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Hauptverfasser: Cheng, Cheng, Sun, Qiyu, Zheng, Cong
Format: Preprint
Veröffentlicht: 2025
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Online-Zugang:https://arxiv.org/abs/2504.14341
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author Cheng, Cheng
Sun, Qiyu
Zheng, Cong
author_facet Cheng, Cheng
Sun, Qiyu
Zheng, Cong
contents Chebyshev interpolation polynomials exhibit the exponential approximation property to analytic functions on a cube. Based on the Chebyshev interpolation polynomial approximation, we propose iterative polynomial approximation algorithms to implement the inverse filter with a polynomial graph filter of commutative graph shifts in a distributed manner. The proposed algorithms exhibit exponential convergence properties, and they can be implemented on distributed networks in which agents are equipped with a data processing subsystem for limited data storage and computation power, and with a one-hop communication subsystem for direct data exchange only with their adjacent agents. Our simulations show that the proposed polynomial approximation algorithms may converge faster than the Chebyshev polynomial approximation algorithm and the conventional gradient descent algorithm do.
format Preprint
id arxiv_https___arxiv_org_abs_2504_14341
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Iterative Polynomial Approximation Algorithms for Inverse Graph Filters
Cheng, Cheng
Sun, Qiyu
Zheng, Cong
Signal Processing
Chebyshev interpolation polynomials exhibit the exponential approximation property to analytic functions on a cube. Based on the Chebyshev interpolation polynomial approximation, we propose iterative polynomial approximation algorithms to implement the inverse filter with a polynomial graph filter of commutative graph shifts in a distributed manner. The proposed algorithms exhibit exponential convergence properties, and they can be implemented on distributed networks in which agents are equipped with a data processing subsystem for limited data storage and computation power, and with a one-hop communication subsystem for direct data exchange only with their adjacent agents. Our simulations show that the proposed polynomial approximation algorithms may converge faster than the Chebyshev polynomial approximation algorithm and the conventional gradient descent algorithm do.
title Iterative Polynomial Approximation Algorithms for Inverse Graph Filters
topic Signal Processing
url https://arxiv.org/abs/2504.14341