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Error estimates in the fast multipole method for scattering problems. Part 2 : truncation of the Gegenbauer series

Quentin Carayol, Francis Collino (2005)

ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique

We perform a complete study of the truncation error of the Gegenbauer series. This series yields an expansion of the Green kernel of the Helmholtz equation, e i | u - v | 4 π i | u - v | , which is the core of the Fast Multipole Method for the integral equations. We consider the truncated series where the summation is performed over the indices L . We prove that if v = | v | is large enough, the truncated series gives rise to an error lower than ϵ as soon as L satisfies L + 1 2 v + C W 2 3 ( K ( α ) ϵ - δ v γ ) v 1 3 where W is the Lambert function, K ( α ) depends only on α = | u | | v | and C , δ , γ are...

Error estimates in the Fast Multipole Method for scattering problems Part 2: Truncation of the Gegenbauer series

Quentin Carayol, Francis Collino (2010)

ESAIM: Mathematical Modelling and Numerical Analysis

We perform a complete study of the truncation error of the Gegenbauer series. This series yields an expansion of the Green kernel of the Helmholtz equation, e i | u - v | 4 π i | u - v | , which is the core of the Fast Multipole Method for the integral equations. We consider the truncated series where the summation is performed over the indices L . We prove that if v = | v | is large enough, the truncated series gives rise to an error lower than ϵ as soon as L satisfies L + 1 2 v + C W 2 3 ( K ( α ) ϵ - δ v γ ) v 1 3 where W is the Lambert function, K ( α ) depends only on...

Error estimates in the fast multipole method for scattering problems Part 1: Truncation of the Jacobi-Anger series

Quentin Carayol, Francis Collino (2010)

ESAIM: Mathematical Modelling and Numerical Analysis

We perform a complete study of the truncation error of the Jacobi-Anger series. This series expands every plane wave e i s ^ · v in terms of spherical harmonics { Y , m ( s ^ ) } | m | . We consider the truncated series where the summation is performed over the ( , m ) 's satisfying | m | L . We prove that if v = | v | is large enough, the truncated series gives rise to an error lower than ϵ as soon as L satisfies L + 1 2 v + C W 2 3 ( K ϵ - δ v γ ) v 1 3 where W is the Lambert function and C , K , δ , γ are pure positive constants. Numerical experiments show that this asymptotic is...

Estimates for spline projections

J. H. Bramble, A. H. Schatz (1976)

ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique

Estimating a discrete distribution via histogram selection

Nathalie Akakpo (2011)

ESAIM: Probability and Statistics

Our aim is to estimate the joint distribution of a finite sequence of independent categorical variables. We consider the collection of partitions into dyadic intervals and the associated histograms, and we select from the data the best histogram by minimizing a penalized least-squares criterion. The choice of the collection of partitions is inspired from approximation results due to DeVore and Yu. Our estimator satisfies a nonasymptotic oracle-type inequality and adaptivity properties in the minimax...

Estimating a discrete distribution via histogram selection

Nathalie Akakpo (2011)

ESAIM: Probability and Statistics

Our aim is to estimate the joint distribution of a finite sequence of independent categorical variables. We consider the collection of partitions into dyadic intervals and the associated histograms, and we select from the data the best histogram by minimizing a penalized least-squares criterion. The choice of the collection of partitions is inspired from approximation results due to DeVore and Yu. Our estimator satisfies a nonasymptotic oracle-type inequality and adaptivity properties in the minimax...

Estimation of a smoothness parameter by spline wavelets

Magdalena Meller, Natalia Jarzębkowska (2013)

Applicationes Mathematicae

We consider the smoothness parameter of a function f ∈ L²(ℝ) in terms of Besov spaces B 2 , s ( ) , s * ( f ) = s u p s > 0 : f B 2 , s ( ) . The existing results on estimation of smoothness [K. Dziedziul, M. Kucharska and B. Wolnik, J. Nonparametric Statist. 23 (2011)] employ the Haar basis and are limited to the case 0 < s*(f) < 1/2. Using p-regular (p ≥ 1) spline wavelets with exponential decay we extend them to density functions with 0 < s*(f) < p+1/2. Applying the Franklin-Strömberg wavelet p = 1, we prove that the presented estimator...

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