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A particular form of the solution (1) arises from
assuming the existence of a basis function set , such that the function f (x) can be represented by a linear
combination of the basis functions in the set, as follows:
| |
(8) |
We can find the linear coefficients ck by multiplying both
sides of equation (8) by one of the basis functions
(e.g. ). Inverting the equality
| |
(9) |
where the parentheses denote the dot product, and
| |
(10) |
leads to the following explicit expression for the coefficients
ck:
| |
(11) |
Here refers to the kj component of the matrix,
which is the inverse of . The matrix is invertible as
long as the basis set of functions is linearly independent. In the
special case of an orthonormal basis, reduces to the identity
matrix:
| |
(12) |
Equation (11) is a least-squares estimate of the coefficients
ck: one can alternatively derive it by minimizing the least-squares
norm of the difference between f(x) and the linear
decomposition (8). For a given set of basis functions,
equation (11) approximates the function f(x) in formula
(1) in the least-squares sense.
Next: Solution
Up: Forward interpolation
Previous: Interpolation theory
Stanford Exploration Project
12/30/2000