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Polyharmonic spline : ウィキペディア英語版
Polyharmonic spline
Polyharmonic splines are used for
function approximation and data interpolation.
They are very useful for interpolation of scattered data
in many dimensions. A special case are thin plate splines.〔R.L. Harder and R.N. Desmarais: (Interpolation using surface splines ). Journal of Aircraft, 1972, Issue 2, pp. 189-191〕〔J. Duchon: Splines minimizing rotation-invariant semi-norms in Sobolev spaces. Constructive Theory of Functions of Several Variables, W. Schempp and K. Zeller (eds), Springer, Berlin, pp.85-100〕
== Definition ==


y(\mathbf) \, = \, \sum_^N w_i \, \phi(||\mathbf - \mathbf_i||) +
\mathbf^T \, \begin 1 \\ \mathbf \end


where
* \mathbf = (x_2, \cdots, x_ )^T is a real-valued vector of nx independent variables,
* \mathbf_i = (c_, \cdots, c_ )^T are N vectors of the same size as \mathbf (often called centers) that the interpolated curve shall pass
* \mathbf = (w_2, \cdots, w_N )^T are the N weights of the basis functions.
* \mathbf = (v_2, \cdots, v_ )^T are the nx+1 weights of the polynomial.
* The linear polynomial with the weighting factors \mathbf improves the interpolation close to the "boundary" and especially the extrapolation "outside" of the centers \mathbf_i. If this is not desired, this term can also be removed (see also figure below).
The basis functions of polyharmonic splines are radial basis functions of the form:


\begin
\phi(r) = \begin
r^k & \mbox k=1,3,5,\dots, \\
r^k \ln(r) & \mbox k=2,4,6,\dots
\end \\()
r = ||\mathbf - \mathbf_i||_2
= \sqrt_i)^T \, (\mathbf - \mathbf_i) }
\end


Other values of exponent k are not useful (such as \phi(r) = r^2 ),
because a solution of the interpolation problem might no
longer exist. To avoid problems at r=0 (since ln(0) = -∞), the polyharmonic splines with the natural logarithm might be implemented as:


\phi(r) = \begin
r^ \ln(r^r) & \mbox r < 1 \\
r^k \ln(r) & \mbox r \ge 1
\end


The weights w_i and v_j are determined such that the function
passes through N given points
(\mathbf_i, y_i) (i=1,2,...,N) and fulfill
the nx+1 orthogonality conditions:


0 = \sum_^N w_i, \;\; 0 = \sum_^N w_i \, c_ \;\;\; (j=1,2,...,nx)


To compute the weights, a symmetric, linear system of equations has to be
solved:


\begin
\mathbf & \mathbf^T \\
\mathbf & \mathbf \end
\;
\begin
\mathbf \\
\mathbf
\end \; = \;
\begin
\mathbf \\
\mathbf
\end\;\;\;\;


where


A_ = \phi(||\mathbf_i - \mathbf_j||), \;\;\;
\mathbf =
\begin
1 & 1 & \cdots & 1 \\
\mathbf_1 & \mathbf_2 & \cdots & \mathbf_
\end, \;\;\;
\mathbf = (y_2, \cdots, y_N )^T


Under very mild conditions (essentially, that at least nx+1 points
are not in a subspace; e.g. for nx=2 that at least 3 points are not
on a straight line), the system matrix of the linear system of equations
is nonsingular and therefore a unique solution of the equation system
exists.
Once the weights are determined, interpolation requires to just evaluate the
top most formula for the provided \mathbf.
Many practical details to implement and use polyharmonic splines are given in the book of Fasshauer.〔G.F. Fasshauer G.F.: (Meshfree Approximation Methods with MATLAB ). World Scientific Publishing Company, 2007, ISPN-10: 9812706348〕 In Iske〔
A. Iske: (Multiresolution Methods in Scattered Data Modelling ), Lecture Notes in Computational Science and Engineering, 2004, Vol. 37, ISBN 3-540-20479-2, Springer-Verlag, Heidelberg.〕 polyharmonic splines are treated as special cases of other multiresolution methods in scattered data modelling.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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