Real root finding for low rank linear matrices

We consider m × s matrices (with m ≥ s ) in a real affine subspace of dimension n . The problem of finding elements of low rank in such spaces finds many applications in information and systems theory, where low rank is synonymous of structure and parsimony. We design computer algebra algorithms, ba...

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Veröffentlicht in:Applicable algebra in engineering, communication and computing Jg. 31; H. 2; S. 101 - 133
Hauptverfasser: Henrion, Didier, Naldi, Simone, Din, Mohab Safey El
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2020
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Abstract We consider m × s matrices (with m ≥ s ) in a real affine subspace of dimension n . The problem of finding elements of low rank in such spaces finds many applications in information and systems theory, where low rank is synonymous of structure and parsimony. We design computer algebra algorithms, based on advanced methods for polynomial system solving, to solve this problem efficiently and exactly: the input are the rational coefficients of the matrices spanning the affine subspace as well as the expected maximum rank, and the output is a rational parametrization encoding a finite set of points that intersects each connected component of the low rank real algebraic set. The complexity of our algorithm is studied thoroughly. It is polynomial in n + m ( s - r ) n . It improves on the state-of-the-art in computer algebra and effective real algebraic geometry. Moreover, computer experiments show the practical efficiency of our approach.
AbstractList We consider m×s matrices (with m≥s) in a real affine subspace of dimension n. The problem of finding elements of low rank in such spaces finds many applications in information and systems theory, where low rank is synonymous of structure and parsimony. We design computer algebra algorithms, based on advanced methods for polynomial system solving, to solve this problem efficiently and exactly: the input are the rational coefficients of the matrices spanning the affine subspace as well as the expected maximum rank, and the output is a rational parametrization encoding a finite set of points that intersects each connected component of the low rank real algebraic set. The complexity of our algorithm is studied thoroughly. It is polynomial in n+m(s-r)n. It improves on the state-of-the-art in computer algebra and effective real algebraic geometry. Moreover, computer experiments show the practical efficiency of our approach.
The problem of finding m × s matrices (with m ≥ s) of rank r in a real affine subspace of dimension n has many applications in information and systems theory, where low rank is synonymous of structure and parsimony. We design computer algebra algorithms to solve this problem efficiently and exactly: the input are the rational coefficients of the matrices spanning the affine subspace as well as the expected maximum rank, and the output is a rational parametrization encoding a finite set of points that intersects each connected component of the low rank real algebraic set. The complexity of our algorithm is studied thoroughly. It is essentially polynomial in n+m(s−r) ; it improves on the state-of-the-art in the field. Moreover, computer experiments show the practical efficiency of our approach.
We consider m × s matrices (with m ≥ s ) in a real affine subspace of dimension n . The problem of finding elements of low rank in such spaces finds many applications in information and systems theory, where low rank is synonymous of structure and parsimony. We design computer algebra algorithms, based on advanced methods for polynomial system solving, to solve this problem efficiently and exactly: the input are the rational coefficients of the matrices spanning the affine subspace as well as the expected maximum rank, and the output is a rational parametrization encoding a finite set of points that intersects each connected component of the low rank real algebraic set. The complexity of our algorithm is studied thoroughly. It is polynomial in n + m ( s - r ) n . It improves on the state-of-the-art in computer algebra and effective real algebraic geometry. Moreover, computer experiments show the practical efficiency of our approach.
Author Naldi, Simone
Henrion, Didier
Din, Mohab Safey El
Author_xml – sequence: 1
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  surname: Henrion
  fullname: Henrion, Didier
  organization: CNRS, LAAS, Université de Toulouse; LAAS, Faculty of Electrical Engineering, Czech Technical University in Prague
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  givenname: Simone
  surname: Naldi
  fullname: Naldi, Simone
  email: simone.naldi@unilim.fr
  organization: CNRS, XLIM, UMR 7252, University of Limoges
– sequence: 3
  givenname: Mohab Safey El
  surname: Din
  fullname: Din, Mohab Safey El
  organization: CNRS, Inria, Laboratoire d’Informatique de Paris 6, Sorbonne Université
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Issue 2
Keywords 13-XX
68W30
Polynomial system solving
14Q20
12Y05
Low rank matrices
Real algebraic geometry
Symbolic computation
Language English
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References_xml – reference: GianniPatriziaMoraTeoAlgebrric solution of systems of polynomirl equations using Groebher basesApplied Algebra, Algebraic Algorithms and Error-Correcting Codes1989Berlin, HeidelbergSpringer Berlin Heidelberg24725710.1007/3-540-51082-6_83
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– reference: LasserreJ-BMoments, Positive Polynomials and Their Applications2010LondonImperial College Press1211.90007
– reference: BankBGiustiMHeintzJLecerfGMateraGSolernóPDegeneracy loci and polynomial equation solvingFound. Comput. Math.2015151159184330369410.1007/s10208-014-9214-z
– reference: AbsilP-AMahonyRSepulchreROptimization Algorithms on Matrix Manifolds2009PrincetonPrinceton University Press1147.65043
– reference: HenrionDNaldiSSafey El DinMExact algorithms for linear matrix inequalitiesSIAM J. Optim.201626425122539357459010.1137/15M1036543
– reference: FaugèreJean-CharlesFGb: A Library for Computing Gröbner BasesMathematical Software – ICMS 20102010Berlin, HeidelbergSpringer Berlin Heidelberg848710.1007/978-3-642-15582-6_17
– reference: Bonnard, B., Faugère, J-C., Jacquemard, A., Safey El Din, M., Verron, T.: Determinantal sets, singularities and application to optimal control in medical imagery. In: Proceedings of the ACM on International Symposium on Symbolic and Algebraic Computation, ISSAC ’16, pp. 103–110, ACM, New York (2016)
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– reference: OttavianiGSpaenlehauerP-JSturmfelsBExact solutions in structured low-rank approximationSIAM J. Matrix Anal. Appl.201435415211542328667910.1137/13094520X
– reference: RouillierFSolving zero-dimensional systems through the rational univariate representationAppl. Algebra Eng. Commun. Comput.199995433461169717910.1007/s002000050114
– reference: Safey El Din, M.: Raglib (Real Algebraic Geometry library), Maple package (2007)
– reference: HenrionDNaldiSSafey El DinMSpectra: a Maple library for solving linear matrix inequalities in exact arithmeticOptim. Methods Softw.20193416278389466810.1080/10556788.2017.1341505
– reference: TarskiAA Decision Method for Elementary Algebra and Geometry1951CaliforniaUniversity of California Press0044.25102
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Snippet We consider m × s matrices (with m ≥ s ) in a real affine subspace of dimension n . The problem of finding elements of low rank in such spaces finds many...
We consider m×s matrices (with m≥s) in a real affine subspace of dimension n. The problem of finding elements of low rank in such spaces finds many...
The problem of finding m × s matrices (with m ≥ s) of rank r in a real affine subspace of dimension n has many applications in information and systems theory,...
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SubjectTerms Algebraic Geometry
Algorithms
Artificial Intelligence
Computer algebra
Computer Hardware
Computer Science
Mathematical analysis
Mathematics
Matrix methods
Original Paper
Parameterization
Polynomials
Symbolic and Algebraic Manipulation
Symbolic Computation
System theory
Systems theory
Theory of Computation
Title Real root finding for low rank linear matrices
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