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  Solving large-scale nonnegative least squares using an adaptive non-monotonic method

Sra, S., Kim, D., & Dhillon, I. (2010). Solving large-scale nonnegative least squares using an adaptive non-monotonic method. Poster presented at 24th European Conference on Operational Research (EURO XXIV), Lisboa, Portugal.

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Sra, S1, 2, Author           
Kim, D, Author
Dhillon, I, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We present an efficient algorithm for large-scale non-negative least-squares
(NNLS). We solve NNLS by extending the unconstrained quadratic optimization
method of Barzilai and Borwein (BB) to handle nonnegativity constraints.
Our approach is simple yet efficient. It differs from other constrained BB variants
as: (i) it uses a specific subset of variables for computing BB steps; and
(ii) it scales these steps adaptively to ensure convergence. We compare our
method with both established convex solvers and specialized NNLS methods,
and observe highly competitive empirical performance.

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 Dates: 2010-07
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: BibTex Citekey: 6521
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Title: 24th European Conference on Operational Research (EURO XXIV)
Place of Event: Lisboa, Portugal
Start-/End Date: 2010-07-11 - 2010-07-14

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Title: 24th European Conference on Operational Research (EURO XXIV)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 223 Identifier: -