interpolation

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interpolation

[in‚tər·pə′lā·shən]
(mathematics)
A process used to estimate an intermediate value of one (dependent) variable which is a function of a second (independent) variable when values of the dependent variable corresponding to several discrete values of the independent variable are known.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.

interpolation

see MEASURES OF CENTRAL TENDENCY.
Collins Dictionary of Sociology, 3rd ed. © HarperCollins Publishers 2000
The following article is from The Great Soviet Encyclopedia (1979). It might be outdated or ideologically biased.

Interpolation

 

an insertion or correction in an original text made by someone other than the author.

Interpolations played a pivotal role in the texts by Roman jurists that are compiled in the Digest. They were made to eliminate contradictions in the texts as well as the statutes and attitudes that were inappropriate for the Justinian era. Various kinds of interpolations were made, including specification and substitutions of the rule of law, substitution or elimination of terms, and lexical changes. The medieval humanists were the first to discover the interpolations in the Digest.


Interpolation

 

in mathematics and statistics, the process of finding values of a quantity between some of its known values. An example is finding values of the function f(x) at points x lying between the points (nodes of interpolation) x0 < x1 < … < xn by means of the known values yi = f(x1), where i = 0, 1, …, n. In the case when x lies outside the interval included between x0 and xn, the analogous problem is called an extrapolation problem.

In the simplest case, linear interpolation, the value of f(x) at a point x satisfying the inequality x0 < x1, is taken to be equal to the value

of the linear function coinciding with f(x) at the points x = x0 and x = x1. The interpolation problem is undefined from a strict mathematical viewpoint: if nothing is known about the function f(x) except its values at the points x0, x1, …, xn, then its value at a point x, which is different from all these points, remains completely arbitrary. The interpolation problem acquires a definite meaning if the function f(x) and its derivatives are subject to certain inequalities. If, for example, the values f(x0) and f(x1) are given and it is known that for xo < x < x1 the inequality | f”(x) | ≤ M is fulfilled, then the error of the formula (*) may be estimated with the aid of the inequality

It makes sense to use more complex interpolation formulas only in the case when it is certain that the function is sufficiently “smooth,” that is, when it has a sufficient number of derivatives that do not increase rapidly.

In addition to the computation of values of functions, interpolation has numerous other applications (for example, approximate integration, approximate solution of equations, and, in statistics, the smoothing of distribution series with the aim of eliminating random distortions).

REFERENCES

Goncharov, V. L. Teoriia interpolirovaniia i priblizheniia funktsii, 2nd ed. Moscow, 1954.
Krylov, A. N. Lektsii o pribiizhennykh vychisleniiakh, 6th ed. Moscow, 1954.
Yule, G. U., and M. G. Kendall. Teoriia statistiki, 14th ed. Moscow, 1960. (Translated from English.)
The Great Soviet Encyclopedia, 3rd Edition (1970-1979). © 2010 The Gale Group, Inc. All rights reserved.

interpolation

This article is provided by FOLDOC - Free Online Dictionary of Computing (foldoc.org)

interpolation

In computer graphics, interpolation is the creation of new values that lie between known values. For example, when objects are rasterized into two-dimensional images from their corner points (vertices), all the pixels between those points are filled in by an interpolation algorithm, which determines their color and other attributes (see graphics pipeline).

Another example is when a video image in a low resolution is upscaled to display on a monitor with a higher resolution, the missing lines are created by interpolation. In a digital camera, the optical zoom is based on the physical lenses, but the digital zoom is accomplished by algorithms (see interpolated resolution).
Copyright © 1981-2019 by The Computer Language Company Inc. All Rights reserved. THIS DEFINITION IS FOR PERSONAL USE ONLY. All other reproduction is strictly prohibited without permission from the publisher.
References in periodicals archive ?
The following utilities and parameters are used to perform comparison of the interpolation methods in this study.
When [[theta].sub.i+1] [less than or equal to] 2[pi], then continue interpolating; when [[theta].sub.i+1] > 2[pi], to amend the last step [DELTA][theta], there will be an interpolation stepping [DELTA][theta]'; then, [DELTA][theta]' = 2[pi] - [[theta].sub.i].
In this part, we just show the computation along x-direction by using the conservative interpolation. The computation along y-direction is similar.
Full Hermite interpolation problem defines a unique polynomial, called [p.sub.N](x), which solves the following problem.
Specifically, the finite-element methodology presented here is based upon a combination of two important techniques: (1) the three-dimensional extension of the Coons patch technique [39-41] and (2) the high-order Lagrange and Hermite interpolation schemes [42-44].
In the quantitative analysis, five rotation increments were considered with three distances (50 mm, 70 mm, and 100 mm), and only the nearest interpolation method was used.
An important advantage of hyperoperations in the Vandermonde method is that the rank r of the hyperoperation does not necessarily have to be r = 3/2, but a free parameter instead, in order to best fit the interpolation behaviour.
Function for MHR calculations is chosen individually at each interpolation and it represents probability distribution function of parameter [alpha] [member of] [0,1] for every point situated between two interpolation knots.
Weight interpolation for efficient data assimilation with the Local Ensemble Transform Kalman Filter.
IDW Interpolation. The IDW algorithm is one of the most commonly used spatial interpolation methods in Geosciences, which calculates the interpolated values of unknown points (prediction points) by weighting average of the values of known points (data points).
The main foci of this study are (i) the methodology development for phase estimation (ii) to improve average daily precipitation interpolation technique and (iii) to know the effect of the predictor variables on estimation of average daily precipitation (month wise, season wise and year wise) in tropical climate.
Basler's virtual pixel interpolation serves as a technological base for a one-to-one replacement of a CCD camera by a CMOS camera sensor.