In mathematics, the logarithm is the inverse function to exponentiation. That means that the logarithm of a number x to the baseb is the exponent to which b must be raised to produce x. For example, since 1000 = 103, the logarithm base 10 of 1000 is 3, or log10 (1000) = 3. The logarithm of x to baseb is denoted as logb (x), or without parentheses, logbx, or even without the explicit base, log x, when no confusion is possible, or when the base does not matter such as in big O notation.
Logarithms were introduced by John Napier in 1614 as a means of simplifying calculations. They were rapidly adopted by navigators, scientists, engineers, surveyors and others to perform high-accuracy computations more easily. Using logarithm tables, tedious multi-digit multiplication steps can be replaced by table look-ups and simpler addition. This is possible because 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 present-day 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.
The concept of logarithm as the inverse of exponentiation extends to other mathematical structures as well. However, in general settings, the logarithm tends to be a multi-valued function. For example, the complex logarithm is the multi-valued inverse of the complex exponential function. Similarly, the discrete logarithm is the multi-valued inverse of the exponential function in finite groups; it has uses in public-key cryptography.
Addition, multiplication, and exponentiation are three of the most fundamental arithmetic operations. The inverse of addition is subtraction, and the inverse of multiplication is division. Similarly, a logarithm is the inverse operation of exponentiation. Exponentiation is when a number b, the base, is raised to a certain power y, the exponent, to give a value x; this is denoted
For example, raising 2 to the power of 3 gives 8:
The logarithm of base b is the inverse operation, that provides the output y from the input x. That is, is equivalent to if b is a positive real number. (If b is not a positive real number, both exponentiation and logarithm can be defined but may take several values, which makes definitions much more complicated.)
One of the main historical motivations of introducing logarithms is the formula
which allowed (before the invention of computers) reducing computation of multiplications and divisions to additions, subtractions and logarithm table looking.
Given a positive real numberb such that b ≠ 1, 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 unique real number y such that .
The logarithm is denoted "logbx" (pronounced as "the logarithm of x to base b", "the base-b logarithm of x", or most commonly "the log, base b, of x").
An equivalent and more succinct definition is that the function logb is the inverse function to the function .
log2 16 = 4, since 24 = 2 × 2 × 2 × 2 = 16.
Logarithms can also be negative: since
log10 150 is approximately 2.176, which lies between 2 and 3, just as 150 lies between 102 = 100 and 103 = 1000.
For any base b, logbb = 1 and logb 1 = 0, since b1 = b and b0 = 1, respectively.
Several important formulas, sometimes called logarithmic identities or logarithmic laws, relate logarithms to one another.
Product, quotient, power, and rootEdit
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 p-th power of a number is ptimes the logarithm of the number itself; the logarithm of a p-th 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.
Change of baseEdit
The logarithm logbx can be computed from the logarithms of x and b with respect to an arbitrary base k using the following formula:[nb 2]
Typical scientific calculators calculate the logarithms to bases 10 and e. 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 y = logbx to an unknown base b, the base is given by:
which can be seen from taking the defining equation to the power of
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, base-10 logarithms (the common logarithm) are easy to use for manual calculations in the decimal number system:
The following table lists common notations for logarithms to these bases and the fields where they are used. Many disciplines write log x instead of logbx, when the intended base can be determined from the context. The "ISO notation" column lists designations suggested by the International Organization for Standardization (ISO 80000-2). 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, log usually refers to log2, and in mathematics log usually refers to loge. In other contexts, log often means log10.
The history of logarithms in seventeenth-century Europe saw 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 Canon of Logarithms). 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. Napier coined the term for logarithm in Middle Latin, "logarithmus," derived from the Greek, literally meaning, "ratio-number," from logos "proportion, ratio, word" + arithmos "number".
The common logarithm of a number is the index of that power of ten which equals the number. 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". 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. Such methods are called prosthaphaeresis.
"...[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."
As the function f(x) = bx is the inverse function of logbx, it has been called an antilogarithm. Nowadays, this function is more commonly called an exponential function.
A key tool that enabled the practical use of logarithms was the table of logarithms. 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 from 1 to 1000, with a precision of 14 digits. Subsequently, tables with increasing scope were written. These tables listed the values of log10x for any number x in a certain range, at a certain precision. Base-10 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. The characteristic of 10 · x is one plus the characteristic of x, and their mantissas are the same. Thus using a three-digit log table, the logarithm of 3542 is approximated by
The value of 10x can be determined by reverse look up in the same table, since the logarithm is a monotonic function.
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:
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
Trigonometric calculations were facilitated by tables that contained the common logarithms of trigonometric functions.
Another critical application was the slide rule, a pair of logarithmically divided scales used for calculation. The non-sliding 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.
A deeper study of logarithms requires the concept of a function. A function is a rule that, given one number, produces another number. An example is the function producing the x-th power of b from any real number x, where the base b is a fixed number. This function is written as f(x) = bx. When b is positive and unequal to 1, we show below that f is invertible when considered as a function from the reals to the positive reals.
Let b be a positive real number not equal to 1 and let f(x) = bx.
It is a standard result in real analysis that any continuous strictly monotonic function is bijective between its domain and range. This fact follows from the intermediate value theorem. Now, f is strictly increasing (for b > 1), or strictly decreasing (for 0 < b < 1), is continuous, has domain , and has range . Therefore, f is a bijection from to . In other words, for each positive real number y, there is exactly one real number x such that .
We let denote the inverse of f. That is, logby is the unique real number x such that . This function is called the base-blogarithm function or logarithmic function (or just logarithm).
Characterization by the product formulaEdit
The function logbx can also be essentially characterized by the product formula
More precisely, the logarithm to any base b > 1 is the only increasing functionf from the positive reals to the reals satisfying f(b) = 1 and
Graph of the logarithm functionEdit
As discussed above, the function logb is the inverse to the exponential function . Therefore, their graphs correspond to each other upon exchanging the x- and the y-coordinates (or upon reflection at the diagonal line x = y), as shown at the right: a point (t, u = bt) on the graph of f yields a point (u, t = logbu) on the graph of the logarithm and vice versa. As a consequence, logb (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, logb(x) is an increasing function. For b < 1, logb (x) tends to minus infinity instead. When x approaches zero, logbx goes to minus infinity for b > 1 (plus infinity for b < 1, respectively).
Derivative and antiderivativeEdit
Analytic properties of functions pass to their inverses. Thus, as f(x) = bx is a continuous and differentiable function, so is logby. Roughly, a continuous function is differentiable if its graph has no sharp "corners". Moreover, as the derivative of f(x) evaluates to ln(b) bx by the properties of the exponential function, the chain rule implies that the derivative of logbx is given by
That is, the slope of the tangent touching the graph of the base-b logarithm at the point (x, logb (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 generalized functional argument f(x) is
This definition has the advantage that it does not rely on the exponential function or any trigonometric functions; the definition is in terms of an integral of a simple reciprocal. As an integral, ln(t) equals the area between the x-axis and the graph of the function 1/x, ranging from x = 1 to x = t. This is a consequence of the fundamental theorem of calculus and the fact that the derivative of ln(x) is 1/x. Product and power logarithm formulas can be derived from this definition. 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(tr) = r ln(t) may be derived in a similar way:
Logarithms are easy to compute in some cases, such as log10 (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.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. Using look-up tables, CORDIC-like methods can be used to compute logarithms by using only the operations of addition and bit shifts. Moreover, the binary logarithm algorithm calculates lb(x)recursively, based on repeated squarings of x, taking advantage of the relation
For any real number z that satisfies 0 < z ≤ 2, the following formula holds:[nb 5]
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 first-order approximation gives ln(1.1) ≈ 0.1, which is less than 5% off the correct value 0.0953.
This series can be derived from the above Taylor series. It converges quicker 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 low-accuracy 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 · 10b, 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 approximationEdit
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):
Here M(x, y) denotes the arithmetic–geometric mean of x and y. It is obtained by repeatedly calculating the average (x + y)/2 (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 π and ln(2) can be calculated with quickly converging series.
While at Los Alamos National Laboratory working on the Manhattan Project, Richard Feynman developed a bit-processing algorithm, to compute the logarithm, that is similar to long division and was later used in the Connection Machine. The algorithm uses the fact that every real number 1 < x < 2 is representable as a product of distinct factors of the form 1 + 2−k. The algorithm sequentially builds that product P, starting with P = 1 and k = 1: if P · (1 + 2−k) < x, then it changes P to P · (1 + 2−k). It then increases by one regardless. The algorithm stops when k is large enough to give the desired accuracy. Because log(x) is the sum of the terms of the form log(1 + 2−k) corresponding to those k for which the factor 1 + 2−k was included in the product P, log(x) may be computed by simple addition, using a table of log(1 + 2−k) for all k. Any base may be used for the logarithm table.
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.Benford's law on the distribution of leading digits can also be explained by scale invariance. Logarithms are also linked to self-similarity. 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. The dimensions of self-similar 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 large-scale scientific data. Logarithms also occur in numerous scientific formulas, such as the Tsiolkovsky rocket equation, the Fenske equation, or the Nernst equation.
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 (101.5) and a 6.0 releases 1000 times (103) the energy of a 4.0.Apparent magnitude measures the brightness of stars logarithmically. In chemistry the negative of the decimal logarithm, the decimal cologarithm, is indicated by the letter p. For instance, pH is the decimal cologarithm of the activity of hydronium ions (the form hydrogenionsH+ take in water). The activity of hydronium ions in neutral water is 10−7mol·L−1, hence a pH of 7. Vinegar typically has a pH of about 3. The difference of 4 corresponds to a ratio of 104 of the activity, that is, vinegar's hydronium ion activity is about 10−3 mol·L−1.
Semilog (log–linear) 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 · bx appear as straight lines with slope equal to the logarithm of b. Log-log graphs scale both axes logarithmically, which causes functions of the form f(x) = a · xk to be depicted as straight lines with slope equal to the exponent k. This is applied in visualizing and analyzing power laws.
Logarithms occur in several laws describing human perception:Hick's law proposes a logarithmic relation between the time individuals take to choose an alternative and the number of choices they have.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. 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. (This "law", however, is less realistic than more recent models, such as Stevens's power law.)
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 10 times as far away) in some circumstances, while logarithms are used when the numbers to be plotted are difficult to plot linearly.
Logarithms also occur in log-normal distributions. When the logarithm of a random variable has a normal distribution, the variable is said to have a log-normal distribution. Log-normal 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.
Logarithms are used for maximum-likelihood 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 parameter-value as a maximum of the logarithm of the likelihood (the "log likelihood"), because the logarithm is an increasing function. The log-likelihood is easier to maximize, especially for the multiplied likelihoods for independent random variables.
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 decimal-digit of an item in the data sample is d (from 1 to 9) equals log10 (d + 1) − log10 (d), regardless of the unit of measurement. 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.
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, log2 (N) comparisons, where N is the list's length. 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 toN · log(N). 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.
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.) For example, any natural numberN can be represented in binary form in no more than log2N + 1bits. In other words, the amount of memory needed to store N grows logarithmically with N.
The sum is over all possible states i of the system in question, such as the positions of gas particles in a container. Moreover, pi 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 log2N bits.
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. At least one Lyapunov exponent of a deterministically chaotic system is positive.
Logarithms occur in definitions of the dimension of fractals. Fractals are geometric objects that are self-similar in the sense that 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 logarithm-based notion of dimension is obtained by counting the number of boxes needed to cover the fractal in question.
Four different octaves shown on a linear scale, then shown on a logarithmic scale (as the ear hears them).
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 B-flat has a frequency of 466 Hz. The interval between A and B-flat is a semitone, as is the one between B-flat 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 base-21/12 logarithm of the frequency ratio, while the base-21/1200 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 non-equal temperaments.
Interval (the two tones are played at the same time)
in the sense that the ratio of π(x) and that fraction approaches 1 when x tends to infinity. 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
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 non-zero complex number z by its absolute value, that is, the (positive, real) distance r to the origin, and an angle between the real (x) axisRe 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 sine and cosine and their periodicity in 2π, 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 2kπradians or k⋅360°[nb 7] to φ corresponds to "winding" around the origin counter-clock-wise by kturns. 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 so-called principal argument, denoted Arg(z), with a capital A, by requiring φ to belong to one, conveniently selected turn, e.g. −π < φ ≤ π or 0 ≤ φ < 2π. These regions, where the argument of z is uniquely determined are called branches of the argument function.
Using this formula, and again the periodicity, the following identities hold:
where ln(r) is the unique real natural logarithm, ak denote the complex logarithms of z, and k is an arbitrary integer. Therefore, the complex logarithms of z, which are all those complex values ak for which the ak-th power of e equals z, are the infinitely many values
for arbitrary integers k.
Taking k such that φ + 2kπ is within the defined interval for the principal arguments, then ak 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.
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 k-value 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", multi-valued functions.
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.Zech's logarithm is related to the discrete logarithm in the multiplicative group of non-zero elements of a finite field.
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. 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. The non-negative 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.
Taking the logarithm to base k of the defining identity one gets
The formula follows by solving for
^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.
The notation was invented by Irving Stringham, a mathematician.
^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: Springer-Verlag, ISBN 0387971955, OCLC 21118309
^Kate, S.K.; Bhapkar, H.R. (2009), Basics Of Mathematics, Pune: Technical Publications, ISBN 978-81-8431-755-8, 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. 1-1, 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: McGraw-Hill, ISBN 978-0-07-005023-5, p. 21
^Downing, Douglas (2003), Algebra the Easy Way, Barron's Educational Series, Hauppauge, NY: Barron's, ISBN 978-0-7641-1972-9, chapter 17, p. 275
^Wegener, Ingo (2005), Complexity theory: exploring the limits of efficient algorithms, Berlin, New York: Springer-Verlag, ISBN 978-3-540-21045-0, p. 20
^Van der Lubbe, Jan C. A. (1997), Information Theory, Cambridge University Press, p. 3, ISBN 978-0-521-46760-5
^Allen, Elizabeth; Triantaphillidou, Sophie (2011), The Manual of Photography, Taylor & Francis, p. 228, ISBN 978-0-240-52037-7
^Quantities and units – Part 2: Mathematics (ISO 80000-2:2019); EN ISO 80000-2
^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 978-0-387-34228-3
^Gullberg, Jan (1997), Mathematics: from the birth of numbers., New York: W. W. Norton & Co, ISBN 978-0-393-04002-9
^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:10.1016/0020-0190(77)90072-2
Paul Halmos (1985), I Want to Be a Mathematician: An Automathography, Berlin, New York: Springer-Verlag, ISBN 978-0-387-96078-4
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 978-0-12-370478-8
^See Theorem 3.29 in Rudin, Walter (1984), Principles of mathematical analysis (3rd ed., International student ed.), Auckland: McGraw-Hill International, ISBN 978-0-07-085613-4
^Napier, John (1614), Mirifici Logarithmorum Canonis Descriptio [The Description of the Wonderful Canon of Logarithms] (in Latin), Edinburgh, Scotland: Andrew Hart The sequel ... Constructio was published posthumously: Napier, John (1619), Mirifici Logarithmorum Canonis Constructio [The Construction of the Wonderful Rule of Logarithms] (in Latin), Edinburgh: Andrew Hart Ian Bruce has made an annotated translation of both books (2012), available from 17centurymaths.com.
^Hobson, Ernest William (1914), John Napier and the invention of logarithms, 1614, Cambridge: The University Press
^Folkerts, Menso; Launert, Dieter; Thom, Andreas (2016), "Jost Bürgi's method for calculating sines", Historia Mathematica, 43 (2): 133–147, arXiv:1510.03180, doi:10.1016/j.hm.2016.03.001, MR 3489006, S2CID 119326088
^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 978-1-58488-449-1, or see the references in function
^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.; Delgado-Frias, 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/ip-cdt:19941268, ISSN 1350-2387, section 1 for an overview
^Meggitt, J.E. (April 1962), "Pseudo Division and Pseudo Multiplication Processes", IBM Journal of Research and Development, 6 (2): 210–26, doi:10.1147/rd.62.0210, S2CID 19387286
^Kahan, W. (20 May 2001), Pseudo-Division Algorithms for Floating-Point Logarithms and Exponentials
^Sasaki, T.; Kanada, Y. (1982), "Practically fast multiple-precision 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, vol. 1564, Berlin, New York: Springer, pp. 302–12, doi:10.1007/3-540-49116-3_28, ISBN 978-3-540-65691-3
^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
^Crauder, Bruce; Evans, Benny; Noell, Alan (2008), Functions and Change: A Modeling Approach to College Algebra (4th ed.), Boston: Cengage Learning, ISBN 978-0-547-15669-9, section 4.4.
^Bradt, Hale (2004), Astronomy methods: a physical approach to astronomical observations, Cambridge Planetary Science, Cambridge University Press, ISBN 978-0-521-53551-9, section 8.3, p. 231
^Nørby, Jens (2000). "The origin and the meaning of the little p in pH". Trends in Biochemical Sciences. 25 (1): 36–37. doi:10.1016/S0968-0004(99)01517-0. PMID 10637613.
^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 978-0-9678550-9-7
^Bird, J.O. (2001), Newnes engineering mathematics pocket book (3rd ed.), Oxford: Newnes, ISBN 978-0-7506-4992-6, section 34
^Goldstein, E. Bruce (2009), Encyclopedia of Perception, Encyclopedia of Perception, Thousand Oaks, CA: Sage, ISBN 978-1-4129-4081-8, pp. 355–56
^Matthews, Gerald (2000), Human Performance: Cognition, Stress, and Individual Differences, Hove: Psychology Press, ISBN 978-0-415-04406-6, p. 48
^Welford, A.T. (1968), Fundamentals of skill, London: Methuen, ISBN 978-0-416-03000-6, 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, S2CID 501599, 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:10.1037/0096-34126.96.36.1992, PMID 1402698, retrieved 30 March 2011
^Banerjee, J.C. (1994), Encyclopaedic dictionary of psychological terms, New Delhi: M.D. Publications, p. 304, ISBN 978-81-85880-28-0, OCLC 33860167
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, CiteSeerX10.1.1.727.3696, doi:10.1111/1467-9280.02438, PMID 12741747, S2CID 9583202, 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, CiteSeerX10.1.1.362.2390, doi:10.1126/science.1156540, PMC2610411, PMID 18511690
^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: Springer-Verlag, ISBN 978-3-540-21045-0, pp. 1–2
^Harel, David; Feldman, Yishai A. (2004), Algorithmics: the spirit of computing, New York: Addison-Wesley, ISBN 978-0-321-11784-7, p. 143
^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 978-981-283-881-0, section 1.9
^Helmberg, Gilbert (2007), Getting acquainted with fractals, De Gruyter Textbook, Berlin, New York: Walter de Gruyter, ISBN 978-3-11-019092-2
^Wright, David (2009), Mathematics and music, Providence, RI: AMS Bookstore, ISBN 978-0-8218-4873-9, chapter 5
^Bateman, P.T.; Diamond, Harold G. (2004), Analytic number theory: an introductory course, New Jersey: World Scientific, ISBN 978-981-256-080-3, OCLC 492669517, theorem 4.1
^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 978-3-642-03595-1
^Stinson, Douglas Robert (2006), Cryptography: Theory and Practice (3rd ed.), London: CRC Press, ISBN 978-1-58488-508-5
^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 1019-7168, S2CID 29028411, 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 978-0-471-68182-3, p. 357