Locating eigenvalues of quadratic matrix polynomials
The location of the roots of a quadratic scalar polynomial may be identified from its coefficients. This paper shows that when the coefficients of the polynomial are square matrices, then appropriate generalizations of some of these statements hold for the eigenvalues of the resulting quadratic matr...
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Veröffentlicht in: | Linear algebra and its applications 2022-09, Vol.649, p.452-490 |
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description | The location of the roots of a quadratic scalar polynomial may be identified from its coefficients. This paper shows that when the coefficients of the polynomial are square matrices, then appropriate generalizations of some of these statements hold for the eigenvalues of the resulting quadratic matrix polynomial. The locations of the eigenvalues are described with respect to the imaginary axis, the unit circle or the real line. The results lead to upper bounds on some important distances associated with quadratic matrix polynomials. The principal tool used is an eigenvalue localization technique using block Geršgorin sets applied to certain linearizations of these polynomials that come from well known vector spaces. New bounds on the eigenvalues of the matrix polynomial arising from these localizations are also presented. |
doi_str_mv | 10.1016/j.laa.2022.05.014 |
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This paper shows that when the coefficients of the polynomial are square matrices, then appropriate generalizations of some of these statements hold for the eigenvalues of the resulting quadratic matrix polynomial. The locations of the eigenvalues are described with respect to the imaginary axis, the unit circle or the real line. The results lead to upper bounds on some important distances associated with quadratic matrix polynomials. The principal tool used is an eigenvalue localization technique using block Geršgorin sets applied to certain linearizations of these polynomials that come from well known vector spaces. New bounds on the eigenvalues of the matrix polynomial arising from these localizations are also presented.</description><identifier>ISSN: 0024-3795</identifier><identifier>EISSN: 1873-1856</identifier><identifier>DOI: 10.1016/j.laa.2022.05.014</identifier><language>eng</language><publisher>Amsterdam: Elsevier Inc</publisher><subject>Block Geršgorin sets ; Eigenvalue localizations ; Eigenvalues ; Linear algebra ; Mathematical analysis ; Polynomials ; Quadratic matrix polynomials ; Structured quadratic matrix polynomials ; Upper bounds ; Vector spaces</subject><ispartof>Linear algebra and its applications, 2022-09, Vol.649, p.452-490</ispartof><rights>2022 Elsevier Inc.</rights><rights>Copyright American Elsevier Company, Inc. 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This paper shows that when the coefficients of the polynomial are square matrices, then appropriate generalizations of some of these statements hold for the eigenvalues of the resulting quadratic matrix polynomial. The locations of the eigenvalues are described with respect to the imaginary axis, the unit circle or the real line. The results lead to upper bounds on some important distances associated with quadratic matrix polynomials. The principal tool used is an eigenvalue localization technique using block Geršgorin sets applied to certain linearizations of these polynomials that come from well known vector spaces. New bounds on the eigenvalues of the matrix polynomial arising from these localizations are also presented.</description><subject>Block Geršgorin sets</subject><subject>Eigenvalue localizations</subject><subject>Eigenvalues</subject><subject>Linear algebra</subject><subject>Mathematical analysis</subject><subject>Polynomials</subject><subject>Quadratic matrix polynomials</subject><subject>Structured quadratic matrix polynomials</subject><subject>Upper bounds</subject><subject>Vector spaces</subject><issn>0024-3795</issn><issn>1873-1856</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kE9LAzEQxYMoWKsfwNuC510n_zZZPEnRKhS86DnE7GzJst20ybbYb29KPXt6MPPezONHyD2FigKtH_tqsLZiwFgFsgIqLsiMasVLqmV9SWYATJRcNfKa3KTUA4BQwGZErIKzkx_XBfo1jgc77DEVoSt2e9vGvHHFxk7R_xTbMBzHsPF2SLfkqsuCd386J1-vL5-Lt3L1sXxfPK9Kx0BNZdO0nUap0QqwiguqsQbuVM2UVrSjUmmBoJwWnex0zVEJrSnLI_hulWj4nDyc725j2OVek-nDPo75pWGKU6FqCSy76NnlYkgpYme20W9sPBoK5gTH9CbDMSc4BqTJcHLm6ZzBXP_gMZrkPI4OWx_RTaYN_p_0L1dVaqU</recordid><startdate>20220915</startdate><enddate>20220915</enddate><creator>Roy, Nandita</creator><creator>Bora, Shreemayee</creator><general>Elsevier Inc</general><general>American Elsevier Company, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-0021-1591</orcidid><orcidid>https://orcid.org/0000-0001-9410-9944</orcidid></search><sort><creationdate>20220915</creationdate><title>Locating eigenvalues of quadratic matrix polynomials</title><author>Roy, Nandita ; Bora, Shreemayee</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c207t-99df8e58ea40a73418e603c7627871f15784e07c84f5f863e748812e070bd7493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Block Geršgorin sets</topic><topic>Eigenvalue localizations</topic><topic>Eigenvalues</topic><topic>Linear algebra</topic><topic>Mathematical analysis</topic><topic>Polynomials</topic><topic>Quadratic matrix polynomials</topic><topic>Structured quadratic matrix polynomials</topic><topic>Upper bounds</topic><topic>Vector spaces</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Roy, Nandita</creatorcontrib><creatorcontrib>Bora, Shreemayee</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Linear algebra and its applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Roy, Nandita</au><au>Bora, Shreemayee</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Locating eigenvalues of quadratic matrix polynomials</atitle><jtitle>Linear algebra and its applications</jtitle><date>2022-09-15</date><risdate>2022</risdate><volume>649</volume><spage>452</spage><epage>490</epage><pages>452-490</pages><issn>0024-3795</issn><eissn>1873-1856</eissn><abstract>The location of the roots of a quadratic scalar polynomial may be identified from its coefficients. This paper shows that when the coefficients of the polynomial are square matrices, then appropriate generalizations of some of these statements hold for the eigenvalues of the resulting quadratic matrix polynomial. The locations of the eigenvalues are described with respect to the imaginary axis, the unit circle or the real line. The results lead to upper bounds on some important distances associated with quadratic matrix polynomials. The principal tool used is an eigenvalue localization technique using block Geršgorin sets applied to certain linearizations of these polynomials that come from well known vector spaces. New bounds on the eigenvalues of the matrix polynomial arising from these localizations are also presented.</abstract><cop>Amsterdam</cop><pub>Elsevier Inc</pub><doi>10.1016/j.laa.2022.05.014</doi><tpages>39</tpages><orcidid>https://orcid.org/0000-0002-0021-1591</orcidid><orcidid>https://orcid.org/0000-0001-9410-9944</orcidid></addata></record> |
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subjects | Block Geršgorin sets Eigenvalue localizations Eigenvalues Linear algebra Mathematical analysis Polynomials Quadratic matrix polynomials Structured quadratic matrix polynomials Upper bounds Vector spaces |
title | Locating eigenvalues of quadratic matrix polynomials |
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