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

Pablo San Juan Sebastián, Tuomas Virtanen, Victor M. Garcia-Molla, Antonio M. Vidal

    Research output: Contribution to journalArticleScientificpeer-review


    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.

    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


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

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Software
    • Theoretical Computer Science
    • Information Systems
    • Hardware and Architecture


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