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Nicola Mastronardi

dblp:85/872 · DBLP profile ↗
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5ranked-venue papers
0as first author
0since 2021 · last 2015
0000-0002-0045-2862ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Applied, interdisciplinary, general and emerging computing · 2Systems, architecture and hardware · 1Human-computer interaction and ubiquitous computing · 1Theory of computation · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Algorithms and data structures · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithms and data structures › numerical linear algebra
eigenvalue computation		 0.112008
Computing a Lower Bound of the Smallest Eigenvalue of a Symmetric Positive-Definite Toeplitz Matrix · IEEE Trans. Inf. Theory 2008
Algorithms and data structures
numerical linear algebra		 0.112008
Computing a Lower Bound of the Smallest Eigenvalue of a Symmetric Positive-Definite Toeplitz Matrix · IEEE Trans. Inf. Theory 2008
Algorithms and data structures › numerical linear algebra › structured matrices
toeplitz matrix		 0.112008
Computing a Lower Bound of the Smallest Eigenvalue of a Symmetric Positive-Definite Toeplitz Matrix · IEEE Trans. Inf. Theory 2008
YearPublicationVenuePosition
2015 Sales Forecasting Models in the Fresh Food Supply Chain
Gabriella Dellino, Teresa Laudadio, Renato Mari, Nicola Mastronardi, Carlo Meloni
 ICORES4
2008 Computing a Lower Bound of the Smallest Eigenvalue of a Symmetric Positive-Definite Toeplitz Matrix
abstract
In this correspondence, several algorithms to compute a lower bound of the smallest eigenvalue of a symmetric positive-definite Toeplitz matrix are described and compared in terms of accuracy and computational efficiency. Exploiting the Toeplitz structure of the considered matrix, new theoretical insights are derived and an efficient implementation of some of the aforementioned algorithms is provided.
Teresa Laudadio, Nicola Mastronardi, Marc Van Barel
 IEEE Trans. Inf. Theory2
2004 On Computing the Spectral Decomposition of Symmetric Arrowhead Matrices
Fasma Diele, Nicola Mastronardi, Marc Van Barel, Ellen Van Camp
 ICCSA (2)2
2000 Advanced Signal Processing Methods for Quantitation of Resonances in Magnetic Resonance Spectra
abstract
A careful comparison between principal component analysis (PCA) and Hankel total least squares (HTLS) based methods for estimating the resonances in sets of magnetic resonance (MR) spectra is presented. After a description of the methods, we compare their performance on simulated data sets and discuss their advantages and limitations.
Yu Wang 0008, Sabine Van Huÿel, Leentje Vanhamme, Nicola Mastronardi, Paul Van Hecke
 CBMS4
1993 A Parallel Version of the Cyclic Reduction Algorithm on a Hypercube
Pierluigi Amodio, Nicola Mastronardi
 Parallel Comput.2