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

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons76142

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

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Sra, S., Kim, D., & Dhillon, I. (2010). Solving large-scale nonnegative least squares using an adaptive non-monotonic method.


Cite as: http://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.