Search Results - "Nataša Krejić"

A stochastic first-order trust-region method with inexact restoration for finite-sum minimization by Bellavia, Stefania, Krejić, Nataša, Morini, Benedetta, Rebegoldi, Simone

“…We propose a stochastic first-order trust-region method with inexact function and gradient evaluations for solving finite-sum minimization problems. Using a…”
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A split Levenberg-Marquardt method for large-scale sparse problems by Krejić, Nataša, Malaspina, Greta, Swaenen, Lense

“…We consider large-scale nonlinear least squares problems with sparse residuals, each of them depending on a small number of variables. A decoupling procedure…”
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Tax evasion risk management using a Hybrid Unsupervised Outlier Detection method by Savić, Miloš, Atanasijević, Jasna, Jakovetić, Dušan, Krejić, Nataša

“…Big data methods are becoming an important tool for tax fraud detection around the world. Unsupervised learning approach is the dominant framework due to the…”
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Inexact restoration with subsampled trust-region methods for finite-sum minimization by Bellavia, Stefania, Krejić, Nataša, Morini, Benedetta

“…Convex and nonconvex finite-sum minimization arises in many scientific computing and machine learning applications. Recently, first-order and second-order…”
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Linear convergence rate analysis of a class of exact first-order distributed methods for weight-balanced time-varying networks and uncoordinated step sizes by Malaspina, Greta, Jakovetić, Dušan, Krejić, Nataša

“…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…”
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An inexact restoration-nonsmooth algorithm with variable accuracy for stochastic nonsmooth convex optimization problems in machine learning and stochastic linear complementarity problems by Krejić, Nataša, Krklec Jerinkić, Nataša, Ostojić, Tijana

“…We study unconstrained optimization problems with nonsmooth and convex objective function in the form of a mathematical expectation. The proposed method…”
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Subsampled inexact Newton methods for minimizing large sums of convex functions by Bellavia, Stefania, Krejić, Nataša, Krklec Jerinkić, Nataša

“…Abstract This paper deals with the minimization of a large sum of convex functions by inexact Newton (IN) methods employing subsampled functions, gradients and…”
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A non-monotone trust-region method with noisy oracles and additional sampling by Krejić, Nataša, Krklec Jerinkić, Nataša, Martínez, Ángeles, Yousefi, Mahsa

“…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,…”
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A harmonic framework for stepsize selection in gradient methods by Ferrandi, Giulia, Hochstenbach, Michiel E., Krejić, Nataša

“…We study the use of inverse harmonic Rayleigh quotients with target for the stepsize selection in gradient methods for nonlinear unconstrained optimization…”
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Distributed fixed point method for solving systems of linear algebraic equations by Jakovetić, Dušan, Krejić, Nataša, Krklec Jerinkić, Nataša, Malaspina, Greta, Micheletti, Alessandra

“…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 or…”
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Detection of Iterative Adversarial Attacks via Counter Attack by Rottmann, Matthias, Maag, Kira, Peyron, Mathis, Gottschalk, Hanno, Krejić, Nataša

“…Deep neural networks (DNNs) have proven to be powerful tools for processing unstructured data. However, for high-dimensional data, like images, they are…”
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Performance evaluation and analysis of distributed multi-agent optimization algorithms with sparsified directed communication by Fodor, Lidija, Jakovetić, Dušan, Krejić, Nataša, Jerinkić, Nataša Krklec, Škrbić, Srđan

“…There has been significant interest in distributed optimization algorithms, motivated by applications in Big Data analytics, smart grid, vehicle networks, etc…”
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Spectral projected subgradient method for nonsmooth convex optimization problems by Krejić, Nataša, Krklec Jerinkić, Nataša, Ostojić, Tijana

“…We consider constrained optimization problems with a nonsmooth objective function in the form of mathematical expectation. The Sample Average Approximation…”
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Inexact Restoration approach for minimization with inexact evaluation of the objective function by Nataša Krejić, J. M. Martínez

“…A new method is introduced for minimizing a function that can be computed only inexactly, with different levels of accuracy. The challenge is to evaluate the…”
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EFIX: Exact fixed point methods for distributed optimization by Jakovetic, Dusan, Krejic, Natasa, Krklec Jerinkic, Natasa

“…We consider strongly convex distributed consensus optimization over connected networks. EFIX, the proposed method, is derived using quadratic penalty approach…”
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Optimal trading of algorithmic orders in a liquidity fragmented market place by Kumaresan, Miles, Krejić, Nataša

“…An optimization model for the execution of algorithmic orders at multiple trading venues is herein proposed and analyzed. The optimal trajectory consists of…”
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Negative selection—a new performance measure for automated order execution by Kumaresan, Miles, Krejić, Nataša, Lončar, Sanja

“…Automated Order Execution is the dominant way of trading at stock markets. Performance of numerous execution algorithms is measured through slippage from some…”
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Nonmonotone line search methods with variable sample size by Krejic, Natasa, Jerinkic, Natasa Krklec

“…Nonmonotone line search methods for unconstrained minimization with the objective functions in the form of mathematical expectation are considered. The…”
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Distributed Gradient Methods with Variable Number of Working Nodes by Jakovetic, Dusan, Bajovic, Dragana, Krejic, Natasa, Jerinkic, Natasa Krklec

“…We consider distributed optimization where N nodes in a connected network minimize the sum of their local costs subject to a common constraint set. We propose…”
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Exact spectral-like gradient method for distributed optimization by Jakovetić, Dušan, Krejić, Nataša, Krklec Jerinkić, Nataša

“…Since the initial proposal in the late 80s, spectral gradient methods continue to receive significant attention, especially due to their excellent numerical…”
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