The Application of Multi-block ADMM on Isotonic Regression Problems
arXiv:1903.01054
Abstract
The multi-block ADMM has received much attention from optimization researchers due to its excellent scalability. In this paper, the multi-block ADMM is applied to solve two large-scale problems related to isotonic regression. Numerical experiments show that the multi-block ADMM is convergent when the chosen parameter is small enough and the multi-block ADMM scales well compared with baselines.
arXiv commentAccepted by 11th Workshop on Optimization for Machine Learning (OPT 2019), co-located with NeurIPS 2019
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