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

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Sra,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C0D8-3
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.