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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.
