Mean Squared Error Minimization for Inverse Moment Problems |
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Authors: | Didier Henrion Jean B. Lasserre Martin Mevissen |
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Affiliation: | 1. CNRS, LAAS, 7 avenue du colonel Roche, 31400?, Toulouse, France 2. Univ. de Toulouse, LAAS, 31400?, Toulouse, France 3. Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 16626?, Prague, Czech Republic 4. Institut de Mathématiques de Toulouse, Univ. de Toulouse, UPS, 31400?, Toulouse, France 5. IBM Research - Ireland, Dublin Technology Campus, Damastown Ind. Park, Mulhuddart, Dublin 15, Ireland
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Abstract: | We consider the problem of approximating the unknown density (uin L^2(Omega ,lambda )) of a measure (mu ) on (Omega subset mathbb {R}^n) , absolutely continuous with respect to some given reference measure (lambda ) , only from the knowledge of finitely many moments of (mu ) . Given (din mathbb {N}) and moments of order (d) , we provide a polynomial (p_d) which minimizes the mean square error (int (u-p)^2dlambda ) over all polynomials (p) of degree at most (d) . If there is no additional requirement, (p_d) is obtained as solution of a linear system. In addition, if (p_d) is expressed in the basis of polynomials that are orthonormal with respect to (lambda ) , its vector of coefficients is just the vector of given moments and no computation is needed. Moreover (p_drightarrow u) in (L^2(Omega ,lambda )) as (drightarrow infty ) . In general nonnegativity of (p_d) is not guaranteed even though (u) is nonnegative. However, with this additional nonnegativity requirement one obtains analogous results but computing (p_dge 0) that minimizes (int (u-p)^2dlambda ) now requires solving an appropriate semidefinite program. We have tested the approach on some applications arising from the reconstruction of geometrical objects and the approximation of solutions of nonlinear differential equations. In all cases our results are significantly better than those obtained with the maximum entropy technique for estimating (u) . |
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