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In the mathematical field of numerical analysis, **interpolation** is a type of estimation, a method of constructing new data points within the range of a discrete set of known data points.

**Mathematics** includes the study of such topics as quantity, structure (algebra), space (geometry), and change. It has no generally accepted definition.

**Numerical analysis** is the study of algorithms that use numerical approximation for the problems of mathematical analysis. Numerical analysis naturally finds application in all fields of engineering and the physical sciences, but in the 21st century also the life sciences, social sciences, medicine, business and even the arts have adopted elements of scientific computations. The growth in computing power has revolutionized the use of realistic mathematical models in science and engineering, and subtle numerical analysis is required to implement these detailed models of the world. For example, ordinary differential equations appear in celestial mechanics ; numerical linear algebra is important for data analysis; stochastic differential equations and Markov chains are essential in simulating living cells for medicine and biology.

**Estimation** is the process of finding an **estimate**, or approximation, which is a value that is usable for some purpose even if input data may be incomplete, uncertain, or unstable. The value is nonetheless usable because it is derived from the best information available. Typically, estimation involves "using the value of a statistic derived from a sample to estimate the value of a corresponding population parameter". The sample provides information that can be projected, through various formal or informal processes, to determine a range most likely to describe the missing information. An estimate that turns out to be incorrect will be an **overestimate** if the estimate exceeded the actual result, and an **underestimate** if the estimate fell short of the actual result.

- Example
- Piecewise constant interpolation
- Linear interpolation
- Polynomial interpolation
- Spline interpolation
- Function approximation
- Via Gaussian processes
- Other forms
- In higher dimensions
- In digital signal processing
- Related concepts
- Generalization
- See also
- References
- External links

In engineering and science, one often has a number of data points, obtained by sampling or experimentation, which represent the values of a function for a limited number of values of the independent variable. It is often required to **interpolate**, i.e., estimate the value of that function for an intermediate value of the independent variable.

**Engineering** is the use of scientific principles to design and build machines, structures, and other items, including bridges, tunnels, roads, vehicles, and buildings. The discipline of engineering encompasses a broad range of more specialized fields of engineering, each with a more specific emphasis on particular areas of applied mathematics, applied science, and types of application. See glossary of engineering.

**Science** is a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe.

In statistics, quality assurance, and survey methodology, **sampling** is the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question. Two advantages of sampling are lower cost and faster data collection than measuring the entire population.

A closely related problem is the approximation of a complicated function by a simple function. Suppose the formula for some given function is known, but too complicated to evaluate efficiently. A few data points from the original function can be interpolated to produce a simpler function which is still fairly close to the original. The resulting gain in simplicity may outweigh the loss from interpolation error.

In general, a function approximation problem asks us to select a function among a well-defined class that closely matches ("approximates") a **target function** in a task-specific way. The need for **function approximations** arises in many branches of applied mathematics, and computer science in particular.

This table gives some values of an unknown function .

x | f(x) | ||||
---|---|---|---|---|---|

0 | 0 | ||||

1 | 0 | . | 8415 | ||

2 | 0 | . | 9093 | ||

3 | 0 | . | 1411 | ||

4 | −0 | . | 7568 | ||

5 | −0 | . | 9589 | ||

6 | −0 | . | 2794 |

Interpolation provides a means of estimating the function at intermediate points, such as .

We describe some methods of interpolation, differing in such properties as: accuracy, cost, number of data points needed, and smoothness of the resulting interpolant function.

In mathematics and computer science, an **algorithm** is a sequence of instructions, typically to solve a class of problems or perform a computation. Algorithms are unambiguous specifications for performing calculation, data processing, automated reasoning, and other tasks.

The simplest interpolation method is to locate the nearest data value, and assign the same value. In simple problems, this method is unlikely to be used, as linear interpolation (see below) is almost as easy, but in higher-dimensional multivariate interpolation, this could be a favourable choice for its speed and simplicity.

In numerical analysis, **multivariate interpolation** or **spatial interpolation** is interpolation on functions of more than one variable.

One of the simplest methods is linear interpolation (sometimes known as lerp). Consider the above example of estimating *f*(2.5). Since 2.5 is midway between 2 and 3, it is reasonable to take *f*(2.5) midway between *f*(2) = 0.9093 and *f*(3) = 0.1411, which yields 0.5252.

Generally, linear interpolation takes two data points, say (*x*_{a},*y*_{a}) and (*x*_{b},*y*_{b}), and the interpolant is given by:

This previous equation states that the slope of the new line between and is the same as the slope of the line between and

Linear interpolation is quick and easy, but it is not very precise. Another disadvantage is that the interpolant is not differentiable at the point *x*_{k}.

The following error estimate shows that linear interpolation is not very precise. Denote the function which we want to interpolate by *g*, and suppose that *x* lies between *x*_{a} and *x*_{b} and that *g* is twice continuously differentiable. Then the linear interpolation error is

In words, the error is proportional to the square of the distance between the data points. The error in some other methods, including polynomial interpolation and spline interpolation (described below), is proportional to higher powers of the distance between the data points. These methods also produce smoother interpolants.

Polynomial interpolation is a generalization of linear interpolation. Note that the linear interpolant is a linear function. We now replace this interpolant with a polynomial of higher degree.

Consider again the problem given above. The following sixth degree polynomial goes through all the seven points:

Substituting *x* = 2.5, we find that *f*(2.5) = 0.5965.

Generally, if we have *n* data points, there is exactly one polynomial of degree at most *n*−1 going through all the data points. The interpolation error is proportional to the distance between the data points to the power *n*. Furthermore, the interpolant is a polynomial and thus infinitely differentiable. So, we see that polynomial interpolation overcomes most of the problems of linear interpolation.

However, polynomial interpolation also has some disadvantages. Calculating the interpolating polynomial is computationally expensive (see computational complexity) compared to linear interpolation. Furthermore, polynomial interpolation may exhibit oscillatory artifacts, especially at the end points (see Runge's phenomenon).

Polynomial interpolation can estimate local maxima and minima that are outside the range of the samples, unlike linear interpolation. For example, the interpolant above has a local maximum at *x* ≈ 1.566, *f*(*x*) ≈ 1.003 and a local minimum at *x* ≈ 4.708, *f*(*x*) ≈ −1.003. However, these maxima and minima may exceed the theoretical range of the function—for example, a function that is always positive may have an interpolant with negative values, and whose inverse therefore contains false vertical asymptotes.

More generally, the shape of the resulting curve, especially for very high or low values of the independent variable, may be contrary to commonsense, i.e. to what is known about the experimental system which has generated the data points. These disadvantages can be reduced by using spline interpolation or restricting attention to Chebyshev polynomials.

Remember that linear interpolation uses a linear function for each of intervals [*x*_{k},*x*_{k+1}]. Spline interpolation uses low-degree polynomials in each of the intervals, and chooses the polynomial pieces such that they fit smoothly together. The resulting function is called a spline.

For instance, the natural cubic spline is piecewise cubic and twice continuously differentiable. Furthermore, its second derivative is zero at the end points. The natural cubic spline interpolating the points in the table above is given by

In this case we get *f*(2.5) = 0.5972.

Like polynomial interpolation, spline interpolation incurs a smaller error than linear interpolation and the interpolant is smoother. However, the interpolant is easier to evaluate than the high-degree polynomials used in polynomial interpolation. However, the global nature of the basis functions leads to ill-conditioning. This is completely mitigated by using splines of compact support, such as are implemented in Boost.Math and discussed in Kress.^{ [1] }

Interpolation is a common way to approximate functions. Given a function with a set of points one can form a function such that for (that is that interpolates at these points). In general, an interpolant need not be a good approximation, but there are well known and often reasonable conditions where it will. For example, if (four times continuously differentiable) then cubic spline interpolation has an error bound given by where and is a constant.^{ [2] }

Gaussian process is a powerful non-linear interpolation tool. Many popular interpolation tools are actually equivalent to particular Gaussian processes. Gaussian processes can be used not only for fitting an interpolant that passes exactly through the given data points but also for regression, i.e., for fitting a curve through noisy data. In the geostatistics community Gaussian process regression is also known as Kriging.

Other forms of interpolation can be constructed by picking a different class of interpolants. For instance, rational interpolation is **interpolation** by rational functions using Padé approximant, and trigonometric interpolation is interpolation by trigonometric polynomials using Fourier series. Another possibility is to use wavelets.

The Whittaker–Shannon interpolation formula can be used if the number of data points is infinite.

Sometimes, we know not only the value of the function that we want to interpolate, at some points, but also its derivative. This leads to Hermite interpolation problems.

When each data point is itself a function, it can be useful to see the interpolation problem as a partial advection problem between each data point. This idea leads to the displacement interpolation problem used in transportation theory.

Multivariate interpolation is the interpolation of functions of more than one variable. Methods include bilinear interpolation and bicubic interpolation in two dimensions, and trilinear interpolation in three dimensions. They can be applied to gridded or scattered data.

- Nearest neighbor
- Bilinear
- Bicubic

In the domain of digital signal processing, the term interpolation refers to the process of converting a sampled digital signal (such as a sampled audio signal) to that of a higher sampling rate (Upsampling) using various digital filtering techniques (e.g., convolution with a frequency-limited impulse signal). In this application there is a specific requirement that the harmonic content of the original signal be preserved without creating aliased harmonic content of the original signal above the original Nyquist limit of the signal (i.e., above fs/2 of the original signal sample rate). An early and fairly elementary discussion on this subject can be found in Rabiner and Crochiere's book *Multirate Digital Signal Processing*.^{ [3] }

The term * extrapolation * is used to find data points outside the range of known data points.

In curve fitting problems, the constraint that the interpolant has to go exactly through the data points is relaxed. It is only required to approach the data points as closely as possible (within some other constraints). This requires parameterizing the potential interpolants and having some way of measuring the error. In the simplest case this leads to least squares approximation.

Approximation theory studies how to find the best approximation to a given function by another function from some predetermined class, and how good this approximation is. This clearly yields a bound on how well the interpolant can approximate the unknown function.

If we consider as a variable in a topological space, with and the function mapping to a Banach space, then the problem is treated as "interpolation of operators".^{ [4] } The classical results about interpolation of operators are the Riesz–Thorin theorem and the Marcinkiewicz theorem. There are also many other subsequent results.

- Barycentric coordinates – for interpolating within on a triangle or tetrahedron
- Bilinear interpolation
- Brahmagupta's interpolation formula
- Extrapolation
- Fractal interpolation
- Imputation (statistics)
- Lagrange interpolation
- Missing data
- Multivariate interpolation
- Newton–Cotes formulas
- Polynomial interpolation
- Radial basis function interpolation
- Simple rational approximation

In the mathematical subfield of numerical analysis, a **B-spline**, or **basis spline**, is a spline function that has minimal support with respect to a given degree, smoothness, and domain partition. Any spline function of given degree can be expressed as a linear combination of B-splines of that degree. Cardinal B-splines have knots that are equidistant from each other. B-splines can be used for curve-fitting and numerical differentiation of experimental data.

In mathematics, **linear interpolation** is a method of curve fitting using linear polynomials to construct new data points within the range of a discrete set of known data points.

In the mathematical field of numerical analysis, **Runge's phenomenon** is a problem of oscillation at the edges of an interval that occurs when using polynomial interpolation with polynomials of high degree over a set of equispaced interpolation points. It was discovered by Carl David Tolmé Runge (1901) when exploring the behavior of errors when using polynomial interpolation to approximate certain functions. The discovery was important because it shows that going to higher degrees does not always improve accuracy. The phenomenon is similar to the Gibbs phenomenon in Fourier series approximations.

In numerical analysis, **polynomial interpolation** is the interpolation of a given data set by the polynomial of lowest possible degree that passes through the points of the dataset.

In the mathematical field of numerical analysis, a **Newton polynomial**, named after its inventor Isaac Newton, is an interpolation polynomial for a given set of data points. The Newton polynomial is sometimes called **Newton's divided differences interpolation polynomial** because the coefficients of the polynomial are calculated using Newton's divided differences method.

In mathematics, a **cubic function** is a function of the form

In mathematics, a **spline** is a special function defined piecewise by polynomials. In interpolating problems, spline interpolation is often preferred to polynomial interpolation because it yields similar results, even when using low degree polynomials, while avoiding Runge's phenomenon for higher degrees.

In the mathematical field of numerical analysis, **spline interpolation** is a form of interpolation where the interpolant is a special type of piecewise polynomial called a spline. Spline interpolation is often preferred over polynomial interpolation because the interpolation error can be made small even when using low degree polynomials for the spline. Spline interpolation avoids the problem of Runge's phenomenon, in which oscillation can occur between points when interpolating using high degree polynomials.

In numerical analysis, a **cubic Hermite spline** or **cubic Hermite interpolator** is a spline where each piece is a third-degree polynomial specified in Hermite form: i.e., by its values and first derivatives at the end points of the corresponding domain interval.

In mathematics, **bilinear interpolation** is an extension of linear interpolation for interpolating functions of two variables on a rectilinear 2D grid. Bilinear interpolation is performed using linear interpolation first in one direction, and then again in the other direction. Although each step is linear in the sampled values and in the position, the interpolation as a whole is not linear but rather quadratic in the sample location.

In mathematics, the **Lebesgue constants** give an idea of how good the interpolant of a function is in comparison with the best polynomial approximation of the function. The Lebesgue constant for polynomials of degree at most n and for the set of *n* + 1 nodes T is generally denoted by Λ_{n}(*T* ). These constants are named after Henri Lebesgue.

The **Remez algorithm** or **Remez exchange algorithm**, published by Evgeny Yakovlevich Remez in 1934, is an iterative algorithm used to find simple approximations to functions, specifically, approximations by functions in a Chebyshev space that are the best in the uniform norm *L*_{∞} sense.

In the mathematical field of numerical analysis, **monotone cubic interpolation** is a variant of cubic interpolation that preserves monotonicity of the data set being interpolated.

In the mathematical subfield numerical analysis, **tricubic interpolation** is a method for obtaining values at arbitrary points in 3D space of a function defined on a regular grid. The approach involves approximating the function locally by an expression of the form

**Smoothing splines** are function estimates, , obtained from a set of noisy observations of the target , in order to balance a measure of goodness of fit of to with a derivative based measure of the smoothness of . They provide a means for smoothing noisy data. The most familiar example is the cubic smoothing spline, but there are many other possibilities, including for the case where is a vector quantity.

In statistics, **polynomial regression** is a form of regression analysis in which the relationship between the independent variable *x* and the dependent variable *y* is modelled as an *n*th degree polynomial in *x*. Polynomial regression fits a nonlinear relationship between the value of *x* and the corresponding conditional mean of *y*, denoted E(*y* |*x*). Although *polynomial regression* fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(*y* | *x*) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression.

In the mathematical field of numerical analysis, **discrete spline interpolation** is a form of interpolation where the interpolant is a special type of piecewise polynomial called a discrete spline. A discrete spline is a piecewise polynomial such that its central differences are continuous at the knots whereas a spline is a piecewise polynomial such that its derivatives are continuous at the knots. Discrete cubic splines are discrete splines where the central differences of orders 0, 1, and 2 are required to be continuous.

**Radial basis function (RBF) interpolation** is an advanced method in approximation theory for constructing high-order accurate interpolants of unstructured data, possibly in high-dimensional spaces. The interpolant takes the form of a weighted sum of radial basis functions. RBF interpolation is a mesh-free method, meaning the nodes need not lie on a structured grid, and does not require the formation of a mesh. It is often spectrally accurate and stable for large numbers of nodes even in high dimensions.

- ↑ Kress, Rainer (1998).
*Numerical Analysis*. - ↑ Hall, Charles A.; Meyer, Weston W. (1976). "Optimal Error Bounds for Cubic Spline Interpolation".
*Journal of Approximation Theory*.**16**(2): 105–122. - ↑ R.E. Crochiere and L.R. Rabiner. (1983). Multirate Digital Signal Processing. Englewood Cliffs, NJ: Prentice–Hall.
- ↑ Colin Bennett, Robert C. Sharpley,
*Interpolation of Operators*, Academic Press 1988

- Online tools for linear, quadratic, cubic spline, and polynomial interpolation with visualisation and JavaScript source code.
- Sol Tutorials - Interpolation Tricks
- Compactly Supported Cubic B-Spline interpolation in Boost.Math
- Barycentric rational interpolation in Boost.Math
- Interpolation via the Chebyshev transform in Boost.Math

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