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Common logarithm
Common logarithm
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The graph shows that log base ten of x rapidly approaches minus infinity as x approaches zero, but gradually rises to the value two as x approaches one hundred.
A graph of the common logarithm of numbers from 0.1 to 100

In mathematics, the common logarithm (aka "standard logarithm") is the logarithm with base 10.[1] It is also known as the decadic logarithm, the decimal logarithm and the Briggsian logarithm. The name "Briggsian logarithm" is in honor of the British mathematician Henry Briggs who conceived of and developed the values for the "common logarithm". Historically, the "common logarithm" was known by its Latin name logarithmus decimalis[2] or logarithmus decadis.[3]

The mathematical notation for using the common logarithm is log(x),[4] log10(x),[5] or sometimes Log(x) with a capital L;[a] on calculators, it is printed as "log",[6] but mathematicians usually mean natural logarithm (logarithm with base e ≈ 2.71828) rather than common logarithm when writing "log", since the natural logarithm is – contrary to what the name of the common logarithm implies – the most commonly used logarithm in pure math.[7]

Page from a table of common logarithms. This page shows the logarithms for numbers from 1000 to 1509 to five decimal places. The complete table covers values up to 9999.

Before the early 1970s, handheld electronic calculators were not available, and mechanical calculators capable of multiplication were bulky, expensive and not widely available. Instead, tables of base-10 logarithms were used in science, engineering and navigation—when calculations required greater accuracy than could be achieved with a slide rule. By turning multiplication and division to addition and subtraction, use of logarithms avoided laborious and error-prone paper-and-pencil multiplications and divisions.[1] Because logarithms were so useful, tables of base-10 logarithms were given in appendices of many textbooks. Mathematical and navigation handbooks included tables of the logarithms of trigonometric functions as well.[8] For the history of such tables, see log table.

Numbers are placed on slide rule scales at distances proportional to the differences between their logarithms. By mechanically adding the distance from 1 to 2 on the lower scale to the distance from 1 to 3 on the upper scale, one can quickly determine that 2  ×  3 = 6.

Mantissa and characteristic

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An important property of base-10 logarithms, which makes them so useful in calculations, is that the logarithm of numbers greater than 1 that differ by a factor of a power of 10 all have the same fractional part. The fractional part is known as the mantissa.[b] Thus, log tables need only show the fractional part. Tables of common logarithms typically listed the mantissa, to four or five decimal places or more, of each number in a range, e.g. 1000 to 9999.

The integer part, called the characteristic, can be computed by simply counting how many places the decimal point must be moved, so that it is just to the right of the first significant digit. For example, the logarithm of 120 is given by the following calculation:

The last number (0.07918)—the fractional part or the mantissa of the common logarithm of 120—can be found in the table shown. The location of the decimal point in 120 tells us that the integer part of the common logarithm of 120, the characteristic, is 2.

Negative logarithms

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Positive numbers less than 1 have negative logarithms. For example,

To avoid the need for separate tables to convert positive and negative logarithms back to their original numbers, one can express a negative logarithm as a negative integer characteristic plus a positive mantissa. To facilitate this, a special notation, called bar notation, is used:

The bar over the characteristic indicates that it is negative, while the mantissa remains positive. When reading a number in bar notation out loud, the symbol is read as "bar n", so that is read as "bar 2 point 07918...". An alternative convention is to express the logarithm modulo 10, in which case

with the actual value of the result of a calculation determined by knowledge of the reasonable range of the result.[c]

The following example uses the bar notation to calculate 0.012 × 0.85 = 0.0102:

* This step makes the mantissa between 0 and 1, so that its antilog (10mantissa) can be looked up.

The following table shows how the same mantissa can be used for a range of numbers differing by powers of ten:

Common logarithm, characteristic, and mantissa of powers of 10 times a number
Number Logarithm Characteristic Mantissa Combined form
n = 5 × 10i log10(n) i = floor(log10(n)) log10(n) − i
5 000 000 6.698 970... 6 0.698 970... 6.698 970...
50 1.698 970... 1 0.698 970... 1.698 970...
5 0.698 970... 0 0.698 970... 0.698 970...
0.5 −0.301 029... −1 0.698 970... 1.698 970...
0.000 005 −5.301 029... −6 0.698 970... 6.698 970...

Note that the mantissa is common to all of the 5  ×  10i. This holds for any positive real number  because

Since i is a constant, the mantissa comes from , which is constant for given . This allows a table of logarithms to include only one entry for each mantissa. In the example of 5  ×  10i, 0.698 970 (004 336 018 ...) will be listed once indexed by 5 (or 0.5, or 500, etc.).

History

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Common logarithms are sometimes also called "Briggsian logarithms" after Henry Briggs, a 17th century British mathematician. In 1616 and 1617, Briggs visited John Napier at Edinburgh, the inventor of what are now called natural (base-e) logarithms, in order to suggest a change to Napier's logarithms. During these conferences, the alteration proposed by Briggs was agreed upon; and after his return from his second visit, he published the first chiliad of his logarithms.

Because base-10 logarithms were most useful for computations, engineers generally simply wrote "log(x)" when they meant log10(x). Mathematicians, on the other hand, wrote "log(x)" when they meant loge(x) for the natural logarithm. Today, both notations are found. Since hand-held electronic calculators are designed by engineers rather than mathematicians, it became customary that they follow engineers' notation. So the notation, according to which one writes "ln(x)" when the natural logarithm is intended, may have been further popularized by the very invention that made the use of "common logarithms" far less common, electronic calculators.

To mitigate the ambiguity, the ISO 80000 specification recommends that loge(x) should be ln(x), while log10(x) should be written lg(x), which unfortunately is used for the base-2 logarithm by CLRS and Sedgwick and The Chicago Manual of Style.[10][11][12]

Numeric value

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The logarithm keys (log for base-10 and ln for base-e) on a typical scientific calculator. The advent of hand-held calculators largely eliminated the use of common logarithms as an aid to computation.

The numerical value for logarithm to the base 10 can be calculated with the following identities:[5]

or or

using logarithms of any available base

as procedures exist for determining the numerical value for logarithm base e (see Natural logarithm § Efficient computation) and logarithm base 2 (see Algorithms for computing binary logarithms).

Derivative

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The derivative of a logarithm with a base b is such that[13]

, so .

See also

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Notes

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References

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Bibliography

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
The common logarithm, denoted as logx\log x for a positive xx, is the exponent to which the base 10 must be raised to produce xx, formally defined by the equation 10y=x10^y = x where y=logxy = \log x. This function serves as the inverse of the with base 10 and is distinguished from other logarithms by its omission of the base in notation, reflecting its conventional status in . The development of common logarithms traces back to the early 17th century, building on John Napier's 1614 invention of logarithms to simplify complex astronomical calculations involving multiplication and division of large numbers. English mathematician Henry Briggs refined Napier's system by adopting base 10, proposing that log1=0\log 1 = 0 and log10=1\log 10 = 1, and publishing extensive tables in his 1624 work Arithmetica Logarithmica to facilitate practical computations. These tables, later expanded by others like Adrian Vlacq, enabled the creation of slide rules by in 1622, which mechanized logarithmic operations and remained a staple tool in science and until electronic calculators supplanted them in the 1970s. Key properties of common logarithms include the log(xy)=logx+logy\log(xy) = \log x + \log y, the log(x/y)=logxlogy\log(x/y) = \log x - \log y, and the power rule log(xr)=rlogx\log(x^r) = r \log x, which mirror the laws of exponents and underpin their utility in algebraic manipulations. In applications, common logarithms are essential for compressing vast ranges of data, such as in the for earthquake magnitudes, the scale for sound intensity in , and pH measurements in chemistry, where they quantify exponential phenomena across orders of magnitude. They also appear in fields like for models and economics for calculations, highlighting their role in modeling real-world exponential processes.

Definition and Fundamentals

Definition

The common logarithm of a xx, denoted as logx\log x, is the exponent yy to which 10 must be raised to obtain xx, such that 10y=x10^y = x, where yy is a . The domain of the common logarithm consists of all (x>0x > 0), as the function is undefined for x0x \leq 0 since no yields a non-positive result when raising 10 to that power. Its range encompasses all , allowing logx\log x to take any value on the real line depending on xx. The common logarithm is the of the base 10, meaning that if y=logxy = \log x, then x=10yx = 10^y, often referred to as the antilogarithm. For example, log10=1\log 10 = 1 because 101=1010^1 = 10, and log100=2\log 100 = 2 because 102=10010^2 = 100.

Notation and Conventions

The common logarithm, defined as the logarithm with base 10, is denoted by logx\log x in numerous mathematical, , and physical contexts, where the base is conventionally understood to be 10 without explicit specification. This shorthand arises from its frequent use in applied fields, but to prevent confusion—particularly since pure mathematicians often reserve logx\log x for the natural logarithm (base ee)—the unambiguous form log10x\log_{10} x is recommended and widely employed in formal writing. Historically, logarithmic notation has shifted from earlier conventions, such as the capitalized "Log" introduced by in 1675 to represent the logarithm of yy, to the modern lowercase log\log popularized by Leonhard Euler in the mid-18th century as part of his treatment of logarithms as exponents of a base. Euler's notation in works like his 1748 established the subscript form logbx\log_b x for arbitrary bases, influencing contemporary standards. In practical tools, conventions vary by domain: scientific calculators, such as those from and commonly used in , default the "log" button to base 10, reflecting its utility in measurements like decibels and Richter scales. In contrast, many programming languages and mathematical software, including Python's math library and , treat "log" as the natural logarithm, necessitating "log10" or an equivalent for the common logarithm to maintain consistency with computational analysis. To resolve such ambiguities in international texts, especially in German and Russian literature, lgx\lg x serves as an alternative symbol exclusively for the base-10 logarithm, as endorsed by standards like ISO 80000. A prominent example of this notation in is the pH scale in chemistry, where pH=log[H+]\mathrm{pH} = -\log [\mathrm{H}^+] quantifies acidity using the base-10 logarithm of the concentration in moles per liter, enabling intuitive scaling across orders of magnitude.

Properties and Operations

Algebraic Properties

The algebraic properties of the common logarithm, denoted log10x\log_{10} x or simply logx\log x where the base 10 is implied, enable the simplification and manipulation of logarithmic expressions. These properties derive from the inverse relationship between logarithms and , where log10x=y\log_{10} x = y if and only if 10y=x10^y = x for x>0x > 0. They hold for all a,b>0a, b > 0 and are fundamental to algebraic operations involving common logs. The product rule states that log10(ab)=log10a+log10b\log_{10} (ab) = \log_{10} a + \log_{10} b. To see this, let log10a=c\log_{10} a = c and log10b=d\log_{10} b = d, so a=10ca = 10^c and b=10db = 10^d. Then ab=10c10d=10c+dab = 10^c \cdot 10^d = 10^{c+d}, which implies log10(ab)=c+d=log10a+log10b\log_{10} (ab) = c + d = \log_{10} a + \log_{10} b. For example, log(25)=log10=1\log (2 \cdot 5) = \log 10 = 1, and indeed log2+log5=1\log 2 + \log 5 = 1 since log10=1\log 10 = 1. The is log10(a/b)=log10alog10b\log_{10} (a/b) = \log_{10} a - \log_{10} b. This follows similarly: a/b=10c/10d=10cda/b = 10^c / 10^d = 10^{c-d}, so log10(a/b)=cd=log10alog10b\log_{10} (a/b) = c - d = \log_{10} a - \log_{10} b. The power rule provides log10(ab)=blog10a\log_{10} (a^b) = b \log_{10} a for real bb. Here, ab=(10c)b=10cba^b = (10^c)^b = 10^{c b}, yielding log10(ab)=cb=blog10a\log_{10} (a^b) = c b = b \log_{10} a. An illustration is log1000=log(103)=3log10=31=3\log 1000 = \log (10^3) = 3 \log 10 = 3 \cdot 1 = 3. Special cases include log101=0\log_{10} 1 = 0, since 100=110^0 = 1, and more generally log10(10k)=k\log_{10} (10^k) = k for any kk, as 10k=10k10^k = 10^k directly by . These properties collectively facilitate the transformation of products, quotients, and powers into sums, differences, and scalar multiples in logarithmic form.

Change of Base Formula

The change of base formula provides a method to express the common logarithm of a number in terms of logarithms using any other valid base. For any x>0x > 0 and base b>0b > 0, b1b \neq 1, it states that log10x=logbxlogb10.\log_{10} x = \frac{\log_b x}{\log_b 10}. This relation follows from the definition of logarithms as exponents and the consistency of logarithmic scales across bases. A frequent practical application involves converting to natural logarithms (base ee), yielding log10x=lnxln10.\log_{10} x = \frac{\ln x}{\ln 10}. This form is advantageous in analytical work and programming, as natural logarithms are often the default in mathematical libraries and allow seamless integration with exponential functions based on ee. The formula's primary significance is its role in facilitating computations of common logarithms by leveraging more accessible or efficient logarithmic evaluations, such as when base-10 tables are absent or when logs are preferred for their mathematical properties. It historically supported manual calculations before electronic tools and remains essential for verifying results across bases. For illustration, consider approximating log102\log_{10} 2: log102ln2ln100.6932.3030.301.\log_{10} 2 \approx \frac{\ln 2}{\ln 10} \approx \frac{0.693}{2.303} \approx 0.301. Here, ln20.693\ln 2 \approx 0.693 and ln102.303\ln 10 \approx 2.303, with the result aligning closely to the known value of log1020.301\log_{10} 2 \approx 0.301. In numerical implementations, however, the change of base formula can encounter stability issues for very large or very small xx, where floating-point representations of the logarithms may introduce errors that propagate through the division, potentially degrading accuracy.

Logarithmic Representation

Characteristic and Mantissa

In the context of common logarithms, which are base-10 logarithms, the value of log10x\log_{10} x for a positive x>0x > 0 is expressed as the sum of two parts: the characteristic, an nn, and the mantissa, a mm where 0m<10 \leq m < 1. Thus, log10x=n+m\log_{10} x = n + m, which equivalently means x=10n+m=10n×10mx = 10^{n+m} = 10^n \times 10^m. Here, 10m10^m represents the significand, a value between 1 and 10 that captures the scale-independent portion of xx. The characteristic nn is determined by taking the floor of log10x\log_{10} x, effectively indicating the order of magnitude of xx. For instance, when xx lies in the interval $[1, 10)$, the characteristic is 0, as the logarithm ranges from 0 to just under 1. This integer part simplifies the handling of large or small numbers by shifting the focus to the normalized significand. The mantissa mm provides the precise decimal adjustment to the characteristic, ensuring the full logarithmic value aligns with xx. Its significance lies in representing the relative precision of the number, independent of its magnitude. For example, consider x=250x = 250; this can be rewritten as 250=2.5×102250 = 2.5 \times 10^2, so log10250=log10(2.5×102)=2+log102.52+0.39794\log_{10} 250 = \log_{10} (2.5 \times 10^2) = 2 + \log_{10} 2.5 \approx 2 + 0.39794, where 2 is the characteristic and 0.39794 is the mantissa. This decomposition into characteristic and mantissa was essential for pre-digital computation, particularly in tools like slide rules, where the integer characteristic is managed through scale alignment and cursor positioning, while the mantissa is read directly from the logarithmic graduations for multiplication, division, and other operations.

Negative and Fractional Logarithms

For numbers xx where 0<x<10 < x < 1, the common logarithm log10x\log_{10} x is negative, reflecting that xx can be expressed as 1010 raised to a negative power. In this case, the logarithm takes the form log10x=n+m\log_{10} x = -n + m, where nn is a positive integer (the negative of the characteristic) and 0m<10 \leq m < 1 is the mantissa, such that x=10n10mx = 10^{-n} \cdot 10^m. The characteristic, which is the integer part of the logarithm, is negative, while the mantissa remains positive and represents the fractional part adjusted accordingly. This representation leverages the property that log10(1/x)=log10x\log_{10}(1/x) = -\log_{10} x for x>1x > 1, allowing computation of logarithms for reciprocals by negating the log of the greater-than-1 value. For instance, log100.50.3010\log_{10} 0.5 \approx -0.3010, which decomposes as a characteristic of 1-1 and a mantissa of 0.69900.6990, since 0.3010=1+0.6990-0.3010 = -1 + 0.6990. Similarly, log100.01=2\log_{10} 0.01 = -2 exactly, with characteristic 2-2 and mantissa 00, as 0.01=1020.01 = 10^{-2}. Another example is log100.3160.5\log_{10} 0.316 \approx -0.5, yielding characteristic 1-1 and mantissa 0.50.5. Fractional logarithms arise naturally in these cases, as the result is generally non-integer for irrational or non-power-of-10 values less than 1. These fractional parts facilitate accurate representation and computation, especially in pre-calculator eras using logarithmic tables that listed only positive mantissas. Notably, log100\log_{10} 0 is undefined, but as x0+x \to 0^+, log10x\log_{10} x \to -\infty, emphasizing the function's behavior near zero.

Historical Development

Origins and Early Concepts

The concept of logarithms has ancient roots in proportional scales and iterative calculations used by early civilizations to handle and astronomical computations. In ancient , around 2000–1600 BC, clay tablets containing tables of successive powers of numbers demonstrated an early understanding of , serving as a precursor to logarithmic thinking by facilitating multiplications through additions in related scales. Similarly, Greek mathematicians employed proportional methods; , in his third-century BC work , developed a system to enumerate extraordinarily by iteratively multiplying by 10 and higher powers, effectively using exponential notation to bound the grains of sand in the , which foreshadowed the inverse relationship central to logarithms. During the medieval period, Islamic scholars advanced these ideas through refined that relied on proportional . In the , Jamshīd al-Kāshī, working under , compiled highly precise sine tables in Zij-i Khāqānī, providing sine values to four places for each degree, accompanied by minute-by-minute differences for using proportional parts. These proportional parts allowed astronomers to estimate intermediate values linearly, a technique that highlighted the need for more efficient ways to perform repeated multiplications and divisions in , laying groundwork for logarithmic simplification. The formal invention of logarithms emerged in 1614 with John Napier's Mirifici logarithmorum canonis descriptio, where he introduced a function precursor to the natural logarithm to ease astronomical calculations. Motivated by the laborious multiplications required for trigonometric products in astronomy, Napier defined logarithms kinematically: the logarithm of a number decreases proportionally as the number itself decreases geometrically, such that the product of two numbers corresponds to the sum of their logarithms. This innovation stemmed from earlier formulas, which converted trigonometric multiplications into additions, but Napier's tables extended the approach to general arithmetic. A key insight in Napier's framework was the recognition that equal ratios between arguments produce equal differences in their logarithms, enabling the construction of arithmetic progressions from geometric sequences—a property that underscored logarithms' utility for computation before mechanical calculators. Invented primarily to expedite trigonometric and multiplicative operations in astronomy and , these early logarithms marked a pivotal shift toward systematic table-based arithmetic.

Standardization and Key Contributors

The standardization of the common logarithm, defined as the logarithm to base 10, began in the early through modifications to John Napier's original concept of logarithms introduced in 1614. In 1615, English mathematician , during his first visit to Napier in , proposed redefining the logarithm to base 10 such that log(1) = 0 and log(10) = 1, aligning it with the system for computational simplicity and eliminating the cumbersome large numbers in Napier's tables. This adjustment transformed Napier's logarithms into the more practical base-10 form, with log(1) = 0, facilitating easier arithmetic operations in scientific calculations. Briggs further refined this during subsequent visits in 1616, setting the stage for widespread adoption. Briggs published the first table of common logarithms in Logarithmorum Chilias Prima in 1617, providing values for integers from 1 to 1,000 to 14 decimal places, dedicated to his audience at . He expanded this work in Arithmetica Logarithmica in 1624, offering comprehensive tables for numbers from 1 to 20,000 and 90,000 to 100,000, also to 14 decimal places, along with logarithmic values for like sines and tangents. These publications marked the initial standardization of common logarithms, enabling their use in astronomy and , as Briggs demonstrated in chapters on ellipses and polygons. Building on ' foundation, Dutch Adriaan Vlacq extended the tables significantly in his 1628 Arithmetica Logarithmica, compiling logarithms for all integers from 1 to 100,000 to 10 decimal places, including the previously omitted range between 20,000 and 90,000. Assisted by Ezechiel de Decker, Vlacq's work filled critical gaps and incorporated trigonometric logarithms, making the tables more accessible for practical applications and serving as a reference for subsequent publications over centuries. By 1628, these efforts had produced the first complete set of common logarithm tables, standardizing their format and precision for broader scientific use. Further refinements came through integration with computational tools, notably by English mathematician and clergyman , who in the 1620s and 1630s developed the based on common logarithmic scales. Oughtred's invention around 1622 placed two logarithmic scales side by side on sliding rods, allowing direct and division via addition of logs, with his 1633 gauging rod applying this to volume calculations for barrels and taxation. This device, building on Napier's and ' logarithmic principles, popularized common logarithms among practitioners by the 1630s, enhancing their utility in everyday computations without requiring full table lookups. By the , common logarithms had become the standard in scientific texts and , largely due to their inherent alignment with the system, which mirrored the base-10 structure of emerging measurement standards like the proposed in the 1790s. This compatibility simplified logarithmic operations in fields requiring precise decimal handling, such as astronomy and , where tables were routinely included in works. The adoption solidified their role as the default logarithm in and , supplanting other bases for most non-specialized applications. A pivotal event underscoring the impact of standardized common logarithms was their application in verifying Kepler's third law of planetary motion and advancing navigation. Although Johannes Kepler initially used Napier's logarithms in his 1619 Harmonices Mundi to analyze orbital data and confirm the relation P2A3P^2 \propto A^3 (where PP is the period and AA the semi-major axis) via log-log plots, the subsequent availability of Briggs' and Vlacq's base-10 tables enabled more efficient computations in celestial mechanics. In navigation, common logarithms became indispensable from the 1620s onward, powering trigonometric calculations in almanacs and slide rules for determining positions at sea, as seen in works like Edmund Gunter's 1620 Canon Triangulorum, which tabulated base-10 logarithmic sines for mariners. This standardization transformed logarithms from a novel aid into an essential tool for empirical sciences.

Computation Methods

Traditional Table-Based Methods

Before the advent of electronic calculators, common logarithm tables were essential tools for performing arithmetic operations efficiently, particularly in fields like astronomy, navigation, and engineering. These tables, first systematically constructed by Henry Briggs in the early 17th century, listed the logarithms of numbers to facilitate computations by converting multiplication and division into addition and subtraction. Briggs' Arithmetica Logarithmica (1624) provided an initial table of common logarithms for numbers from 1 to 20,000 and 90,000 to 100,000, computed to 14 decimal places using manual iterative techniques. The construction of these tables relied on painstaking numerical methods without digital aids. Briggs employed the "continued means" approach, involving repeated extraction of square roots to iteratively approximate logarithms of key values, such as log(2) ≈ 0.3010, which required up to 54 iterations for high precision. Further entries were derived using difference methods and finite differences for between known points, allowing extension to larger ranges while minimizing redundant calculations; for instance, logarithms of primes were computed directly, then combined for composites. Later tables, such as those by Adriaan Vlacq in 1628, expanded to 10 decimals and covered up to 100,000 numbers, building on ' foundation through similar interpolation techniques. In practice, these tables were used to simplify operations by leveraging logarithmic properties. For multiplication of two numbers aa and bb, one locates log(a)\log(a) and log(b)\log(b) in the table, adds them to get log(ab)\log(ab), and then finds the antilogarithm (number whose log is the sum) to obtain the product; division follows by subtraction of logs. Powers and roots were handled by multiplying or dividing the log by the exponent or root index, respectively, before taking the antilog. Tables typically formatted the mantissa (fractional part) in columns for numbers from 1.000 to 9.999 (or scaled to 0.00 to 99.99), with 4 to 5 decimal places of precision, while the characteristic (integer part) was determined separately based on the number's magnitude—for example, numbers between 10 and 100 have a characteristic of 1. A representative example illustrates the process: to compute 23×4723 \times 47, find log(23)1.3617\log(23) \approx 1.3617 (characteristic 1, mantissa 0.3617) and log(47)1.6721\log(47) \approx 1.6721 (characteristic 1, mantissa 0.6721), add to get 3.03383.0338, and take the antilog of 3.0338 (which is 103×100.03381000×1.081=108110^3 \times 10^{0.0338} \approx 1000 \times 1.081 = 1081). This method reduced complex multiplications to table lookups and basic arithmetic. Despite their utility, traditional logarithm tables had inherent limitations. Interpolation between table entries introduced potential human error, as users manually estimated values for non-tabulated numbers, often leading to inaccuracies beyond 4-5 decimal places. Tables were generally restricted to 5-7 digits of overall precision due to printing constraints and computational labor, making them unsuitable for high-precision work without extensive manual adjustments.

Modern Numerical Algorithms

Modern numerical algorithms for computing the common logarithm, log10(x)\log_{10}(x), leverage efficient iterative techniques and hardware optimizations, often reducing the problem to evaluating the natural logarithm via the change of base formula log10(x)=ln(x)ln(10)\log_{10}(x) = \frac{\ln(x)}{\ln(10)}. For x>0x > 0, a preliminary step normalizes the input by determining the characteristic n=log10(x)n = \lfloor \log_{10}(x) \rfloor, expressing x=10nmx = 10^n \cdot m where 1m<101 \leq m < 10, so log10(x)=n+log10(m)\log_{10}(x) = n + \log_{10}(m). This reduces computation to the mantissa in [1,10)[1, 10). For example, with x=100x = 100, n=2n = 2 and m=1m = 1, yielding log10(100)=2+log10(1)=2\log_{10}(100) = 2 + \log_{10}(1) = 2. One foundational approach uses series expansions for the natural logarithm. The Taylor series for ln(1+u)\ln(1 + u) around u=0u = 0, where u<1|u| < 1, is ln(1+u)=uu22+u33u44+\ln(1 + u) = u - \frac{u^2}{2} + \frac{u^3}{3} - \frac{u^4}{4} + \cdots. To compute ln(x)\ln(x), rewrite x=(1+u)2kx = (1 + u) \cdot 2^k for suitable integer kk and u<1|u| < 1, then ln(x)=ln(1+u)+kln(2)\ln(x) = \ln(1 + u) + k \ln(2), with ln(2)\ln(2) precomputed. Dividing by the constant ln(10)\ln(10) gives log10(x)\log_{10}(x). This method converges with sufficient terms but requires careful range reduction for efficiency. Iterative root-finding methods, such as the bisection algorithm, approximate log10(x)\log_{10}(x) by solving 10yx=010^y - x = 0 for yy over an initial interval [a,b][a, b] containing the root, where f(a)f(b)<0f(a) \cdot f(b) < 0 and f(y)=10yxf(y) = 10^y - x. The algorithm repeatedly bisects the interval, selecting the subinterval where the function changes sign, converging linearly to the root with error halving each step. For instance, starting with [0,2][0, 2] for x=100x = 100, iterations narrow to y2y \approx 2. While simple and guaranteed to converge, bisection is slower than higher-order methods for high precision. The CORDIC (COordinate Rotation DIgital Computer) algorithm provides a hardware-efficient alternative, particularly for embedded systems and calculators. In vectoring mode with hyperbolic rotations, it computes ln(x)\ln(x) through iterative bit shifts and additions, avoiding multiplications. Extensions handle the base change to log10(x)\log_{10}(x). Introduced for trigonometric functions, CORDIC's logarithmic variant achieves fixed-point precision iteratively, making it ideal for low-power devices. In programming libraries like C++'s <cmath>, the log10() function implements high-precision computation using range reduction followed by minimax polynomials or the arithmetic-geometric mean (AGM). The AGM iterates arithmetic and geometric means starting from initial values related to xx, converging rapidly to values of elliptic integrals that can be used to compute ln(x)\ln(x) after appropriate transformation and range reduction, such as via relations involving ln(1+k1k)\ln\left(\frac{1+k}{1-k}\right) where kk is derived from xx. The result is then divided by ln(10)\ln(10) for the common logarithm. Implementations in libraries such as glibc or LLVM libc ensure IEEE 754 compliance, delivering results accurate to the full precision of the input type. Double-precision floating-point arithmetic, with 53-bit mantissa, provides approximately 15 decimal digits of precision.

Applications

In Mathematics and Science

In mathematics, common logarithms provide a fundamental tool for solving exponential equations involving base 10, such as determining the exponent xx in the equation 10x=5010^x = 50, which yields x=log10501.699x = \log_{10} 50 \approx 1.699. This application leverages the inverse relationship between exponential and logarithmic functions to isolate variables in equations that model growth, decay, or scaling processes. In chemistry, the pH scale quantifies the acidity or basicity of a solution using the formula pH=log10[H+]\mathrm{pH} = -\log_{10} [\mathrm{H}^+], where [H+][\mathrm{H}^+] represents the of hydrogen ions. For instance, a neutral solution with [H+]=107[\mathrm{H}^+] = 10^{-7} M has pH=7\mathrm{pH} = 7, since log10(107)=7\log_{10} (10^{-7}) = -7 and thus pH=(7)\mathrm{pH} = -(-7). The Richter scale, applied in to assess strength, defines magnitude as M=log10(A/A0)M = \log_{10} (A / A_0), where AA is the maximum of seismic waves and A0A_0 is a reference amplitude. In physics, common logarithms underpin the decibel scale for measuring , given by dB=10log10(I/I0)\mathrm{dB} = 10 \log_{10} (I / I_0), where II is the sound intensity and I0=1012I_0 = 10^{-12} W/m² is the reference threshold of human hearing. Stellar magnitudes in astronomy employ a to quantify brightness, where a difference of 5 magnitudes corresponds to a 100-fold change in brightness ratio, compressing vast ranges into a manageable numerical system. Logarithmic scales for astronomical distances, such as the distance modulus, further facilitate analysis by transforming exponential variations in flux with distance into linear relations. These applications highlight the advantage of common logarithms in compressing wide dynamic ranges—spanning orders of magnitude—into compact, interpretable scales that reveal patterns in natural phenomena otherwise obscured by linear representations.

In Engineering and Computing

In engineering, common logarithms are fundamental to , where they quantify gain and using decibels (dB). The voltage gain in dB is calculated as 20log10(VoutVin)20 \log_{10} \left( \frac{V_{\text{out}}}{V_{\text{in}}} \right), allowing engineers to handle wide dynamic ranges of signal amplitudes efficiently on ./10%3A_Decibels_and_Bode_Plots/10.2%3A_The_Decibel) This approach compresses exponential variations into linear increments, facilitating analysis of amplifiers and transmission lines. In , common logarithms underpin Bode plots, which plot magnitude response in dB against on a (log10f\log_{10} f), enabling visualization of frequency-dependent behavior across decades without distortion from linear scaling. In networking, common logarithms express signal-to-noise ratios (SNR) in dB as 10log10(PsignalPnoise)10 \log_{10} \left( \frac{P_{\text{signal}}}{P_{\text{noise}}} \right), providing a standardized metric for assessing communication quality and link budgets. Higher SNR values indicate clearer signals, with thresholds like 20 dB or more recommended for reliable data transmission in wireless systems. For instance, in audio engineering, a 3 dB increase corresponds approximately to a doubling of power, derived from 10log10(2)310 \log_{10}(2) \approx 3, which guides adjustments in sound systems for perceptual loudness. In , common logarithms aid in managing data compression ratios by scaling file sizes logarithmically, as seen in histograms where the x-axis uses log10\log_{10} () to visualize distributions spanning multiple orders of magnitude, from kilobytes to terabytes. This logarithmic representation highlights patterns in storage efficiency without skew from extreme values. Algorithmic complexity is occasionally analyzed in terms using log10n\log_{10} n, particularly when converting between binary and notations, though the base change is a constant factor in Big O analysis. The legacy of slide rules, which relied on common logarithm scales for analog and division, persists in niche tools like specialized nomograms for quick field calculations in civil and mechanical design. Modern computing leverages logarithmic number systems (LNS), where numbers are stored as their logarithms (typically base-2) to simplify multiplication into addition and enhance precision in digital signal processing hardware. In error analysis, log10\log_{10} quantifies relative errors in numerical computations, plotting them on logarithmic scales to assess propagation in iterative algorithms and validate accuracy across varying input magnitudes.

Mathematical Analysis

Derivative and Integral

The first derivative of the common logarithm function log10x\log_{10} x is given by ddxlog10x=1xln10,\frac{d}{dx} \log_{10} x = \frac{1}{x \ln 10}, where ln102.302585\ln 10 \approx 2.302585 is the natural logarithm of 10. This result follows from the change of base formula, log10x=lnxln10\log_{10} x = \frac{\ln x}{\ln 10}, combined with the chain rule and the known ddxlnx=1x\frac{d}{dx} \ln x = \frac{1}{x}, yielding 1ln101x\frac{1}{\ln 10} \cdot \frac{1}{x}. Higher-order derivatives can be found by successive differentiation. The second derivative is d2dx2log10x=1x2ln10.\frac{d^2}{dx^2} \log_{10} x = -\frac{1}{x^2 \ln 10}. In general, the nnth derivative for n1n \geq 1 takes the form dndxnlog10x=(1)n1(n1)!xnln10.\frac{d^n}{dx^n} \log_{10} x = \frac{(-1)^{n-1} (n-1)!}{x^n \ln 10}. This pattern arises because the first derivative is 1ln101x\frac{1}{\ln 10} \cdot \frac{1}{x}, and the nnth derivative of 1x\frac{1}{x} is (1)n1(n1)!xn(-1)^{n-1} (n-1)! x^{-n}, scaled by the constant 1ln10\frac{1}{\ln 10}. The indefinite integral of the common logarithm is log10xdx=xlog10xxln10+C.\int \log_{10} x \, dx = x \log_{10} x - \frac{x}{\ln 10} + C. This is obtained by integrating log10x=lnxln10\log_{10} x = \frac{\ln x}{\ln 10}, using the standard integral lnxdx=xlnxx+C\int \ln x \, dx = x \ln x - x + C and scaling by 1ln10\frac{1}{\ln 10}. As an example, the definite integral from 1 to 10 evaluates to 110log10xdx=[xlog10xxln10]110=109ln106.092.\int_1^{10} \log_{10} x \, dx = \left[ x \log_{10} x - \frac{x}{\ln 10} \right]_1^{10} = 10 - \frac{9}{\ln 10} \approx 6.092. By the , integrating the first derivative from 1 to 10 recovers the net change in the function: 1101xln10dx=ln10ln10=1=log1010log101\int_1^{10} \frac{1}{x \ln 10} \, dx = \frac{\ln 10}{\ln 10} = 1 = \log_{10} 10 - \log_{10} 1.

Series Expansions

The expansion for the common logarithm log10(x)\log_{10}(x) around x=1x = 1 is derived from the corresponding series for the natural logarithm via the change-of-base log10(x)=lnxln10\log_{10}(x) = \frac{\ln x}{\ln 10}. This adaptation of the , originally for the natural logarithm ln(1+u)=k=1(1)k+1ukk\ln(1 + u) = \sum_{k=1}^{\infty} (-1)^{k+1} \frac{u^k}{k} where u<1|u| < 1, yields log10(1+u)=1ln10k=1(1)k+1ukk,u<1.\log_{10}(1 + u) = \frac{1}{\ln 10} \sum_{k=1}^{\infty} (-1)^{k+1} \frac{u^k}{k}, \quad |u| < 1. The series converges within a radius of 1 centered at 1, providing a local approximation useful for analytical purposes and numerical evaluation near this point. For a general positive real number x>0x > 0, the common logarithm is expressed as log10(x)=n+log10(m)\log_{10}(x) = n + \log_{10}(m), where nn is the unique integer such that x=m×10nx = m \times 10^n with 1m<101 \leq m < 10. The then applies to the mantissa term log10(m)\log_{10}(m) when mm is sufficiently close to 1 (i.e., 0u<10 \leq u < 1 where m=1+um = 1 + u); for larger mm, alternative series representations, such as those involving m1m+1\frac{m-1}{m+1}, may be employed to ensure convergence. This decomposition facilitates the use of in broader computational contexts while respecting the convergence of 1 for the primary expansion around 1. As an illustrative example, consider log10(1.1)\log_{10}(1.1), where u=0.1u = 0.1. Using the first three terms of the series, ln(1.1)0.1(0.1)22+(0.1)33=0.10.005+0.000333=0.095333,\ln(1.1) \approx 0.1 - \frac{(0.1)^2}{2} + \frac{(0.1)^3}{3} = 0.1 - 0.005 + 0.000333\ldots = 0.095333\ldots, so log10(1.1)0.0953332.3025850.04139.\log_{10}(1.1) \approx \frac{0.095333\ldots}{2.302585\ldots} \approx 0.04139. This approximation matches the exact value to four decimal places.

References

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