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

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Original languageEnglish
Pages (from-to)1298-1309
Number of pages12
JournalJournal of Supercomputing
Issue number3
Early online date19 May 2018
Publication statusPublished - Mar 2019
Publication typeA1 Journal article-refereed


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.


  • Convex optimization, Multicore, Newton algorithm, Parallel computing, Sparse representation

Publication forum classification

Field of science, Statistics Finland