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Efficient ADMM-Based Algorithm for Regularized Minimax Approximation
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DOI：10.1109/LSP.2023.3253053.png)
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Abstract
En 中文
Minimax approximations have found many applications but are lack of efficient solution algorithms for large-scale problems. Based on the alternating direction method of multipliers (ADMM) for convex optimization, this letter presents an efficient scalarwise algorithm for a regularized minimax approximation problem. The ADMM-based algorithm is then applied in the minimax design of two-dimensional (2-D) digital filters and the training of randomized neural networks for regression on a realworld benchmark dataset. Experimental results demonstrate the fast convergence rate and low computational complexity of the proposed algorithm, as well as the good approximation/prediction performance of the learned approximation model.
Keywords:
Minimax approximation
digital filter design
machine learning
ADMM
Journal
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9.6
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1.1W
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1.7W