Logarithm
In mathematics, the logarithm is the inverse function to exponentiation. That means the logarithm of a given number x is the exponent to which another fixed number, the base b, must be raised, to produce that number x. In the simplest case, the logarithm counts the number of occurrences of the same factor in repeated multiplication; e.g., since 1000 = 10 × 10 × 10 = 10^{3}, the "logarithm base 10" of 1000 is 3. The logarithm of x to base b is denoted as log_{b} (x), or without parentheses, log_{b} x, or even without the explicit base, log x—if no confusion is possible.
Arithmetic operations  

More generally, exponentiation allows any positive real number as base to be raised to any real power, always producing a positive result, so log_{b} (x) for any two positive real numbers b and x, where b is not equal to 1, is always a unique real number y. More explicitly, the defining relation between exponentiation and logarithm is:
 exactly if
For example, log_{2} 64 = 6, as 2^{6} = 64.
The logarithm base 10 (that is b = 10) is called the common logarithm and has many applications in science and engineering. The natural logarithm has the number e (that is b ≈ 2.718) as its base; its use is widespread in mathematics and physics, because of its simpler integral and derivative. The binary logarithm uses base 2 (that is b = 2) and is commonly used in computer science. Logarithms are examples of concave functions.[1]
Logarithms were introduced by John Napier in 1614 as a means to simplify calculations.[2] They were rapidly adopted by navigators, scientists, engineers, surveyors and others to perform highaccuracy computations more easily. Using logarithm tables, tedious multidigit multiplication steps can be replaced by table lookups and simpler addition. This is possible because of the fact—important in its own right—that the logarithm of a product is the sum of the logarithms of the factors:
provided that b, x and y are all positive and b ≠ 1. The slide rule, also based on logarithms, allows quick calculations without tables, but at lower precision. The presentday notion of logarithms comes from Leonhard Euler, who connected them to the exponential function in the 18th century, and who also introduced the letter e as the base of natural logarithms.[3]
Logarithmic scales reduce wideranging quantities to tiny scopes. For example, the decibel (dB) is a unit used to express ratio as logarithms, mostly for signal power and amplitude (of which sound pressure is a common example). In chemistry, pH is a logarithmic measure for the acidity of an aqueous solution. Logarithms are commonplace in scientific formulae, and in measurements of the complexity of algorithms and of geometric objects called fractals. They help describing frequency ratios of musical intervals, appear in formulas counting prime numbers or approximating factorials, inform some models in psychophysics, and can aid in forensic accounting.
In the same way as the logarithm reverses exponentiation, the complex logarithm is the inverse function of the exponential function applied to complex numbers. The modular discrete logarithm is another variant; it has uses in publickey cryptography.
Motivation and definition
Addition, multiplication, and exponentiation are three fundamental arithmetic operations (or in some context the first three hyperoperations). Addition, the simplest of these, can be undone by subtraction: for example, the addition of 2 in 3 + 2 = 5 can be undone by subtracting 2: 5 − 2 = 3. Multiplication, the nextsimplest operation, can be undone by division: doubling a number x, i.e., multiplying x by 2, the result is 2x. To get back x, it is necessary to divide by 2. For example, 3 × 2 = 6 and the process of multiplying by 2 is undone by dividing by 2: 6 ÷ 2 = 3. The idea and purpose of logarithms is also to undo a fundamental arithmetic operation, namely raising a number to a certain power (an operation also known as exponentiation). For example, raising 2 to the power 3 yields 8, because 8 is the product of three factors of 2:
The logarithm (with respect to base 2) of 8 is 3, reflecting the fact that 2 was raised to the power 3 to get 8.
Exponentiation
This subsection contains a short overview of the exponentiation operation, which is fundamental to understanding logarithms. Raising b to the nth power, where n is a natural number, is done by multiplying n factors equal to b. The nth power of b is written b^{n}, so that
Exponentiation may be extended to b^{y}, where b is a positive number and the exponent y is any real number.[4] For example, b^{−1} is the reciprocal of b, that is, 1/b. Raising b to the power 1/2 is the square root of b.
More generally, raising b to a rational power p/q, where p and q are integers, is given by
the qth root of .
Finally, any irrational number (a real number which is not rational) y can be approximated to arbitrary precision by rational numbers. This can be used to compute the yth power of b: for example and is increasingly well approximated by . A more detailed explanation, as well as the formula b^{m + n} = b^{m} · b^{n} is contained in the article on exponentiation.
Definition
The logarithm of a positive real number x with respect to base b[nb 1] is the exponent by which b must be raised to yield x. In other words, the logarithm of x to base b is the solution y to the equation[5]
The logarithm is denoted "log_{b} x" (pronounced as "the logarithm of x to base b" or "the baseb logarithm of x" or (most commonly) "the log, base b, of x").
In the equation y = log_{b} x, the value y is the answer to the question "To what power must b be raised, in order to yield x?".
Examples
 log_{2} 16 = 4 , since 2^{4} = 2 ×2 × 2 × 2 = 16.
 Logarithms can also be negative: since
 log_{10}150 is approximately 2.176, which lies between 2 and 3, just as 150 lies between 10^{2} = 100 and 10^{3} = 1000.
 For any base b, log_{b} b = 1 and log_{b} 1 = 0, since b^{1} = b and b^{0} = 1, respectively.
Logarithmic identities
Several important formulas, sometimes called logarithmic identities or logarithmic laws, relate logarithms to one another.[6]
Product, quotient, power, and root
The logarithm of a product is the sum of the logarithms of the numbers being multiplied; the logarithm of the ratio of two numbers is the difference of the logarithms. The logarithm of the pth power of a number is p times the logarithm of the number itself; the logarithm of a pth root is the logarithm of the number divided by p. The following table lists these identities with examples. Each of the identities can be derived after substitution of the logarithm definitions or in the left hand sides.[1]
Formula  Example  

Product  
Quotient  
Power  
Root 
Change of base
The logarithm log_{b}x can be computed from the logarithms of x and b with respect to an arbitrary base k using the following formula:
Derivation of the conversion factor between logarithms of arbitrary base 

Starting from the defining identity we can apply log_{k} to both sides of this equation, to get
Solving for yields:
showing the conversion factor from given values to their corresponding values to be 
Typical scientific calculators calculate the logarithms to bases 10 and e.[7] Logarithms with respect to any base b can be determined using either of these two logarithms by the previous formula:
Given a number x and its logarithm log_{b}x to an unknown base b, the base is given by:
which can be seen from taking the defining equation to the power of
Particular bases
Among all choices for the base, three are particularly common. These are b = 10, b = e (the irrational mathematical constant ≈ 2.71828), and b = 2 (the binary logarithm). In mathematical analysis, the logarithm base e is widespread because of analytical properties explained below. On the other hand, base10 logarithms are easy to use for manual calculations in the decimal number system:[8]
Thus, log_{10}x is related to the number of decimal digits of a positive integer x: the number of digits is the smallest integer strictly bigger than log_{10}x.[9] For example, log_{10}1430 is approximately 3.15. The next integer is 4, which is the number of digits of 1430. Both the natural logarithm and the logarithm to base two are used in information theory, corresponding to the use of nats or bits as the fundamental units of information, respectively.[10] Binary logarithms are also used in computer science, where the binary system is ubiquitous, in music theory, where a pitch ratio of two (the octave) is ubiquitous and the cent is the binary logarithm (scaled by 1200) of the ratio between two adjacent equallytempered pitches in European classical music, and in photography to measure exposure values.[11]
The following table lists common notations for logarithms to these bases and the fields where they are used. Many disciplines write logx instead of log_{b}x, when the intended base can be determined from the context. The notation ^{b}logx also occurs.[12] The "ISO notation" column lists designations suggested by the International Organization for Standardization (ISO 3111).[13] Because the notation log x has been used for all three bases (or when the base is indeterminate or immaterial), the intended base must often be inferred based on context or discipline. In computer science and mathematics, log usually refers to log_{2} and log_{e}, respectively.[1][14] In other contexts log often means log_{10}.[15]
Base b  Name for log_{b}x  ISO notation  Other notations  Used in 

2  binary logarithm  lb x[16]  ld x, log x, lg x,[17] log_{2}x  computer science, information theory, music theory, photography 
e  natural logarithm  ln x[nb 2]  log x (in mathematics [1][21] and many programming languages[nb 3]) 
mathematics, physics, chemistry, statistics, economics, information theory, and engineering 
10  common logarithm  lg x  log x, log_{10}x (in engineering, biology, astronomy) 
various engineering fields (see decibel and see below), logarithm tables, handheld calculators, spectroscopy 
History
The history of logarithm in seventeenthcentury Europe is the discovery of a new function that extended the realm of analysis beyond the scope of algebraic methods. The method of logarithms was publicly propounded by John Napier in 1614, in a book titled Mirifici Logarithmorum Canonis Descriptio (Description of the Wonderful Rule of Logarithms).[22][23] Prior to Napier's invention, there had been other techniques of similar scopes, such as the prosthaphaeresis or the use of tables of progressions, extensively developed by Jost Bürgi around 1600.[24][25]
The common logarithm of a number is the index of that power of ten which equals the number.[26] Speaking of a number as requiring so many figures is a rough allusion to common logarithm, and was referred to by Archimedes as the "order of a number".[27] The first real logarithms were heuristic methods to turn multiplication into addition, thus facilitating rapid computation. Some of these methods used tables derived from trigonometric identities.[28] Such methods are called prosthaphaeresis.
Invention of the function now known as natural logarithm began as an attempt to perform a quadrature of a rectangular hyperbola by Grégoire de SaintVincent, a Belgian Jesuit residing in Prague. Archimedes had written The Quadrature of the Parabola in the third century BC, but a quadrature for the hyperbola eluded all efforts until SaintVincent published his results in 1647. The relation that the logarithm provides between a geometric progression in its argument and an arithmetic progression of values, prompted A. A. de Sarasa to make the connection of SaintVincent's quadrature and the tradition of logarithms in prosthaphaeresis, leading to the term "hyperbolic logarithm", a synonym for natural logarithm. Soon the new function was appreciated by Christiaan Huygens, and James Gregory. The notation Log y was adopted by Leibniz in 1675,[29] and the next year he connected it to the integral
Logarithm tables, slide rules, and historical applications
By simplifying difficult calculations before calculators and computers became available, logarithms contributed to the advance of science, especially astronomy. They were critical to advances in surveying, celestial navigation, and other domains. PierreSimon Laplace called logarithms
 "...[a]n admirable artifice which, by reducing to a few days the labour of many months, doubles the life of the astronomer, and spares him the errors and disgust inseparable from long calculations."[30]
As the function f(x) = b^{x} is the inverse function of log_{b}x, it has been called the antilogarithm.[31]
Log tables
A key tool that enabled the practical use of logarithms was the table of logarithms.[32] The first such table was compiled by Henry Briggs in 1617, immediately after Napier's invention but with the innovation of using 10 as the base. Briggs' first table contained the common logarithms of all integers in the range 1–1000, with a precision of 14 digits. Subsequently, tables with increasing scope were written. These tables listed the values of log_{10}x for any number x in a certain range, at a certain precision. Base10 logarithms were universally used for computation, hence the name common logarithm, since numbers that differ by factors of 10 have logarithms that differ by integers. The common logarithm of x can be separated into an integer part and a fractional part, known as the characteristic and mantissa. Tables of logarithms need only include the mantissa, as the characteristic can be easily determined by counting digits from the decimal point.[33] The characteristic of 10 · x is one plus the characteristic of x, and their mantissas are the same. Thus using a threedigit log table, the logarithm of 3542 is approximated by
Greater accuracy can be obtained by interpolation:
The value of 10^{x} can be determined by reverse look up in the same table, since the logarithm is a monotonic function.
Computations
The product and quotient of two positive numbers c and d were routinely calculated as the sum and difference of their logarithms. The product cd or quotient c/d came from looking up the antilogarithm of the sum or difference, via the same table:
and
For manual calculations that demand any appreciable precision, performing the lookups of the two logarithms, calculating their sum or difference, and looking up the antilogarithm is much faster than performing the multiplication by earlier methods such as prosthaphaeresis, which relies on trigonometric identities.
Calculations of powers and roots are reduced to multiplications or divisions and lookups by
and
Trigonometric calculations were facilitated by tables that contained the common logarithms of trigonometric functions.
Slide rules
Another critical application was the slide rule, a pair of logarithmically divided scales used for calculation. The nonsliding logarithmic scale, Gunter's rule, was invented shortly after Napier's invention. William Oughtred enhanced it to create the slide rule—a pair of logarithmic scales movable with respect to each other. Numbers are placed on sliding scales at distances proportional to the differences between their logarithms. Sliding the upper scale appropriately amounts to mechanically adding logarithms, as illustrated here:
For example, adding the distance from 1 to 2 on the lower scale to the distance from 1 to 3 on the upper scale yields a product of 6, which is read off at the lower part. The slide rule was an essential calculating tool for engineers and scientists until the 1970s, because it allows, at the expense of precision, much faster computation than techniques based on tables.[34]
Analytic properties
A deeper study of logarithms requires the concept of a function. A function is a rule that, given one number, produces another number.[35] An example is the function producing the xth power of b from any real number x, where the base b is a fixed number. This function is written:
Logarithmic function
To justify the definition of logarithms, it is necessary to show that the equation
has a solution x and that this solution is unique, provided that y is positive and that b is positive and unequal to 1. A proof of that fact requires the intermediate value theorem from elementary calculus.[36] This theorem states that a continuous function that produces two values m and n also produces any value that lies between m and n. A function is continuous if it does not "jump", that is, if its graph can be drawn without lifting the pen.
This property can be shown to hold for the function f(x) = b^{x}. Because f takes arbitrarily large and arbitrarily small positive values, any number y > 0 lies between f(x_{0}) and f(x_{1}) for suitable x_{0} and x_{1}. Hence, the intermediate value theorem ensures that the equation f(x) = y has a solution. Moreover, there is only one solution to this equation, because the function f is strictly increasing (for b > 1), or strictly decreasing (for 0 < b < 1).[37]
The unique solution x is the logarithm of y to base b, log_{b}y. The function that assigns to y its logarithm is called logarithm function or logarithmic function (or just logarithm).
The function log_{b}x is essentially characterized by the product formula
More precisely, the logarithm to any base b > 1 is the only increasing function f from the positive reals to the reals satisfying f(b) = 1 and [38]
Inverse function
The formula for the logarithm of a power says in particular that for any number x,
In prose, taking the xth power of b and then the baseb logarithm gives back x. Conversely, given a positive number y, the formula
says that first taking the logarithm and then exponentiating gives back y. Thus, the two possible ways of combining (or composing) logarithms and exponentiation give back the original number. Therefore, the logarithm to base b is the inverse function of f(x) = b^{x}.[39]
Inverse functions are closely related to the original functions. Their graphs correspond to each other upon exchanging the x and the ycoordinates (or upon reflection at the diagonal line x = y), as shown at the right: a point (t, u = b^{t}) on the graph of f yields a point (u, t = log_{b}u) on the graph of the logarithm and vice versa. As a consequence, log_{b}(x) diverges to infinity (gets bigger than any given number) if x grows to infinity, provided that b is greater than one. In that case, log_{b}(x) is an increasing function. For b < 1, log_{b}(x) tends to minus infinity instead. When x approaches zero, log_{b}x goes to minus infinity for b > 1 (plus infinity for b < 1, respectively).
Derivative and antiderivative
Analytic properties of functions pass to their inverses.[36] Thus, as f(x) = b^{x} is a continuous and differentiable function, so is log_{b}y. Roughly, a continuous function is differentiable if its graph has no sharp "corners". Moreover, as the derivative of f(x) evaluates to ln(b)b^{x} by the properties of the exponential function, the chain rule implies that the derivative of log_{b}x is given by[37][40]
That is, the slope of the tangent touching the graph of the baseb logarithm at the point (x, log_{b}(x)) equals 1/(x ln(b)).
The derivative of ln x is 1/x; this implies that ln x is the unique antiderivative of 1/x that has the value 0 for x =1. It is this very simple formula that motivated to qualify as "natural" the natural logarithm; this is also one of the main reasons of the importance of the constant e.
The derivative with a generalised functional argument f(x) is
The quotient at the right hand side is called the logarithmic derivative of f. Computing f'(x) by means of the derivative of ln(f(x)) is known as logarithmic differentiation.[41] The antiderivative of the natural logarithm ln(x) is:[42]
Related formulas, such as antiderivatives of logarithms to other bases can be derived from this equation using the change of bases.[43]
Integral representation of the natural logarithm
The natural logarithm of t equals the integral of 1/x dx from 1 to t:
In other words, ln(t) equals the area between the x axis and the graph of the function 1/x, ranging from x = 1 to x = t (figure at the right). This is a consequence of the fundamental theorem of calculus and the fact that the derivative of ln(x) is 1/x. The right hand side of this equation can serve as a definition of the natural logarithm. Product and power logarithm formulas can be derived from this definition.[44] For example, the product formula ln(tu) = ln(t) + ln(u) is deduced as:
The equality (1) splits the integral into two parts, while the equality (2) is a change of variable (w = x/t). In the illustration below, the splitting corresponds to dividing the area into the yellow and blue parts. Rescaling the left hand blue area vertically by the factor t and shrinking it by the same factor horizontally does not change its size. Moving it appropriately, the area fits the graph of the function f(x) = 1/x again. Therefore, the left hand blue area, which is the integral of f(x) from t to tu is the same as the integral from 1 to u. This justifies the equality (2) with a more geometric proof.
The power formula ln(t^{r}) = r ln(t) may be derived in a similar way:
The second equality uses a change of variables (integration by substitution), w = x^{1/r}.
The sum over the reciprocals of natural numbers,
is called the harmonic series. It is closely tied to the natural logarithm: as n tends to infinity, the difference,
converges (i.e., gets arbitrarily close) to a number known as the Euler–Mascheroni constant γ = 0.5772.... This relation aids in analyzing the performance of algorithms such as quicksort.[45]
There are also some other integral representations of the logarithm that are useful in some situations:
The first identity can be verified by showing that it has the same value at x = 1, and the same derivative. The second identity can be proven by writing
and then inserting the Laplace transform of cos(xt) (and cos(t)).
Transcendence of the logarithm
Real numbers that are not algebraic are called transcendental;[46] for example, π and e are such numbers, but is not. Almost all real numbers are transcendental. The logarithm is an example of a transcendental function. The Gelfond–Schneider theorem asserts that logarithms usually take transcendental, i.e., "difficult" values.[47]
Calculation
Logarithms are easy to compute in some cases, such as log_{10}(1000) = 3. In general, logarithms can be calculated using power series or the arithmetic–geometric mean, or be retrieved from a precalculated logarithm table that provides a fixed precision.[48][49] Newton's method, an iterative method to solve equations approximately, can also be used to calculate the logarithm, because its inverse function, the exponential function, can be computed efficiently.[50] Using lookup tables, CORDIClike methods can be used to compute logarithms if the only available operations are addition and bit shifts.[51][52] Moreover, the binary logarithm algorithm calculates lb(x) recursively based on repeated squarings of x, taking advantage of the relation
Power series
 Taylor series
For any real number z that satisfies 0 < z < 2, the following formula holds:[nb 4][53]
This is a shorthand for saying that ln(z) can be approximated to a more and more accurate value by the following expressions:
For example, with z = 1.5 the third approximation yields 0.4167, which is about 0.011 greater than ln(1.5) = 0.405465. This series approximates ln(z) with arbitrary precision, provided the number of summands is large enough. In elementary calculus, ln(z) is therefore the limit of this series. It is the Taylor series of the natural logarithm at z = 1. The Taylor series of ln(z) provides a particularly useful approximation to ln(1+z) when z is small, z < 1, since then
For example, with z = 0.1 the firstorder approximation gives ln(1.1) ≈ 0.1, which is less than 5% off the correct value 0.0953.
 More efficient series
Another series is based on the area hyperbolic tangent function:
for any real number z > 0.[nb 5][53] Using sigma notation, this is also written as
This series can be derived from the above Taylor series. It converges more quickly than the Taylor series, especially if z is close to 1. For example, for z = 1.5, the first three terms of the second series approximate ln(1.5) with an error of about 3×10^{−6}. The quick convergence for z close to 1 can be taken advantage of in the following way: given a lowaccuracy approximation y ≈ ln(z) and putting
the logarithm of z is:
The better the initial approximation y is, the closer A is to 1, so its logarithm can be calculated efficiently. A can be calculated using the exponential series, which converges quickly provided y is not too large. Calculating the logarithm of larger z can be reduced to smaller values of z by writing z = a · 10^{b}, so that ln(z) = ln(a) + b · ln(10).
A closely related method can be used to compute the logarithm of integers. Putting in the above series, it follows that:
If the logarithm of a large integer n is known, then this series yields a fast converging series for log(n+1), with a rate of convergence of .
Arithmetic–geometric mean approximation
The arithmetic–geometric mean yields high precision approximations of the natural logarithm. Sasaki and Kanada showed in 1982 that it was particularly fast for precisions between 400 and 1000 decimal places, while Taylor series methods were typically faster when less precision was needed. In their work ln(x) is approximated to a precision of 2^{−p} (or p precise bits) by the following formula (due to Carl Friedrich Gauss):[54][55]
Here M(x,y) denotes the arithmetic–geometric mean of x and y. It is obtained by repeatedly calculating the average (arithmetic mean) and (geometric mean) of x and y then let those two numbers become the next x and y. The two numbers quickly converge to a common limit which is the value of M(x,y). m is chosen such that
to ensure the required precision. A larger m makes the M(x,y) calculation take more steps (the initial x and y are farther apart so it takes more steps to converge) but gives more precision. The constants pi and ln(2) can be calculated with quickly converging series.
Feynman's algorithm
While at Los Alamos National Laboratory working on the Manhattan Project, Richard Feynman developed a bitprocessing algorithm that is similar to long division and was later used in the Connection Machine. The algorithm uses the fact that every real number is uniquely representable as a product of distinct factors of the form . The algorithm sequentially builds that product : if , then it changes to . It then increase by one regardless. The algorithm stops when is large enough to give the desired accuracy. Because is the sum of the terms of the form corresponding to those for which the factor was included in the product , may be computed by simple addition, using a table of for all . Any base may be used for the logarithm table.[56]
Applications
Logarithms have many applications inside and outside mathematics. Some of these occurrences are related to the notion of scale invariance. For example, each chamber of the shell of a nautilus is an approximate copy of the next one, scaled by a constant factor. This gives rise to a logarithmic spiral.[57] Benford's law on the distribution of leading digits can also be explained by scale invariance.[58] Logarithms are also linked to selfsimilarity. For example, logarithms appear in the analysis of algorithms that solve a problem by dividing it into two similar smaller problems and patching their solutions.[59] The dimensions of selfsimilar geometric shapes, that is, shapes whose parts resemble the overall picture are also based on logarithms. Logarithmic scales are useful for quantifying the relative change of a value as opposed to its absolute difference. Moreover, because the logarithmic function log(x) grows very slowly for large x, logarithmic scales are used to compress largescale scientific data. Logarithms also occur in numerous scientific formulas, such as the Tsiolkovsky rocket equation, the Fenske equation, or the Nernst equation.
Logarithmic scale
Scientific quantities are often expressed as logarithms of other quantities, using a logarithmic scale. For example, the decibel is a unit of measurement associated with logarithmicscale quantities. It is based on the common logarithm of ratios—10 times the common logarithm of a power ratio or 20 times the common logarithm of a voltage ratio. It is used to quantify the loss of voltage levels in transmitting electrical signals,[60] to describe power levels of sounds in acoustics,[61] and the absorbance of light in the fields of spectrometry and optics. The signaltonoise ratio describing the amount of unwanted noise in relation to a (meaningful) signal is also measured in decibels.[62] In a similar vein, the peak signaltonoise ratio is commonly used to assess the quality of sound and image compression methods using the logarithm.[63]
The strength of an earthquake is measured by taking the common logarithm of the energy emitted at the quake. This is used in the moment magnitude scale or the Richter magnitude scale. For example, a 5.0 earthquake releases 32 times (10^{1.5}) and a 6.0 releases 1000 times (10^{3}) the energy of a 4.0.[64] Another logarithmic scale is apparent magnitude. It measures the brightness of stars logarithmically.[65] Yet another example is pH in chemistry; pH is the negative of the common logarithm of the activity of hydronium ions (the form hydrogen ions H^{+}
_{} take in water).[66] The activity of hydronium ions in neutral water is 10^{−7} mol·L^{−1}, hence a pH of 7. Vinegar typically has a pH of about 3. The difference of 4 corresponds to a ratio of 10^{4} of the activity, that is, vinegar's hydronium ion activity is about 10^{−3} mol·L^{−1}.
Semilog (loglinear) graphs use the logarithmic scale concept for visualization: one axis, typically the vertical one, is scaled logarithmically. For example, the chart at the right compresses the steep increase from 1 million to 1 trillion to the same space (on the vertical axis) as the increase from 1 to 1 million. In such graphs, exponential functions of the form f(x) = a · b^{x} appear as straight lines with slope equal to the logarithm of b. Loglog graphs scale both axes logarithmically, which causes functions of the form f(x) = a · x^{k} to be depicted as straight lines with slope equal to the exponent k. This is applied in visualizing and analyzing power laws.[67]
Psychology
Logarithms occur in several laws describing human perception:[68][69] Hick's law proposes a logarithmic relation between the time individuals take to choose an alternative and the number of choices they have.[70] Fitts's law predicts that the time required to rapidly move to a target area is a logarithmic function of the distance to and the size of the target.[71] In psychophysics, the Weber–Fechner law proposes a logarithmic relationship between stimulus and sensation such as the actual vs. the perceived weight of an item a person is carrying.[72] (This "law", however, is less realistic than more recent models, such as Stevens's power law.[73])
Psychological studies found that individuals with little mathematics education tend to estimate quantities logarithmically, that is, they position a number on an unmarked line according to its logarithm, so that 10 is positioned as close to 100 as 100 is to 1000. Increasing education shifts this to a linear estimate (positioning 1000 10x as far away) in some circumstances, while logarithms are used when the numbers to be plotted are difficult to plot linearly.[74][75]
Probability theory and statistics
Logarithms arise in probability theory: the law of large numbers dictates that, for a fair coin, as the number of cointosses increases to infinity, the observed proportion of heads approaches onehalf. The fluctuations of this proportion about onehalf are described by the law of the iterated logarithm.[76]
Logarithms also occur in lognormal distributions. When the logarithm of a random variable has a normal distribution, the variable is said to have a lognormal distribution.[77] Lognormal distributions are encountered in many fields, wherever a variable is formed as the product of many independent positive random variables, for example in the study of turbulence.[78]
Logarithms are used for maximumlikelihood estimation of parametric statistical models. For such a model, the likelihood function depends on at least one parameter that must be estimated. A maximum of the likelihood function occurs at the same parametervalue as a maximum of the logarithm of the likelihood (the "log likelihood"), because the logarithm is an increasing function. The loglikelihood is easier to maximize, especially for the multiplied likelihoods for independent random variables.[79]
Benford's law describes the occurrence of digits in many data sets, such as heights of buildings. According to Benford's law, the probability that the first decimaldigit of an item in the data sample is d (from 1 to 9) equals log_{10}(d + 1) − log_{10}(d), regardless of the unit of measurement.[80] Thus, about 30% of the data can be expected to have 1 as first digit, 18% start with 2, etc. Auditors examine deviations from Benford's law to detect fraudulent accounting.[81]
Computational complexity
Analysis of algorithms is a branch of computer science that studies the performance of algorithms (computer programs solving a certain problem).[82] Logarithms are valuable for describing algorithms that divide a problem into smaller ones, and join the solutions of the subproblems.[83]
For example, to find a number in a sorted list, the binary search algorithm checks the middle entry and proceeds with the half before or after the middle entry if the number is still not found. This algorithm requires, on average, log_{2}(N) comparisons, where N is the list's length.[84] Similarly, the merge sort algorithm sorts an unsorted list by dividing the list into halves and sorting these first before merging the results. Merge sort algorithms typically require a time approximately proportional to N · log(N).[85] The base of the logarithm is not specified here, because the result only changes by a constant factor when another base is used. A constant factor is usually disregarded in the analysis of algorithms under the standard uniform cost model.[86]
A function f(x) is said to grow logarithmically if f(x) is (exactly or approximately) proportional to the logarithm of x. (Biological descriptions of organism growth, however, use this term for an exponential function.[87]) For example, any natural number N can be represented in binary form in no more than log_{2}(N) + 1 bits. In other words, the amount of memory needed to store N grows logarithmically with N.
Entropy and chaos
Entropy is broadly a measure of the disorder of some system. In statistical thermodynamics, the entropy S of some physical system is defined as
The sum is over all possible states i of the system in question, such as the positions of gas particles in a container. Moreover, p_{i} is the probability that the state i is attained and k is the Boltzmann constant. Similarly, entropy in information theory measures the quantity of information. If a message recipient may expect any one of N possible messages with equal likelihood, then the amount of information conveyed by any one such message is quantified as log_{2}(N) bits.[88]
Lyapunov exponents use logarithms to gauge the degree of chaoticity of a dynamical system. For example, for a particle moving on an oval billiard table, even small changes of the initial conditions result in very different paths of the particle. Such systems are chaotic in a deterministic way, because small measurement errors of the initial state predictably lead to largely different final states.[89] At least one Lyapunov exponent of a deterministically chaotic system is positive.
Fractals
Logarithms occur in definitions of the dimension of fractals.[90] Fractals are geometric objects that are selfsimilar: small parts reproduce, at least roughly, the entire global structure. The Sierpinski triangle (pictured) can be covered by three copies of itself, each having sides half the original length. This makes the Hausdorff dimension of this structure ln(3)/ln(2) ≈ 1.58. Another logarithmbased notion of dimension is obtained by counting the number of boxes needed to cover the fractal in question.
Music
Logarithms are related to musical tones and intervals. In equal temperament, the frequency ratio depends only on the interval between two tones, not on the specific frequency, or pitch, of the individual tones. For example, the note A has a frequency of 440 Hz and Bflat has a frequency of 466 Hz. The interval between A and Bflat is a semitone, as is the one between Bflat and B (frequency 493 Hz). Accordingly, the frequency ratios agree:
Therefore, logarithms can be used to describe the intervals: an interval is measured in semitones by taking the base2^{1/12} logarithm of the frequency ratio, while the base2^{1/1200} logarithm of the frequency ratio expresses the interval in cents, hundredths of a semitone. The latter is used for finer encoding, as it is needed for nonequal temperaments.[91]
Interval (the two tones are played at the same time) 
1/12 tone 
Semitone 
Just major third 
Major third 
Tritone 
Octave 
Frequency ratio r  
Corresponding number of semitones 

Corresponding number of cents 
Number theory
Natural logarithms are closely linked to counting prime numbers (2, 3, 5, 7, 11, ...), an important topic in number theory. For any integer x, the quantity of prime numbers less than or equal to x is denoted π(x). The prime number theorem asserts that π(x) is approximately given by
in the sense that the ratio of π(x) and that fraction approaches 1 when x tends to infinity.[92] As a consequence, the probability that a randomly chosen number between 1 and x is prime is inversely proportional to the number of decimal digits of x. A far better estimate of π(x) is given by the offset logarithmic integral function Li(x), defined by
The Riemann hypothesis, one of the oldest open mathematical conjectures, can be stated in terms of comparing π(x) and Li(x).[93] The Erdős–Kac theorem describing the number of distinct prime factors also involves the natural logarithm.
The logarithm of n factorial, n! = 1 · 2 · ... · n, is given by
This can be used to obtain Stirling's formula, an approximation of n! for large n.[94]
Generalizations
Complex logarithm
All the complex numbers a that solve the equation
are called complex logarithms of z, when z is (considered as) a complex number. A complex number is commonly represented as z = x + iy, where x and y are real numbers and i is an imaginary unit, the square of which is −1. Such a number can be visualized by a point in the complex plane, as shown at the right. The polar form encodes a nonzero complex number z by its absolute value, that is, the (positive, real) distance r to the origin, and an angle between the real (x) axis Re and the line passing through both the origin and z. This angle is called the argument of z.
The absolute value r of z is given by
Using the geometrical interpretation of and and their periodicity in any complex number z may be denoted as
for any integer number k. Evidently the argument of z is not uniquely specified: both φ and φ' = φ + 2kπ are valid arguments of z for all integers k, because adding 2kπ radian or k⋅360°[nb 6] to φ corresponds to "winding" around the origin counterclockwise by k turns. The resulting complex number is always z, as illustrated at the right for k = 1. One may select exactly one of the possible arguments of z as the socalled principal argument, denoted Arg(z), with a capital A, by requiring φ to belong to one, conveniently selected turn, e.g., [95] or [96] These regions, where the argument of z is uniquely determined are called branches of the argument function.
Euler's formula connects the trigonometric functions sine and cosine to the complex exponential:
Using this formula, and again the periodicity, the following identities hold:[97]
where ln(r) is the unique real natural logarithm, a_{k} denote the complex logarithms of z, and k is an arbitrary integer. Therefore, the complex logarithms of z, which are all those complex values a_{k} for which the a_{k}th power of e equals z, are the infinitely many values
 for arbitrary integers k.
Taking k such that is within the defined interval for the principal arguments, then a_{k} is called the principal value of the logarithm, denoted Log(z), again with a capital L. The principal argument of any positive real number x is 0; hence Log(x) is a real number and equals the real (natural) logarithm. However, the above formulas for logarithms of products and powers do not generalize to the principal value of the complex logarithm.[98]
The illustration at the right depicts Log(z), confining the arguments of z to the interval (π, π]. This way the corresponding branch of the complex logarithm has discontinuities all along the negative real x axis, which can be seen in the jump in the hue there. This discontinuity arises from jumping to the other boundary in the same branch, when crossing a boundary, i.e., not changing to the corresponding kvalue of the continuously neighboring branch. Such a locus is called a branch cut. Dropping the range restrictions on the argument makes the relations "argument of z", and consequently the "logarithm of z", multivalued functions.
Inverses of other exponential functions
Exponentiation occurs in many areas of mathematics and its inverse function is often referred to as the logarithm. For example, the logarithm of a matrix is the (multivalued) inverse function of the matrix exponential.[99] Another example is the padic logarithm, the inverse function of the padic exponential. Both are defined via Taylor series analogous to the real case.[100] In the context of differential geometry, the exponential map maps the tangent space at a point of a manifold to a neighborhood of that point. Its inverse is also called the logarithmic (or log) map.[101]
In the context of finite groups exponentiation is given by repeatedly multiplying one group element b with itself. The discrete logarithm is the integer n solving the equation
where x is an element of the group. Carrying out the exponentiation can be done efficiently, but the discrete logarithm is believed to be very hard to calculate in some groups. This asymmetry has important applications in public key cryptography, such as for example in the Diffie–Hellman key exchange, a routine that allows secure exchanges of cryptographic keys over unsecured information channels.[102] Zech's logarithm is related to the discrete logarithm in the multiplicative group of nonzero elements of a finite field.[103]
Further logarithmlike inverse functions include the double logarithm ln(ln(x)), the super or hyper4logarithm (a slight variation of which is called iterated logarithm in computer science), the Lambert W function, and the logit. They are the inverse functions of the double exponential function, tetration, of f(w) = we^{w},[104] and of the logistic function, respectively.[105]
Related concepts
From the perspective of group theory, the identity log(cd) = log(c) + log(d) expresses a group isomorphism between positive reals under multiplication and reals under addition. Logarithmic functions are the only continuous isomorphisms between these groups.[106] By means of that isomorphism, the Haar measure (Lebesgue measure) dx on the reals corresponds to the Haar measure dx/x on the positive reals.[107] The nonnegative reals not only have a multiplication, but also have addition, and form a semiring, called the probability semiring; this is in fact a semifield. The logarithm then takes multiplication to addition (log multiplication), and takes addition to log addition (LogSumExp), giving an isomorphism of semirings between the probability semiring and the log semiring.
Logarithmic oneforms df/f appear in complex analysis and algebraic geometry as differential forms with logarithmic poles.[108]
The polylogarithm is the function defined by
It is related to the natural logarithm by Li_{1}(z) = −ln(1 − z). Moreover, Li_{s}(1) equals the Riemann zeta function ζ(s).[109]
See also
 Cologarithm
 Decimal exponent (dex)
 Exponential function
 Index of logarithm articles
 Logarithmic notation
Notes
 The restrictions on x and b are explained in the section "Analytic properties".
 Some mathematicians disapprove of this notation. In his 1985 autobiography, Paul Halmos criticized what he considered the "childish ln notation," which he said no mathematician had ever used.[18] The notation was invented by Irving Stringham, a mathematician.[19][20]
 For example C, Java, Haskell, and BASIC.
 The same series holds for the principal value of the complex logarithm for complex numbers z satisfying z − 1 < 1.
 The same series holds for the principal value of the complex logarithm for complex numbers z with positive real part.
 See radian for the conversion between 2π and 360 degree.
References
 "The Ultimate Guide to Logarithm — Theory & Applications". Math Vault. 8 May 2016. Retrieved 24 July 2019.
 Hobson, Ernest William (1914). John Napier and the invention of logarithms, 1614; a lecture. University of California Libraries. Cambridge : University Press.
 Remmert, Reinhold. (1991). Theory of complex functions. New York: SpringerVerlag. ISBN 0387971955. OCLC 21118309.
 Shirali, Shailesh (2002), A Primer on Logarithms, Hyderabad: Universities Press, ISBN 9788173714146, esp. section 2
 Kate, S.K.; Bhapkar, H.R. (2009), Basics Of Mathematics, Pune: Technical Publications, ISBN 9788184317558, chapter 1
 All statements in this section can be found in Shailesh Shirali 2002, section 4, (Douglas Downing 2003, p. 275), or Kate & Bhapkar 2009, p. 11, for example.
 Bernstein, Stephen; Bernstein, Ruth (1999), Schaum's outline of theory and problems of elements of statistics. I, Descriptive statistics and probability, Schaum's outline series, New York: McGrawHill, ISBN 9780070050235, p. 21
 Downing, Douglas (2003), Algebra the Easy Way, Barron's Educational Series, Hauppauge, NY: Barron's, ISBN 9780764119729, chapter 17, p. 275
 Wegener, Ingo (2005), Complexity theory: exploring the limits of efficient algorithms, Berlin, New York: SpringerVerlag, ISBN 9783540210450, p. 20
 Van der Lubbe, Jan C. A. (1997), Information Theory, Cambridge University Press, p. 3, ISBN 9780521467605
 Allen, Elizabeth; Triantaphillidou, Sophie (2011), The Manual of Photography, Taylor & Francis, p. 228, ISBN 9780240520377
 Franz Embacher; Petra Oberhuemer, Mathematisches Lexikon (in German), mathe online: für Schule, Fachhochschule, Universität unde Selbststudium, retrieved 22 March 2011
 Taylor, B.N. (1995), Guide for the Use of the International System of Units (SI), US Department of Commerce, archived from the original on 29 June 2007, retrieved 25 May 2007
 Goodrich, Michael T.; Tamassia, Roberto (2002), Algorithm Design: Foundations, Analysis, and Internet Examples, John Wiley & Sons, p. 23,
One of the interesting and sometimes even surprising aspects of the analysis of data structures and algorithms is the ubiquitous presence of logarithms ... As is the custom in the computing literature, we omit writing the base b of the logarithm when b = 2.
 Parkhurst, David F. (2007). Introduction to Applied Mathematics for Environmental Science (illustrated ed.). Springer Science & Business Media. p. 288. ISBN 9780387342283.
 Gullberg, Jan (1997), Mathematics: from the birth of numbers., New York: W. W. Norton & Co, ISBN 9780393040029
 See footnote 1 in Perl, Yehoshua; Reingold, Edward M. (December 1977). "Understanding the complexity of interpolation search". Information Processing Letters. 6 (6): 219–22. doi:10.1016/00200190(77)900722.
 Paul Halmos (1985), I Want to Be a Mathematician: An Automathography, Berlin, New York: SpringerVerlag, ISBN 9780387960784
 Irving Stringham (1893), Uniplanar algebra: being part I of a propædeutic to the higher mathematical analysis, The Berkeley Press, p. xiii
 Roy S. Freedman (2006), Introduction to Financial Technology, Amsterdam: Academic Press, p. 59, ISBN 9780123704788
 See Theorem 3.29 in Rudin, Walter (1984). Principles of mathematical analysis (3rd ed., International student ed.). Auckland: McGrawHill International. ISBN 9780070856134.
 Napier, John (1614), Mirifici Logarithmorum Canonis Descriptio [The Description of the Wonderful Rule of Logarithms] (in Latin), Edinburgh, Scotland: Andrew Hart
 Hobson, Ernest William (1914), John Napier and the invention of logarithms, 1614, Cambridge: The University Press
 Folkerts, Menso; Launert, Dieter; Thom, Andreas (October 2015), Jost Bürgi's Method for Calculating Sines, arXiv:1510.03180, Bibcode:2015arXiv151003180F
 "Burgi biography". wwwhistory.mcs.stand.ac.uk. Retrieved 14 February 2018.
 William Gardner (1742) Tables of Logarithms
 R.C. Pierce (1977) "A brief history of logarithm", TwoYear College Mathematics Journal 8(1):22–26.
 Enrique GonzalesVelasco (2011) Journey through Mathematics – Creative Episodes in its History, §2.4 Hyperbolic logarithms, p. 117, Springer ISBN 9780387921532
 Florian Cajori (1913) "History of the exponential and logarithm concepts", American Mathematical Monthly 20: 5, 35, 75, 107, 148, 173, 205.
 Bryant, Walter W. (1907), A History of Astronomy, London: Methuen & Co, p. 44
 Abramowitz, Milton; Stegun, Irene A., eds. (1972), Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables (10th ed.), New York: Dover Publications, ISBN 9780486612720, section 4.7., p. 89
 CampbellKelly, Martin (2003), The history of mathematical tables: from Sumer to spreadsheets, Oxford scholarship online, Oxford University Press, ISBN 9780198508410, section 2
 Spiegel, Murray R.; Moyer, R.E. (2006), Schaum's outline of college algebra, Schaum's outline series, New York: McGrawHill, ISBN 9780071452274, p. 264
 Maor 2009, sections 1, 13
 Devlin, Keith (2004). Sets, functions, and logic: an introduction to abstract mathematics. Chapman & Hall/CRC mathematics (3rd ed.). Boca Raton, Fla: Chapman & Hall/CRC. ISBN 9781584884491., or see the references in function
 Lang, Serge (1997), Undergraduate analysis, Undergraduate Texts in Mathematics (2nd ed.), Berlin, New York: SpringerVerlag, doi:10.1007/9781475726985, ISBN 9780387948416, MR 1476913, section III.3
 Lang 1997, section IV.2
 Dieudonné, Jean (1969). Foundations of Modern Analysis. 1. Academic Press. p. 84. item (4.3.1)
 Stewart, James (2007), Single Variable Calculus: Early Transcendentals, Belmont: Thomson Brooks/Cole, ISBN 9780495011699, section 1.6
 "Calculation of d/dx(Log(b,x))". Wolfram Alpha. Wolfram Research. Retrieved 15 March 2011.
 Kline, Morris (1998), Calculus: an intuitive and physical approach, Dover books on mathematics, New York: Dover Publications, ISBN 9780486404530, p. 386
 "Calculation of Integrate(ln(x))". Wolfram Alpha. Wolfram Research. Retrieved 15 March 2011.
 Abramowitz & Stegun, eds. 1972, p. 69
 Courant, Richard (1988), Differential and integral calculus. Vol. I, Wiley Classics Library, New York: John Wiley & Sons, ISBN 9780471608424, MR 1009558, section III.6
 Havil, Julian (2003), Gamma: Exploring Euler's Constant, Princeton University Press, ISBN 9780691099835, sections 11.5 and 13.8
 Nomizu, Katsumi (1996), Selected papers on number theory and algebraic geometry, 172, Providence, RI: AMS Bookstore, p. 21, ISBN 9780821804452
 Baker, Alan (1975), Transcendental number theory, Cambridge University Press, ISBN 9780521204613, p. 10
 Muller, JeanMichel (2006), Elementary functions (2nd ed.), Boston, MA: Birkhäuser Boston, ISBN 9780817643720, sections 4.2.2 (p. 72) and 5.5.2 (p. 95)
 Hart; Cheney; Lawson; et al. (1968), Computer Approximations, SIAM Series in Applied Mathematics, New York: John Wiley, section 6.3, pp. 105–11
 Zhang, M.; DelgadoFrias, J.G.; Vassiliadis, S. (1994), "Table driven Newton scheme for high precision logarithm generation", IEE Proceedings  Computers and Digital Techniques, 141 (5): 281–92, doi:10.1049/ipcdt:19941268, ISSN 13502387, Archived from the original on 29 May 2015CS1 maint: unfit url (link), section 1 for an overview
 Meggitt, J.E. (April 1962), "Pseudo Division and Pseudo Multiplication Processes", IBM Journal, 6 (2): 210–26, doi:10.1147/rd.62.0210
 Kahan, W. (20 May 2001), PseudoDivision Algorithms for FloatingPoint Logarithms and Exponentials
 Abramowitz & Stegun, eds. 1972, p. 68
 Sasaki, T.; Kanada, Y. (1982), "Practically fast multipleprecision evaluation of log(x)", Journal of Information Processing, 5 (4): 247–50, retrieved 30 March 2011
 Ahrendt, Timm (1999), "Fast Computations of the Exponential Function", Stacs 99, Lecture notes in computer science, 1564, Berlin, New York: Springer, pp. 302–12, doi:10.1007/3540491163_28, ISBN 9783540656913
 Hillis, Danny (15 January 1989). "Richard Feynman and The Connection Machine". Physics Today. 42 (2): 78. Bibcode:1989PhT....42b..78H. doi:10.1063/1.881196.
 Maor 2009, p. 135
 Frey, Bruce (2006), Statistics hacks, Hacks Series, Sebastopol, CA: O'Reilly, ISBN 9780596101640, chapter 6, section 64
 Ricciardi, Luigi M. (1990), Lectures in applied mathematics and informatics, Manchester: Manchester University Press, ISBN 9780719026713, p. 21, section 1.3.2
 Bakshi, U.A. (2009), Telecommunication Engineering, Pune: Technical Publications, ISBN 9788184317251, section 5.2
 Maling, George C. (2007), "Noise", in Rossing, Thomas D. (ed.), Springer handbook of acoustics, Berlin, New York: SpringerVerlag, ISBN 9780387304465, section 23.0.2
 Tashev, Ivan Jelev (2009), Sound Capture and Processing: Practical Approaches, New York: John Wiley & Sons, p. 98, ISBN 9780470319833
 Chui, C.K. (1997), Wavelets: a mathematical tool for signal processing, SIAM monographs on mathematical modeling and computation, Philadelphia: Society for Industrial and Applied Mathematics, ISBN 9780898713848
 Crauder, Bruce; Evans, Benny; Noell, Alan (2008), Functions and Change: A Modeling Approach to College Algebra (4th ed.), Boston: Cengage Learning, ISBN 9780547156699, section 4.4.
 Bradt, Hale (2004), Astronomy methods: a physical approach to astronomical observations, Cambridge Planetary Science, Cambridge University Press, ISBN 9780521535519, section 8.3, p. 231
 IUPAC (1997), A. D. McNaught, A. Wilkinson (ed.), Compendium of Chemical Terminology ("Gold Book") (2nd ed.), Oxford: Blackwell Scientific Publications, doi:10.1351/goldbook, ISBN 9780967855097
 Bird, J.O. (2001), Newnes engineering mathematics pocket book (3rd ed.), Oxford: Newnes, ISBN 9780750649926, section 34
 Goldstein, E. Bruce (2009), Encyclopedia of Perception, Encyclopedia of Perception, Thousand Oaks, CA: Sage, ISBN 9781412940818, pp. 355–56
 Matthews, Gerald (2000), Human performance: cognition, stress, and individual differences, Human Performance: Cognition, Stress, and Individual Differences, Hove: Psychology Press, ISBN 9780415044066, p. 48
 Welford, A.T. (1968), Fundamentals of skill, London: Methuen, ISBN 9780416030006, OCLC 219156, p. 61
 Paul M. Fitts (June 1954), "The information capacity of the human motor system in controlling the amplitude of movement", Journal of Experimental Psychology, 47 (6): 381–91, doi:10.1037/h0055392, PMID 13174710, reprinted in Paul M. Fitts (1992), "The information capacity of the human motor system in controlling the amplitude of movement" (PDF), Journal of Experimental Psychology: General, 121 (3): 262–69, doi:10.1037/00963445.121.3.262, PMID 1402698, retrieved 30 March 2011
 Banerjee, J.C. (1994), Encyclopaedic dictionary of psychological terms, New Delhi: M.D. Publications, p. 304, ISBN 9788185880280, OCLC 33860167
 Nadel, Lynn (2005), Encyclopedia of cognitive science, New York: John Wiley & Sons, ISBN 9780470016190, lemmas Psychophysics and Perception: Overview
 Siegler, Robert S.; Opfer, John E. (2003), "The Development of Numerical Estimation. Evidence for Multiple Representations of Numerical Quantity" (PDF), Psychological Science, 14 (3): 237–43, CiteSeerX 10.1.1.727.3696, doi:10.1111/14679280.02438, PMID 12741747, archived from the original (PDF) on 17 May 2011, retrieved 7 January 2011
 Dehaene, Stanislas; Izard, Véronique; Spelke, Elizabeth; Pica, Pierre (2008), "Log or Linear? Distinct Intuitions of the Number Scale in Western and Amazonian Indigene Cultures", Science, 320 (5880): 1217–20, Bibcode:2008Sci...320.1217D, CiteSeerX 10.1.1.362.2390, doi:10.1126/science.1156540, PMC 2610411, PMID 18511690
 Breiman, Leo (1992), Probability, Classics in applied mathematics, Philadelphia: Society for Industrial and Applied Mathematics, ISBN 9780898712964, section 12.9
 Aitchison, J.; Brown, J.A.C. (1969), The lognormal distribution, Cambridge University Press, ISBN 9780521040112, OCLC 301100935
 Jean Mathieu and Julian Scott (2000), An introduction to turbulent flow, Cambridge University Press, p. 50, ISBN 9780521775380
 Rose, Colin; Smith, Murray D. (2002), Mathematical statistics with Mathematica, Springer texts in statistics, Berlin, New York: SpringerVerlag, ISBN 9780387952345, section 11.3
 Tabachnikov, Serge (2005), Geometry and Billiards, Providence, RI: American Mathematical Society, pp. 36–40, ISBN 9780821839195, section 2.1
 Durtschi, Cindy; Hillison, William; Pacini, Carl (2004). "The Effective Use of Benford's Law in Detecting Fraud in Accounting Data" (PDF). Journal of Forensic Accounting. V: 17–34. Archived from the original (PDF) on 29 August 2017. Retrieved 28 May 2018.
 Wegener, Ingo (2005), Complexity theory: exploring the limits of efficient algorithms, Berlin, New York: SpringerVerlag, ISBN 9783540210450, pp. 1–2
 Harel, David; Feldman, Yishai A. (2004), Algorithmics: the spirit of computing, New York: AddisonWesley, ISBN 9780321117847, p. 143
 Knuth, Donald (1998), The Art of Computer Programming, Reading, MA: AddisonWesley, ISBN 9780201896855, section 6.2.1, pp. 409–26
 Donald Knuth 1998, section 5.2.4, pp. 158–68
 Wegener, Ingo (2005), Complexity theory: exploring the limits of efficient algorithms, Berlin, New York: SpringerVerlag, p. 20, ISBN 9783540210450
 Mohr, Hans; Schopfer, Peter (1995), Plant physiology, Berlin, New York: SpringerVerlag, ISBN 9783540580164, chapter 19, p. 298
 Eco, Umberto (1989), The open work, Harvard University Press, ISBN 9780674639768, section III.I
 Sprott, Julien Clinton (2010), "Elegant Chaos: Algebraically Simple Chaotic Flows", Elegant Chaos: Algebraically Simple Chaotic Flows. Edited by Sprott Julien Clinton. Published by World Scientific Publishing Co. Pte. Ltd, New Jersey: World Scientific, Bibcode:2010ecas.book.....S, doi:10.1142/7183, ISBN 9789812838810, section 1.9
 Helmberg, Gilbert (2007), Getting acquainted with fractals, De Gruyter Textbook, Berlin, New York: Walter de Gruyter, ISBN 9783110190922
 Wright, David (2009), Mathematics and music, Providence, RI: AMS Bookstore, ISBN 9780821848739, chapter 5
 Bateman, P.T.; Diamond, Harold G. (2004), Analytic number theory: an introductory course, New Jersey: World Scientific, ISBN 9789812560803, OCLC 492669517, theorem 4.1
 P. T. Bateman & Diamond 2004, Theorem 8.15
 Slomson, Alan B. (1991), An introduction to combinatorics, London: CRC Press, ISBN 9780412353703, chapter 4
 Ganguly, S. (2005), Elements of Complex Analysis, Kolkata: Academic Publishers, ISBN 9788187504863, Definition 1.6.3
 Nevanlinna, Rolf Herman; Paatero, Veikko (2007), "Introduction to complex analysis", London: Hilger, Providence, RI: AMS Bookstore, Bibcode:1974aitc.book.....W, ISBN 9780821843994, section 5.9
 Moore, Theral Orvis; Hadlock, Edwin H. (1991), Complex analysis, Singapore: World Scientific, ISBN 9789810202460, section 1.2
 Wilde, Ivan Francis (2006), Lecture notes on complex analysis, London: Imperial College Press, ISBN 9781860946424, theorem 6.1.
 Higham, Nicholas (2008), Functions of Matrices. Theory and Computation, Philadelphia, PA: SIAM, ISBN 9780898716467, chapter 11.
 Neukirch, Jürgen (1999). Algebraic Number Theory. Grundlehren der mathematischen Wissenschaften. 322. Berlin: SpringerVerlag. ISBN 9783540653998. MR 1697859. Zbl 0956.11021., section II.5.
 Hancock, Edwin R.; Martin, Ralph R.; Sabin, Malcolm A. (2009), Mathematics of Surfaces XIII: 13th IMA International Conference York, UK, September 7–9, 2009 Proceedings, Springer, p. 379, ISBN 9783642035951
 Stinson, Douglas Robert (2006), Cryptography: Theory and Practice (3rd ed.), London: CRC Press, ISBN 9781584885085
 Lidl, Rudolf; Niederreiter, Harald (1997), Finite fields, Cambridge University Press, ISBN 9780521392310
 Corless, R.; Gonnet, G.; Hare, D.; Jeffrey, D.; Knuth, Donald (1996), "On the Lambert W function" (PDF), Advances in Computational Mathematics, 5: 329–59, doi:10.1007/BF02124750, ISSN 10197168, archived from the original (PDF) on 14 December 2010, retrieved 13 February 2011
 Cherkassky, Vladimir; Cherkassky, Vladimir S.; Mulier, Filip (2007), Learning from data: concepts, theory, and methods, Wiley series on adaptive and learning systems for signal processing, communications, and control, New York: John Wiley & Sons, ISBN 9780471681823, p. 357
 Bourbaki, Nicolas (1998), General topology. Chapters 5–10, Elements of Mathematics, Berlin, New York: SpringerVerlag, ISBN 9783540645634, MR 1726872, section V.4.1
 Ambartzumian, R.V. (1990), Factorization calculus and geometric probability, Cambridge University Press, ISBN 9780521345354, section 1.4
 Esnault, Hélène; Viehweg, Eckart (1992), Lectures on vanishing theorems, DMV Seminar, 20, Basel, Boston: Birkhäuser Verlag, CiteSeerX 10.1.1.178.3227, doi:10.1007/9783034886000, ISBN 9783764328221, MR 1193913, section 2
 Apostol, T.M. (2010), "Logarithm", in Olver, Frank W. J.; Lozier, Daniel M.; Boisvert, Ronald F.; Clark, Charles W. (eds.), NIST Handbook of Mathematical Functions, Cambridge University Press, ISBN 9780521192255, MR 2723248
External links
Media related to Logarithm at Wikimedia Commons The dictionary definition of logarithm at Wiktionary  Khan Academy: Logarithms, free online micro lectures
 Hazewinkel, Michiel, ed. (2001) [1994], "Logarithmic function", Encyclopedia of Mathematics, Springer Science+Business Media B.V. / Kluwer Academic Publishers, ISBN 9781556080104
 Colin Byfleet, Educational video on logarithms, retrieved 12 October 2010
 Edward Wright, Translation of Napier's work on logarithms, Archived from the original on 3 December 2002, retrieved 12 October 2010CS1 maint: unfit url (link)
 Glaisher, James Whitbread Lee (1911). Encyclopædia Britannica. 16 (11th ed.). Cambridge University Press. pp. 868–77. . In Chisholm, Hugh (ed.).