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Analysis of an efficient parallel implementation of active-set Newton algorithm

Tutkimustuotosvertaisarvioitu

Standard

Analysis of an efficient parallel implementation of active-set Newton algorithm. / San Juan Sebastián, Pablo; Virtanen, Tuomas; Garcia-Molla, Victor M.; Vidal, Antonio M.

julkaisussa: Journal of Supercomputing, Vuosikerta 75, Nro 3, 03.2019, s. 1298-1309.

Tutkimustuotosvertaisarvioitu

Harvard

San Juan Sebastián, P, Virtanen, T, Garcia-Molla, VM & Vidal, AM 2019, 'Analysis of an efficient parallel implementation of active-set Newton algorithm' Journal of Supercomputing, Vuosikerta. 75, Nro 3, Sivut 1298-1309. https://doi.org/10.1007/s11227-018-2423-5

APA

San Juan Sebastián, P., Virtanen, T., Garcia-Molla, V. M., & Vidal, A. M. (2019). Analysis of an efficient parallel implementation of active-set Newton algorithm. Journal of Supercomputing, 75(3), 1298-1309. https://doi.org/10.1007/s11227-018-2423-5

Vancouver

San Juan Sebastián P, Virtanen T, Garcia-Molla VM, Vidal AM. Analysis of an efficient parallel implementation of active-set Newton algorithm. Journal of Supercomputing. 2019 maalis;75(3):1298-1309. https://doi.org/10.1007/s11227-018-2423-5

Author

San Juan Sebastián, Pablo ; Virtanen, Tuomas ; Garcia-Molla, Victor M. ; Vidal, Antonio M. / Analysis of an efficient parallel implementation of active-set Newton algorithm. Julkaisussa: Journal of Supercomputing. 2019 ; Vuosikerta 75, Nro 3. Sivut 1298-1309.

Bibtex - Lataa

@article{5ffbd396d2074f948876597559b8f419,
title = "Analysis of an efficient parallel implementation of active-set Newton algorithm",
abstract = "This paper presents an analysis of an efficient parallel implementation of the active-set Newton algorithm (ASNA), which is used to estimate the nonnegative weights of linear combinations of the atoms in a large-scale dictionary to approximate an observation vector by minimizing the Kullback–Leibler divergence between the observation vector and the approximation. The performance of ASNA has been proved in previous works against other state-of-the-art methods. The implementations analysed in this paper have been developed in C, using parallel programming techniques to obtain a better performance in multicore architectures than the original MATLAB implementation. Also a hardware analysis is performed to check the influence of CPU frequency and number of CPU cores in the different implementations proposed. The new implementations allow ASNA algorithm to tackle real-time problems due to the execution time reduction obtained.",
keywords = "Convex optimization, Multicore, Newton algorithm, Parallel computing, Sparse representation",
author = "{San Juan Sebasti{\'a}n}, Pablo and Tuomas Virtanen and Garcia-Molla, {Victor M.} and Vidal, {Antonio M.}",
year = "2019",
month = "3",
doi = "10.1007/s11227-018-2423-5",
language = "English",
volume = "75",
pages = "1298--1309",
journal = "Journal of Supercomputing",
issn = "0920-8542",
publisher = "Springer Verlag",
number = "3",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Analysis of an efficient parallel implementation of active-set Newton algorithm

AU - San Juan Sebastián, Pablo

AU - Virtanen, Tuomas

AU - Garcia-Molla, Victor M.

AU - Vidal, Antonio M.

PY - 2019/3

Y1 - 2019/3

N2 - This paper presents an analysis of an efficient parallel implementation of the active-set Newton algorithm (ASNA), which is used to estimate the nonnegative weights of linear combinations of the atoms in a large-scale dictionary to approximate an observation vector by minimizing the Kullback–Leibler divergence between the observation vector and the approximation. The performance of ASNA has been proved in previous works against other state-of-the-art methods. The implementations analysed in this paper have been developed in C, using parallel programming techniques to obtain a better performance in multicore architectures than the original MATLAB implementation. Also a hardware analysis is performed to check the influence of CPU frequency and number of CPU cores in the different implementations proposed. The new implementations allow ASNA algorithm to tackle real-time problems due to the execution time reduction obtained.

AB - This paper presents an analysis of an efficient parallel implementation of the active-set Newton algorithm (ASNA), which is used to estimate the nonnegative weights of linear combinations of the atoms in a large-scale dictionary to approximate an observation vector by minimizing the Kullback–Leibler divergence between the observation vector and the approximation. The performance of ASNA has been proved in previous works against other state-of-the-art methods. The implementations analysed in this paper have been developed in C, using parallel programming techniques to obtain a better performance in multicore architectures than the original MATLAB implementation. Also a hardware analysis is performed to check the influence of CPU frequency and number of CPU cores in the different implementations proposed. The new implementations allow ASNA algorithm to tackle real-time problems due to the execution time reduction obtained.

KW - Convex optimization

KW - Multicore

KW - Newton algorithm

KW - Parallel computing

KW - Sparse representation

U2 - 10.1007/s11227-018-2423-5

DO - 10.1007/s11227-018-2423-5

M3 - Article

VL - 75

SP - 1298

EP - 1309

JO - Journal of Supercomputing

JF - Journal of Supercomputing

SN - 0920-8542

IS - 3

ER -