Approximation

An approximation is anything that is intentionally similar but not exactly equal to something else.

Etymology and usage

The word approximation is derived from Latin approximatus, from proximus meaning very near and the prefix ap- (ad- before p) meaning to.[1] Words like approximate, approximately and approximation are used especially in technical or scientific contexts. In everyday English, words such as roughly or around are used with a similar meaning.[2] It is often found abbreviated as approx.

The term can be applied to various properties (e.g., value, quantity, image, description) that are nearly, but not exactly correct; similar, but not exactly the same (e.g., the approximate time was 10 o'clock).

Although approximation is most often applied to numbers, it is also frequently applied to such things as mathematical functions, shapes, and physical laws.

In science, approximation can refer to using a simpler process or model when the correct model is difficult to use. An approximate model is used to make calculations easier. Approximations might also be used if incomplete information prevents use of exact representations.

The type of approximation used depends on the available information, the degree of accuracy required, the sensitivity of the problem to this data, and the savings (usually in time and effort) that can be achieved by approximation.

Mathematics

Approximation theory is a branch of mathematics, a quantitative part of functional analysis. Diophantine approximation deals with approximations of real numbers by rational numbers. Approximation usually occurs when an exact form or an exact numerical number is unknown or difficult to obtain. However some known form may exist and may be able to represent the real form so that no significant deviation can be found. It also is used when a number is not rational, such as the number π, which often is shortened to 3.14159, or √2 to 1.414.

Numerical approximations sometimes result from using a small number of significant digits. Calculations are likely to involve rounding errors leading to approximation. Log tables, slide rules and calculators produce approximate answers to all but the simplest calculations. The results of computer calculations are normally an approximation expressed in a limited number of significant digits, although they can be programmed to produce more precise results.[3] Approximation can occur when a decimal number cannot be expressed in a finite number of binary digits.

Related to approximation of functions is the asymptotic value of a function, i.e. the value as one or more of a function's parameters becomes arbitrarily large. For example, the sum (k/2)+(k/4)+(k/8)+...(k/2^n) is asymptotically equal to k. Unfortunately no consistent notation is used throughout mathematics and some texts will use ≈ to mean approximately equal and ~ to mean asymptotically equal whereas other texts use the symbols the other way around.

As another example, in order to accelerate the convergence rate of evolutionary algorithms, fitness approximation—that leads to build model of the fitness function to choose smart search steps—is a good solution.

Science

Approximation arises naturally in scientific experiments. The predictions of a scientific theory can differ from actual measurements. This can be because there are factors in the real situation that are not included in the theory. For example, simple calculations may not include the effect of air resistance. Under these circumstances, the theory is an approximation to reality. Differences may also arise because of limitations in the measuring technique. In this case, the measurement is an approximation to the actual value.

The history of science shows that earlier theories and laws can be approximations to some deeper set of laws. Under the correspondence principle, a new scientific theory should reproduce the results of older, well-established, theories in those domains where the old theories work.[4] The old theory becomes an approximation to the new theory.

Some problems in physics are too complex to solve by direct analysis, or progress could be limited by available analytical tools. Thus, even when the exact representation is known, an approximation may yield a sufficiently accurate solution while reducing the complexity of the problem significantly. Physicists often approximate the shape of the Earth as a sphere even though more accurate representations are possible, because many physical characteristics (e.g., gravity) are much easier to calculate for a sphere than for other shapes.

Approximation is also used to analyze the motion of several planets orbiting a star. This is extremely difficult due to the complex interactions of the planets' gravitational effects on each other.[5] An approximate solution is effected by performing iterations. In the first iteration, the planets' gravitational interactions are ignored, and the star is assumed to be fixed. If a more precise solution is desired, another iteration is then performed, using the positions and motions of the planets as identified in the first iteration, but adding a first-order gravity interaction from each planet on the others. This process may be repeated until a satisfactorily precise solution is obtained.

The use of perturbations to correct for the errors can yield more accurate solutions. Simulations of the motions of the planets and the star also yields more accurate solutions.

The most common versions of philosophy of science accept that empirical measurements are always approximations—they do not perfectly represent what is being measured.

The error-tolerance property of several applications (e.g., graphics applications) allows use of approximation (e.g., lowering the precision of numerical computations) to improve performance and energy efficiency.[6] This approach of using deliberate, controlled approximation for achieving various optimizations is referred to as approximate computing.

Unicode

Symbols used to denote items that are approximately equal are wavy or dotted equals signs.[7]

• ≈ (U+2248, almost equal to)
• ≉ (U+2249, not almost equal to)
• ≃ (U+2243), a combination of "≈" and "=", also used to indicate asymptotically equal to
  • ≒ (U+2252), which is used like "≃" in Japan, Taiwan, and Korea
  • ≓ (U+2253), a reversed variation of "≒"
• ≅ (U+2245), another combination of "≈" and "=", which is used to indicate isomorphism or congruence
• ≊ (U+224A), yet another combination of "≈" and "=", used to indicate equivalence or approximate equivalence
• ∼ (U+223C), which is also sometimes used to indicate proportionality
• ∽ (U+223D), which is also sometimes used to indicate proportionality
• ≐ (U+2250, approaches the limit), which can be used to represent the approach of a variable, y, to a limit; like the common syntax, ${\displaystyle \scriptstyle \lim _{x\to \infty }y(x)}$ ≐ 0 [8]

LaTeX symbols

Symbols used in LaTeX markup.

• ${\displaystyle \approx }$ (\approx), usually to indicate approximation between numbers, like ${\displaystyle \pi \approx 3.14}$.
• ${\displaystyle \not \approx }$ (\not\approx), usually to indicate that numbers are not approximately equal (1 ${\displaystyle \not \approx }$ 2).
• ${\displaystyle \simeq }$ (\simeq), usually to indicate asymptotic equivalence between functions, like ${\displaystyle f(n)\simeq 3n^{2}}$. So writing ${\displaystyle \pi \simeq 3.14}$ would be wrong, despite wide use.
• ${\displaystyle \sim }$ (\sim), usually to indicate proportionality between functions, the same ${\displaystyle f(n)}$ of the line above will be ${\displaystyle f(n)\sim n^{2}}$.
• ${\displaystyle \cong }$ (\cong), usually to indicate congruence between figures, like ${\displaystyle \Delta ABC\cong \Delta A'B'C'}$.

See also

• Approximately equals sign
• Approximation error
• Congruence relation
• Estimation
• Fermi estimate
• Fitness approximation
• Least squares
• Linear approximation
• Binomial approximation
• Newton's method
• Numerical analysis
• Orders of approximation
• Runge–Kutta methods
• Successive approximation ADC
• Taylor series
• Small-angle approximation
• Approximate computing
• Tolerance relation
• Rough set

References

1. The Concise Oxford Dictionary, Eighth edition 1990, ISBN 0-19-861243-5
2. Longman Dictionary of Contemporary English, Pearson Education Ltd 2009, ISBN 978 1 4082 1532 6
3. "Numerical Computation Guide". Archived from the original on 2016-04-06. Retrieved 2013-06-16.
4. Encyclopædia Britannica
5. The three body problem
6. Mittal, Sparsh (May 2016). "A Survey of Techniques for Approximate Computing". ACM Comput. Surv. ACM. 48 (4): 62:1–62:33. doi:10.1145/2893356. S2CID 4668902.
7. "Mathematical Operators – Unicode" (PDF). Retrieved 2013-04-20.
8. D & D Standard Oil & Gas Abbreviator. PennWell. 2006. p. 366. Retrieved May 21, 2020. ≐ approaches a limit

External links

• Media related to Approximation at Wikimedia Commons