L∞Error Bounds in Partial Deconvolution of the Inverse Gaussian Pulse
When a C∞approximation to the Dirac δ-function, in the form of an inverse Gaussian pulse, is used as input into a linear time invariant system, the output waveform is an approximation to that system's Green's function, in which the singularities have been smoothed out. The ill-posed deconv...
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Veröffentlicht in: | SIAM journal on applied mathematics 1985-12, Vol.45 (6), p.1029-1038 |
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description | When a C∞approximation to the Dirac δ-function, in the form of an inverse Gaussian pulse, is used as input into a linear time invariant system, the output waveform is an approximation to that system's Green's function, in which the singularities have been smoothed out. The ill-posed deconvolution problem for the output signal aims at reconstructing these singularities. By exploiting the smoothing properties of the inverse Gaussian kernel, we prove that partial deconvolution of the output waveform, given L2a priori bounds on the data noise and the unknown Green's function, results in L∞error bounds for the regularized solution and its derivatives. Consequently, when the L2norm of the output noise is sufficiently small, partial deconvolution is a pointwise reliable C∞function, which in turn approximates the desired Green's function in many applications. |
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The ill-posed deconvolution problem for the output signal aims at reconstructing these singularities. By exploiting the smoothing properties of the inverse Gaussian kernel, we prove that partial deconvolution of the output waveform, given L2a priori bounds on the data noise and the unknown Green's function, results in L∞error bounds for the regularized solution and its derivatives. Consequently, when the L2norm of the output noise is sufficiently small, partial deconvolution is a pointwise reliable C∞function, which in turn approximates the desired Green's function in many applications.</description><identifier>ISSN: 0036-1399</identifier><identifier>EISSN: 1095-712X</identifier><identifier>CODEN: SMJMAP</identifier><language>eng</language><publisher>Philadelphia, PA: Society for Industrial and Applied Mathematics</publisher><subject>A priori knowledge ; Approximation ; Conductive heat transfer ; Error bounds ; Exact sciences and technology ; Greens function ; Heat equation ; Ill posed problems ; Mathematical functions ; Mathematics ; Numerical analysis ; Numerical analysis. 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The ill-posed deconvolution problem for the output signal aims at reconstructing these singularities. By exploiting the smoothing properties of the inverse Gaussian kernel, we prove that partial deconvolution of the output waveform, given L2a priori bounds on the data noise and the unknown Green's function, results in L∞error bounds for the regularized solution and its derivatives. Consequently, when the L2norm of the output noise is sufficiently small, partial deconvolution is a pointwise reliable C∞function, which in turn approximates the desired Green's function in many applications.</description><subject>A priori knowledge</subject><subject>Approximation</subject><subject>Conductive heat transfer</subject><subject>Error bounds</subject><subject>Exact sciences and technology</subject><subject>Greens function</subject><subject>Heat equation</subject><subject>Ill posed problems</subject><subject>Mathematical functions</subject><subject>Mathematics</subject><subject>Numerical analysis</subject><subject>Numerical analysis. Scientific computation</subject><subject>Partial differential equations, miscellaneous problems</subject><subject>Sciences and techniques of general use</subject><subject>Signal noise</subject><subject>Waveforms</subject><issn>0036-1399</issn><issn>1095-712X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1985</creationdate><recordtype>article</recordtype><recordid>eNo9jE9KxDAcRoMoWEdv4CILt4X8aZJmqeNYBwq6mIW74dckxZSaDEk74A08hYfzJBZGXL3Fe993hgpKtCgVZW_nqCCEy5JyrS_RVc4DIZTKSheoaX--vjcpxYQf4hxsxj7gV0iThxE_OhPDMY7z5GPAscfTu8PbcHQpO9zAnLOHpZ7H7K7RRQ8Lb_64QrunzW79XLYvzXZ935aDIKJ0rGYVtVQqqymIHqwxjEnHnVDcEN7VzEAnhbVcaQWS9bTu6s7ZatkwzfgK3Z1uD5ANjH2CYHzeH5L_gPS5r1WlCRVLdnvKhjzF9K8ZXSQj_BddwlNJ</recordid><startdate>19851201</startdate><enddate>19851201</enddate><creator>Carasso, Alfred S.</creator><creator>Hsu, Nelson N.</creator><general>Society for Industrial and Applied Mathematics</general><scope>IQODW</scope></search><sort><creationdate>19851201</creationdate><title>L∞Error Bounds in Partial Deconvolution of the Inverse Gaussian Pulse</title><author>Carasso, Alfred S. ; Hsu, Nelson N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-j505-e28241d167d91a5fadcc226e3e573c03b82cab65dd3797a62f18b8bed4d162923</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1985</creationdate><topic>A priori knowledge</topic><topic>Approximation</topic><topic>Conductive heat transfer</topic><topic>Error bounds</topic><topic>Exact sciences and technology</topic><topic>Greens function</topic><topic>Heat equation</topic><topic>Ill posed problems</topic><topic>Mathematical functions</topic><topic>Mathematics</topic><topic>Numerical analysis</topic><topic>Numerical analysis. Scientific computation</topic><topic>Partial differential equations, miscellaneous problems</topic><topic>Sciences and techniques of general use</topic><topic>Signal noise</topic><topic>Waveforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Carasso, Alfred S.</creatorcontrib><creatorcontrib>Hsu, Nelson N.</creatorcontrib><collection>Pascal-Francis</collection><jtitle>SIAM journal on applied mathematics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Carasso, Alfred S.</au><au>Hsu, Nelson N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>L∞Error Bounds in Partial Deconvolution of the Inverse Gaussian Pulse</atitle><jtitle>SIAM journal on applied mathematics</jtitle><date>1985-12-01</date><risdate>1985</risdate><volume>45</volume><issue>6</issue><spage>1029</spage><epage>1038</epage><pages>1029-1038</pages><issn>0036-1399</issn><eissn>1095-712X</eissn><coden>SMJMAP</coden><abstract>When a C∞approximation to the Dirac δ-function, in the form of an inverse Gaussian pulse, is used as input into a linear time invariant system, the output waveform is an approximation to that system's Green's function, in which the singularities have been smoothed out. The ill-posed deconvolution problem for the output signal aims at reconstructing these singularities. By exploiting the smoothing properties of the inverse Gaussian kernel, we prove that partial deconvolution of the output waveform, given L2a priori bounds on the data noise and the unknown Green's function, results in L∞error bounds for the regularized solution and its derivatives. Consequently, when the L2norm of the output noise is sufficiently small, partial deconvolution is a pointwise reliable C∞function, which in turn approximates the desired Green's function in many applications.</abstract><cop>Philadelphia, PA</cop><pub>Society for Industrial and Applied Mathematics</pub><tpages>10</tpages></addata></record> |
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source | JSTOR Mathematics & Statistics; JSTOR Archive Collection A-Z Listing; LOCUS - SIAM's Online Journal Archive |
subjects | A priori knowledge Approximation Conductive heat transfer Error bounds Exact sciences and technology Greens function Heat equation Ill posed problems Mathematical functions Mathematics Numerical analysis Numerical analysis. Scientific computation Partial differential equations, miscellaneous problems Sciences and techniques of general use Signal noise Waveforms |
title | L∞Error Bounds in Partial Deconvolution of the Inverse Gaussian Pulse |
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