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polynomial interpolation
In numerical analysis, polynomial interpolation is the interpolation of a given data set by a polynomial: given some points, find a polynomial which goes exactly through these points.
== Applications ==
Polynomials can be used to approximate complicated curves, for example, the shapes of letters in typography, given a few points. A relevant application is the evaluation of the natural logarithm and trigonometric functions: pick a few known data points, create a lookup table, and interpolate between those data points. This results in significantly faster computations. Polynomial interpolation also forms the basis for algorithms in numerical quadrature and numerical ordinary differential equations.
Polynomial interpolation is also essential to perform sub-quadratic multiplication and squaring such as Karatsuba multiplication and Toom–Cook multiplication, where an interpolation through points on a polynomial which defines the product yields the product itself. For example, given ''a'' = ''f''(''x'') = ''a''0''x''0 + ''a''1''x''1 + ... and ''b'' = ''g''(''x'') = ''b''0''x''0 + ''b''1''x''1 + ..., the product ''ab'' is equivalent to ''W''(''x'') = ''f''(''x'')''g''(''x''). Finding points along ''W''(''x'') by substituting ''x'' for small values in ''f''(''x'') and ''g''(''x'') yields points on the curve. Interpolation based on those points will yield the terms of ''W''(''x'') and subsequently the product ''ab''. In the case of Karatsuba multiplication this technique is substantially faster than quadratic multiplication, even for modest-sized inputs. This is especially true when implemented in parallel hardware.
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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