View source for Order of approximation

{{Short description|Expressions for approximation accuracy}}
{{original research|date=November 2024}}
{{Order-of-approx}}
{{unclear|date=March 2016}}

In science, engineering, and other quantitative disciplines, '''order of approximation''' refers to formal or informal expressions for how accurate an approximation is in terms of the number of parameters used to construct the approximation. This article focuses on the approximation of smooth real-valued functions of one variable - the notion extends to functions between other spaces, such as between Euclidean spaces of varying dimension.

==Usage in science and engineering==
In formal expressions, the [[English_numerals#Ordinal_numbers|ordinal number]] used before the word [[Order_(mathematics)#Analysis|order]] refers to the highest [[Power function|power]] in the [[series expansion]] used in the [[Approximation#Etymology_and_usage|approximation]].  The expressions: a '''''zeroth-order''' approximation'', a '''''first-order''' approximation'', a '''''second-order''' approximation'', and so forth are used as [[fixed phrase]]s. The expression a ''zero-order approximation'' is also common. [[Cardinal numeral]]s are occasionally used in expressions like an ''order-zero approximation'', an ''order-one approximation'', etc.

In the case of a [[smooth function]], the ''n''th-order approximation is a [[polynomial]] of [[degree of a polynomial|degree]] ''n'', which is obtained by truncating the Taylor series to this degree. The formal usage of ''order of approximation'' corresponds to the omission of some terms of the [[Series_(mathematics)|series]] used in the [[Series_expansion|expansion]]. This affects [[Accuracy_and_precision|accuracy]]. The error usually varies within the interval. Thus the terms (''zeroth'', ''first'', ''second,'' etc.) used above meaning do not directly give information about [[percent error]] or [[significant figures]]. For example, in the [[w:Taylor's theorem|Taylor series]] expansion of the [[Exponential_function#Formal_definition|exponential function]],
<math display="block">e^x=\underbrace{1}_{0^\text{th}}+\underbrace{x}_{1^\text{st}}+\underbrace{\frac{x^2}{2!}}_{2^\text{nd}}+\underbrace{\frac{x^3}{3!}}_{3^\text{rd}} + \underbrace{\frac{x^4}{4!}}_{4^\text{th}} + \ldots\;, </math>
the zeroth-order term is <math>1;</math> the first-order term is <math>x,</math> second-order is <math>x^2/2,</math> and so forth. If <math>|x|<1,</math> each higher order term is smaller than the previous.  If <math>|x|<<1,\,</math> then the first order approximation,
<math display="block">e^x\approx 1+x, </math>
is often sufficient.  But at <math>x=1,</math> the first-order term, <math>x,</math> is not smaller than the zeroth-order term, <math>1.</math>  And at <math>x=2,</math> even the second-order term, <math>2^3/3!=4/3,\,</math> is greater than the zeroth-order term.  

=== Zeroth-order ===
A zeroth-order approximation of a [[function (mathematics)|function]] (that is, [[mathematics|mathematically]] determining a [[formula]] to fit multiple [[data point]]s) will be [[Constant (mathematics)|constant]], or a flat [[line (mathematics)|line]] with no [[slope]]: a polynomial of degree 0. For example,

: <math>x = [0, 1, 2],</math>
: <math>y = [3, 3, 5],</math>
: <math>y \sim f(x) = 3.67</math>

could be – if data point accuracy were reported – an approximate fit to the data, obtained by simply averaging the ''x'' values and the ''y'' values. However, data points represent [[Unit_of_observation#Data_point|results of measurements]]  and they do differ from [[Point_(geometry)#Points_in_Euclidean_geometry|points in Euclidean geometry]]. Thus quoting an average value containing three significant digits in the output with just one significant digit in the input data could be recognized as an example of [[false precision]]. With the implied accuracy of the data points of ±0.5, the zeroth order approximation could at best yield the result for ''y'' of ~3.7&nbsp;±&nbsp;2.0 in the interval of ''x'' from −0.5 to 2.5, considering the [[standard deviation]]. 

If the data points are reported as 
: <math>x = [0.00, 1.00, 2.00],</math>
: <math>y = [3.00, 3.00, 5.00],</math>
the zeroth-order approximation results in
: <math>y \sim f(x) = 3.67.</math>
The accuracy of the result justifies an attempt to derive a multiplicative function for that average, for example,

: <math>y \sim x + 2.67.</math>

One should be careful though, because the multiplicative function will be defined for the whole interval. If only three data points are available, one has no knowledge about the rest of the [[Interval (mathematics)|interval]], which may be a large part of it. This means that ''y'' could have another component which equals 0 at the ends and in the middle of the interval. A number of functions having this property are known, for example ''y'' = sin π''x''. [[Taylor series]] are useful and help predict [[Closed-form expression|analytic solutions]], but the approximations alone do not provide conclusive evidence.

===First-order===
A first-order approximation of a function (that is, mathematically determining a formula to fit multiple data points) will be a linear approximation, straight line with a slope: a polynomial of degree&nbsp;1. For example:

: <math>x = [0.00, 1.00, 2.00],</math>
: <math>y = [3.00, 3.00, 5.00],</math>
: <math>y \sim f(x) = x + 2.67</math>

is an approximate fit to the data.
In this example there is a zeroth-order approximation that is the same as the first-order, but the method of getting there is different; i.e. a wild stab in the dark at a relationship happened to be as good as an "educated guess".

===Second-order===
A second-order approximation of a function (that is, mathematically determining a formula to fit multiple data points) will be a [[quadratic polynomial]], geometrically, a [[parabola]]: a polynomial of degree&nbsp;2. For example:

: <math>x = [0.00, 1.00, 2.00],</math>
: <math>y = [3.00, 3.00, 5.00],</math>
: <math>y \sim f(x) = x^2 - x + 3</math>

is an approximate fit to the data. In this case, with only three data points, a parabola is an exact fit based on the data provided. However, the data points for most of the interval are not available, which advises caution (see "zeroth order").

===Higher-order===
While higher-order approximations exist and are crucial to a better understanding and description of reality, they are not typically referred to by number.

Continuing the above, a third-order approximation would be required to perfectly fit four data points, and so on. See [[polynomial interpolation]].

==Colloquial usage==
These terms are also used [[Colloquialism|colloquially]] by scientists and engineers to describe phenomena that can be neglected as not significant (e.g. "Of course the rotation of the Earth affects our experiment, but it's such a high-order effect that we wouldn't be able to measure it." or "At these velocities, relativity is a fourth-order effect that we only worry about at the annual calibration.") In this usage, the ordinality of the approximation is not exact, but is used to emphasize its insignificance; the higher the number used, the less important the effect. The terminology, in this context, represents a high level of precision required to account for an effect which is inferred to be very small when compared to the overall subject matter. The higher the order, the more precision is required to measure the effect, and therefore the smallness of the effect in comparison to the overall measurement.

== See also ==
* [[Linearization]]
* [[Perturbation theory]]
* [[Taylor series]]
* [[Chapman–Enskog_theory#Mathematical_Formulation | Chapman–Enskog method]]
* [[Big O notation]]
* [[Order of accuracy]]

==References==
{{reflist}}

{{Calculus topics}}

[[Category:Perturbation theory]]
[[Category:Numerical analysis]]