Frugal Coordinate Descent for Large-Scale NNLS
DOI:
https://doi.org/10.1609/aaai.v26i1.8432Abstract
The Nonnegative Least Squares (NNLS) formulation arises in many important regression problems. We present a novel coordinate descent method which differs from previous approaches in that we do not explicitly maintain complete gradient information. Empirical evidence shows that our approach outperforms a state-of-the-art NNLS solver in computation time for calculating radiation dosage for cancer treatment problems.
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Published
2021-09-20
How to Cite
Potluru, V. (2021). Frugal Coordinate Descent for Large-Scale NNLS. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 2451-2452. https://doi.org/10.1609/aaai.v26i1.8432
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Student Abstract Track