Polynomial–Exponential Decomposition From Moments

We analyze the decomposition problem of multivariate polynomial–exponential functions from their truncated series and present new algorithms to compute their decomposition. Using the duality between polynomials and formal power series, we first show how the elements in the dual of an Artinian algebr...

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Veröffentlicht in:Foundations of computational mathematics Jg. 18; H. 6; S. 1435 - 1492
1. Verfasser: Mourrain, Bernard
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.12.2018
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Abstract We analyze the decomposition problem of multivariate polynomial–exponential functions from their truncated series and present new algorithms to compute their decomposition. Using the duality between polynomials and formal power series, we first show how the elements in the dual of an Artinian algebra correspond to polynomial–exponential functions. They are also the solutions of systems of partial differential equations with constant coefficients. We relate their representation to the inverse system of the isolated points of the characteristic variety. Using the properties of Hankel operators, we establish a correspondence between polynomial–exponential series and Artinian Gorenstein algebras. We generalize Kronecker theorem to the multivariate case, by showing that the symbol of a Hankel operator of finite rank is a polynomial–exponential series and by connecting the rank of the Hankel operator with the decomposition of the symbol. A generalization of Prony’s approach to multivariate decomposition problems is presented, exploiting eigenvector methods for solving polynomial equations. We show how to compute the frequencies and weights of a minimal polynomial–exponential decomposition, using the first coefficients of the series. A key ingredient of the approach is the flat extension criteria, which leads to a multivariate generalization of a rank condition for a Carathéodory–Fejér decomposition of multivariate Hankel matrices. A new algorithm is given to compute a basis of the Artinian Gorenstein algebra, based on a Gram–Schmidt orthogonalization process and to decompose polynomial–exponential series. A general framework for the applications of this approach is described and illustrated in different problems. We provide Kronecker-type theorems for convolution operators, showing that a convolution operator (or a cross-correlation operator) is of finite rank, if and only if, its symbol is a polynomial–exponential function, and we relate its rank to the decomposition of its symbol. We also present Kronecker-type theorems for the reconstruction of measures as weighted sums of Dirac measures from moments and for the decomposition of polynomial–exponential functions from values. Finally, we describe an application of this method for the sparse interpolation of polylog functions from values.
AbstractList We analyze the decomposition problem of multivariate polynomial-exponential functions from truncated series and present new algorithms to compute their decomposition. Using the duality between polynomials and formal power series, we first show how the elements in the dual of an Artinian algebra correspond to polynomial-exponential functions. They are also the solutions of systems of partial differential equations with constant coefficients. We relate their representation to the inverse system of the roots of the characteristic variety. Using the properties of Hankel operators, we establish a correspondence between polynomial exponential series and Artinian Gorenstein algebras. We generalize Kronecker theorem to the multivariate case, by showing that the symbol of a Hankel operator of finite rank is a polynomial-exponential series and by connecting the rank of the Hankel operator with the decomposition of the symbol. A generalization of Prony's approach to multivariate decomposition problems is presented , exploiting eigenvector methods for solving polynomial equations. We show how to compute the frequencies and weights of a minimal polynomial-exponential decomposition , using the first coefficients of the series. A key ingredient of the approach is the flat extension criteria, which leads to a multivariate generalization of a rank condition for a Carathéodory-Fejér decomposition of multivariate Hankel matrices. A new algorithm is given to compute a basis of the Artinian Gorenstein algebra, based on a Gram-Schmidt orthogonalization process and to decompose polynomial-exponential series. A general framework for the applications of this approach is described and illustrated in different problems. We provide Kronecker-type theorems for convolution operators, showing that a convolution operator (or a cross-correlation operator) is of finite rank, if and only if, its symbol is a polynomial-exponential function, and we relate its rank to the decomposition of its symbol. We also present Kronecker-type theorems for the reconstruction of measures as weighted sums of Dirac measures from moments and for the decomposition of polynomial-exponential functions from values. Finally, we describe an application of this method for the sparse interpolation of polylog functions from values.
We analyze the decomposition problem of multivariate polynomial–exponential functions from their truncated series and present new algorithms to compute their decomposition. Using the duality between polynomials and formal power series, we first show how the elements in the dual of an Artinian algebra correspond to polynomial–exponential functions. They are also the solutions of systems of partial differential equations with constant coefficients. We relate their representation to the inverse system of the isolated points of the characteristic variety. Using the properties of Hankel operators, we establish a correspondence between polynomial–exponential series and Artinian Gorenstein algebras. We generalize Kronecker theorem to the multivariate case, by showing that the symbol of a Hankel operator of finite rank is a polynomial–exponential series and by connecting the rank of the Hankel operator with the decomposition of the symbol. A generalization of Prony’s approach to multivariate decomposition problems is presented, exploiting eigenvector methods for solving polynomial equations. We show how to compute the frequencies and weights of a minimal polynomial–exponential decomposition, using the first coefficients of the series. A key ingredient of the approach is the flat extension criteria, which leads to a multivariate generalization of a rank condition for a Carathéodory–Fejér decomposition of multivariate Hankel matrices. A new algorithm is given to compute a basis of the Artinian Gorenstein algebra, based on a Gram–Schmidt orthogonalization process and to decompose polynomial–exponential series. A general framework for the applications of this approach is described and illustrated in different problems. We provide Kronecker-type theorems for convolution operators, showing that a convolution operator (or a cross-correlation operator) is of finite rank, if and only if, its symbol is a polynomial–exponential function, and we relate its rank to the decomposition of its symbol. We also present Kronecker-type theorems for the reconstruction of measures as weighted sums of Dirac measures from moments and for the decomposition of polynomial–exponential functions from values. Finally, we describe an application of this method for the sparse interpolation of polylog functions from values.
Author Mourrain, Bernard
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  givenname: Bernard
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  email: bernard.mourrain@inria.fr
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Issue 6
Keywords Polynomial–exponential series
68W30
Prony
Differential equation
14Q20
15B05
Artinian
Hankel matrix
Interpolation
Gorenstein
Moment
47B35
Sparse representation
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15B05 1
Language English
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Snippet We analyze the decomposition problem of multivariate polynomial–exponential functions from their truncated series and present new algorithms to compute their...
We analyze the decomposition problem of multivariate polynomial-exponential functions from truncated series and present new algorithms to compute their...
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SubjectTerms Algebra
Algebraic Geometry
Algorithms
Applications of Mathematics
Computer Science
Convolution
Decomposition
Economics
Eigenvectors
Exponential functions
Hankel matrices
Interpolation
Linear and Multilinear Algebras
Math Applications in Computer Science
Mathematics
Mathematics and Statistics
Matrix Theory
Numerical Analysis
Operators (mathematics)
Partial differential equations
Polynomials
Power series
Theorems
Title Polynomial–Exponential Decomposition From Moments
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https://inria.hal.science/hal-01367730
Volume 18
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