Distance algorithm based procedure for nonnegative least squares

Róbert Rajkó, Yu Zheng

Research output: Contribution to journalArticle

2 Citations (Scopus)


In chemistry and many other scientific disciplines, non-negativity-constrained estimation of models is of practical importance. The time required for estimating true least squares non-negativity-constrained models is typically many times longer than that for estimating unconstrained models. That is why it is necessary to find faster and faster non-negative least squares (NNLS) algorithms. Very recently, the distance algorithm has been developed, and this algorithm can be adapted to solve NNLS regression task faster (in some cases) than the conventional algorithms. Based on some simulated investigation, DA_NNLS was the fastest for small-sized and medium-sized linear regression tasks. The visualization (geometry) of the NNLS task being solved by our new algorithm is discussed as well. Besides linear algebra, convex geometrical concepts and tools are suggested to investigate, to use, and to develop in chemometrics for exploiting the geometry of chemometry.

Original languageEnglish
Pages (from-to)691-695
Number of pages5
JournalJournal of Chemometrics
Issue number9
Publication statusPublished - Jan 1 2014



  • Convex geometry
  • Distance algorithm (DA)
  • Geometry of chemometry
  • Non-negative least squares (NNLS)

ASJC Scopus subject areas

  • Analytical Chemistry
  • Applied Mathematics

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