Efficient ADMM-Based Algorithm for Regularized Minimax Approximation
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 approx...
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| Published in | IEEE signal processing letters Vol. 30; pp. 210 - 214 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Online Access | Get full text |
| ISSN | 1070-9908 1558-2361 |
| DOI | 10.1109/LSP.2023.3253053 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Shentu, Xuanyue Wang, Tianlei Cao, Jiuwen Lai, Xiaoping |
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| SubjectTerms | ADMM Algorithms Approximation Approximation algorithms Computational geometry Convex functions Convexity digital filter design Digital filters Finite impulse response filters Machine learning Machine learning algorithms Mathematical analysis Minimax approximation Minimax technique Neural networks Optimization Programming Signal processing algorithms |
| Title | Efficient ADMM-Based Algorithm for Regularized Minimax Approximation |
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