Zobrazeno 1 - 10
of 1 495
pro vyhledávání: '"Necoara A"'
Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which data volume exceeds the capacity of the computational resources, part
Externí odkaz:
http://arxiv.org/abs/2402.16566
Autor:
Bourkhissi, Lahcen El, Necoara, Ion
In this paper we consider a nonconvex optimization problem with nonlinear equality constraints. We assume that both, the objective function and the functional constraints, are locally smooth. For solving this problem, we propose a linearized quadrati
Externí odkaz:
http://arxiv.org/abs/2402.15639
Autor:
Nabou, Yassine, Necoara, Ion
In this paper we develop a higher-order method for solving composite (non)convex minimization problems with smooth (non)convex functional constraints. At each iteration our method approximates the smooth part of the objective function and of the cons
Externí odkaz:
http://arxiv.org/abs/2402.15022
Autor:
Singh, Nitesh Kumar, Necoara, Ion
In this paper, we consider constrained optimization problems with convex, smooth objective and constraints. We propose a new stochastic gradient algorithm, called the Stochastic Moving Ball Approximation (SMBA) method, to solve this class of problems
Externí odkaz:
http://arxiv.org/abs/2402.15016
Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission. Compressive Sensing has been used in the field of Hyperspectral Imaging as a technique to compress the large
Externí odkaz:
http://arxiv.org/abs/2401.14762
Publikováno v:
Optimization Letters, 2024
We introduce the concept of inexact first-order oracle of degree q for a possibly nonconvex and nonsmooth function, which naturally appears in the context of approximate gradient, weak level of smoothness and other situations. Our definition is less
Externí odkaz:
http://arxiv.org/abs/2401.10624
Autor:
Singh, Nitesh Kumar, Necoara, Ion
Publikováno v:
Optimization, 2023
In this paper we consider finite sum composite convex optimization problems with many functional constraints. The objective function is expressed as a finite sum of two terms, one of which admits easy computation of (sub)gradients while the other is
Externí odkaz:
http://arxiv.org/abs/2401.10616
Autor:
Lupu, Daniela, Necoara, Ion
Publikováno v:
Systems and Control Letters, 2024
Deep neural networks have revolutionized many fields, including image processing, inverse problems, text mining and more recently, give very promising results in systems and control. Neural networks with hidden layers have a strong potential as an ap
Externí odkaz:
http://arxiv.org/abs/2401.05076
Autor:
Necoara, Ion
Higher-order tensor methods were recently proposed for minimizing smooth convex and nonconvex functions. Higher-order algorithms accelerate the convergence of the classical first-order methods thanks to the higher-order derivatives used in the update
Externí odkaz:
http://arxiv.org/abs/2401.05063
Autor:
Chorobura, Flavia, Necoara, Ion
Publikováno v:
Computational Optimization and Applications, 2024
This paper deals with convex nonsmooth optimization problems. We introduce a general smooth approximation framework for the original function and apply random (accelerated) coordinate descent methods for minimizing the corresponding smooth approximat
Externí odkaz:
http://arxiv.org/abs/2401.04640