an arbitrary feasible set by using different…
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…
Inexact restoration with subsampled trust-region methods for finite-sum minimization
Stefania Bellavia, Natasa Krejic, Benedetta Morini
Convex and nonconvex finite-sum minimization arises in many scientific computing and machine learning applications. Recently, first-order and second-order methods where objective f…
Exact Spectral-Like Gradient Method for Distributed Optimization
Dusan Jakovetic, Natasa Krejic, Natasa Krklec Jerinkic
Since the initial proposal in the late 80s, spectral gradient methods continue to receive significant attention, especially due to their excellent numerical performance on various…
Subsampled Inexact Newton methods for minimizing large sums of convex functions
Stefania Bellavia, Natasa Krejic, Natasa Krklec Jerinkic
This paper deals with the minimization of large sum of convex functions by Inexact Newton (IN) methods employing subsampled functions, gradients and Hessian approximations. The Con…
Distributed second order methods with increasing number of working nodes
Natasa Krklec Jerinkic, Dusan Jakovetic, Natasa Krejic +1
Recently, an idling mechanism has been introduced in the context of distributed \emph{first order} methods for minimization of a sum of nodes' local convex costs over a generic, co…
Newton-like method with diagonal correction for distributed optimization
Dragana Bajovic, Dusan Jakovetic, Natasa Krejic +1
We consider distributed optimization problems where networked nodes cooperatively minimize the sum of their locally known convex costs. A popular class of methods to solve these pr…
Distributed Gradient Methods with Variable Number of Working Nodes
Dusan Jakovetic, Dragana Bajovic, Natasa Krejic +1
We consider distributed optimization where nodes in a connected network minimize the sum of their local costs subject to a common constraint set. We propose a distributed proje…