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[[File:Tangent to a curve.svg|thumb|200px|width=150|length=150|The graph of a function, drawn in black, and a tangent line to that function, drawn in red. The slope of the tangent line equals the derivative of the function at the marked point.]]
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{{Calculus}}
 
In [[mathematics]], '''differential calculus''' is a subfield of [[calculus]] concerned with the study of the rates at which quantities change. It is one of the two traditional divisions of calculus, the other being [[integral calculus]].
 
The primary objects of study in differential calculus are the [[derivative]] of a [[Function (mathematics)|function]], related notions such as the [[Differential of a function|differential]], and their applications. The derivative of a function at a chosen input value describes the rate of change of the function near that input value. The process of finding a derivative is called '''differentiation'''. Geometrically, the derivative at a point is the [[slope]] of the [[tangent#Geometry|tangent line]] to the [[graph of a function|graph of the function]] at that point, provided that the derivative exists and is defined at that point. For a [[real-valued function]] of a single real variable, the derivative of a function at a point generally determines the best [[linear approximation]] to the function at that point.  
 
Differential calculus and integral calculus are connected by the [[fundamental theorem of calculus]], which states that differentiation is the reverse process to [[integration (mathematics)|integration]].
 
Differentiation has applications to nearly all quantitative disciplines. For example, in [[physics]], the derivative of the [[displacement (vector)|displacement]] of a moving body with respect to [[time]] is the [[velocity]] of the body, and the derivative of velocity with respect to [[time]] is acceleration. [[Newton's laws of motion|Newton's second law of motion]] states that the derivative of the [[momentum]] of a body equals the force applied to the body. The [[reaction rate]] of a [[chemical reaction]] is a derivative. In [[operations research]], derivatives determine the most efficient ways to transport materials and design factories.  
 
Derivatives are frequently used to find the [[maxima and minima]] of a function. Equations involving derivatives are called [[differential equations]] and are fundamental in describing [[Natural phenomenon|natural phenomena]]. Derivatives and their generalizations appear in many fields of mathematics, such as [[complex analysis]], [[functional analysis]], [[differential geometry]], [[measure theory]] and [[abstract algebra]].
 
== The derivative ==
{{Main|Derivative}}
 
Suppose that ''x'' and ''y'' are [[real number]]s and that ''y'' is a [[Function (mathematics)|function]] of ''x'', that is, for every value of ''x'', there is a corresponding value of ''y''. This relationship can be written as ''y'' = ''f''(''x''). If ''f''(''x'') is the equation for a straight line, then there are two real numbers ''m'' and ''b'' such that {{nobreak|''y'' {{=}} ''m'' ''x'' + ''b''}}. ''m'' is called the [[slope]] and can be determined from the formula:
:<math>m=\frac{\text{change in } y}{\text{change in } x} = \frac{\Delta y}{\Delta x},</math>
where the symbol Δ (the uppercase form of the [[Greek alphabet|Greek]] letter [[Delta (letter)|Delta]]) is an abbreviation for "change in". It follows that {{nobreak|Δ''y'' {{=}} ''m'' Δ''x''}}.
 
A general function is not a line, so it does not have a slope. The '''derivative''' of ''f'' at the point ''x'' is the slope of the linear approximation to ''f'' at the point ''x''. It is usually denoted {{nowrap|''f'' &prime;(''x'')}} or ''dy''/''dx''. Together with the value of ''f'' at ''x'', the derivative of ''f'' determines the best linear approximation, or [[linearization]], of ''f'' near the point ''x''. This latter property is usually taken as the definition of the derivative.
 
A closely related notion is the [[differential (calculus)|differential]] of a function.
 
[[Image:Tangent-calculus.svg|thumb|300px|The [[tangent line]] at (''x'',''f''(''x''))]]
When ''x'' and ''y'' are real variables, the derivative of ''f'' at ''x'' is the slope of the [[tangent line]] to the graph of ''f'' at ''x''.  Because the source and target of ''f'' are one-dimensional, the derivative of ''f'' is a real number.  If ''x'' and ''y'' are vectors, then the best linear approximation to the graph of ''f'' depends on how ''f'' changes in several directions at once.  Taking the best linear approximation in a single direction determines a '''[[partial derivative]]''', which is usually denoted ∂''y''/∂''x''.  The linearization of ''f'' in all directions at once is called the '''[[total derivative]]'''.
 
== History of differentiation ==
{{Main|History of calculus}}
 
The concept of a derivative in the sense of a [[tangent line]] is a very old one, familiar to [[Ancient Greece|Greek]] geometers such as
[[Euclid]] (c. 300 BC), [[Archimedes]] (c. 287–212 BC) and [[Apollonius of Perga]] (c. 262–190 BC).<ref>See [[Euclid's Elements]], The [[Archimedes Palimpsest]] and {{MacTutor Biography|id=Apollonius|title=Apollonius of Perga}}</ref> [[Archimedes]] also introduced the use of [[infinitesimal]]s, although these were primarily used to study areas and volumes rather than derivatives and tangents; see [[Archimedes' use of infinitesimals]].
 
The use of infinitesimals to study rates of change can be found in [[Indian mathematics]], perhaps as early as 500 AD, when the astronomer and mathematician [[Aryabhata]] (476–550) used infinitesimals to study the [[Orbit of the Moon|motion of the moon]].<ref>{{MacTutor Biography|id=Aryabhata_I|title=Aryabhata the Elder}}</ref> The use of infinitesimals to compute rates of change was developed significantly by [[Bhāskara II]] (1114–1185); indeed, it has been argued<ref>Ian G. Pearce. [http://turnbull.mcs.st-and.ac.uk/~history/Projects/Pearce/Chapters/Ch8_5.html Bhaskaracharya II.]</ref> that many of the key notions of differential calculus can be found in his work, such as "[[Rolle's theorem]]".<ref>{{Cite journal|first=T. A. A.|last=Broadbent|title=Reviewed work(s): ''The History of Ancient Indian Mathematics'' by C. N. Srinivasiengar|journal=The Mathematical Gazette|volume=52|issue=381|date=October 1968|pages=307–8|doi=10.2307/3614212|jstor=3614212|last2=Kline|first2=M.|postscript=<!-- Bot inserted parameter. Either remove it; or change its value to "." for the cite to end in a ".", as necessary. -->{{inconsistent citations}}}}</ref> The [[Islamic mathematics|Persian mathematician]], [[Sharaf al-Dīn al-Tūsī]] (1135–1213), was the first to discover the [[derivative]] of [[Cubic function|cubic polynomials]], an important result in differential calculus;<ref>J. L. Berggren (1990). "Innovation and Tradition in Sharaf al-Din al-Tusi's Muadalat", ''Journal of the American Oriental Society'' '''110''' (2), p. 304-309.</ref> his ''Treatise on Equations'' developed concepts related to differential calculus, such as the derivative [[Function (mathematics)|function]] and the [[maxima and minima]] of curves, in order to solve [[cubic equation]]s which may not have positive solutions.<ref name=Sharaf>{{MacTutor|id=Al-Tusi_Sharaf|title=Sharaf al-Din al-Muzaffar al-Tusi}}</ref>
 
The modern development of calculus is usually credited to [[Isaac Newton]] (1643–1727) and [[Gottfried Leibniz]] (1646–1716), who provided independent<ref>Newton began his work in 1666 and Leibniz began his in 1676. However, Leibniz published his first paper in 1684, predating Newton's publication in 1693. It is possible that Leibniz saw drafts of Newton's work in 1673 or 1676, or that Newton made use of Leibniz's work to refine his own. Both Newton and Leibniz claimed that the other plagiarized their respective works. This resulted in a bitter [[Newton v. Leibniz calculus controversy|controversy]] between the two men over who first invented calculus which shook the mathematical community in the early 18th century.</ref> and unified approaches to differentiation and derivatives. The key insight, however, that earned them this credit, was the [[fundamental theorem of calculus]] relating differentiation and integration: this rendered obsolete most previous methods for computing areas and volumes,<ref>This was a monumental achievement, even though a restricted version had been proven previously by [[James Gregory (astronomer and mathematician)|James Gregory]] (1638–1675), and some key examples can be found in the work of [[Pierre de Fermat]] (1601–1665).</ref> which had not been significantly extended since the time of [[Ibn al-Haytham]] (Alhazen).<ref name=Katz>Victor J. Katz (1995), "Ideas of Calculus in Islam and India", ''Mathematics Magazine'' '''68''' (3): 163-174 [165-9 & 173-4]</ref> For their ideas on derivatives, both Newton and Leibniz built on significant earlier work by mathematicians such as [[Isaac Barrow]] (1630–1677), [[René Descartes]] (1596–1650), [[Christiaan Huygens]] (1629–1695), [[Blaise Pascal]] (1623–1662) and [[John Wallis]] (1616–1703). Isaac Barrow is generally given credit for the early development of the derivative.<ref>Eves, H. (1990).</ref> Nevertheless, Newton and Leibniz remain key figures in the history of differentiation, not least because Newton was the first to apply differentiation to [[theoretical physics]], while Leibniz systematically developed much of the notation still used today.
 
Since the 17th century many mathematicians have contributed to the theory of differentiation. In the 19th century, calculus was put on a much more rigorous footing by mathematicians such as [[Augustin Louis Cauchy]] (1789–1857), [[Bernhard Riemann]] (1826–1866), and [[Karl Weierstrass]] (1815–1897). It was also during this period that the differentiation was generalized to [[Euclidean space]] and the [[complex plane]].
 
== Applications of derivatives ==
=== Optimization ===
If ''f'' is a [[differentiable function]] on '''R''' (or an [[open interval]]) and ''x'' is a [[local maximum]] or a [[local minimum]] of ''f'', then the derivative of ''f'' at ''x'' is zero; points where {{nobreak|''f<nowiki>'</nowiki>''(''x'') {{=}} 0}} are called ''[[critical point (mathematics)|critical points]]'' or ''[[stationary point]]s'' (and the value of ''f'' at ''x'' is called a ''[[critical value]]''). (The definition of a critical point is sometimes extended to include points where the derivative does not exist.) Conversely, a critical point ''x'' of ''f'' can be analysed by considering the [[second derivative]] of ''f'' at ''x'':
* if it is positive, ''x'' is a local minimum;
* if it is negative, ''x'' is a local maximum;
* if it is zero, then ''x'' could be a local minimum, a local maximum, or neither. (For example, {{nobreak|''f''(''x'') {{=}} ''x''<sup>3</sup>}} has a critical point at {{nobreak|''x'' {{=}} 0}}, but it has neither a maximum nor a minimum there, whereas {{nobreak|''f''(''x'') {{=}} ±''x''<sup>4</sup>}} has a critical point at {{nobreak|''x'' {{=}} 0}} and a minimum and a maximum, respectively, there.)
This is called the [[second derivative test]]. An alternative approach, called the [[first derivative test]], involves considering the sign of the ''f<nowiki>'</nowiki>'' on each side of the critical point.
 
Taking derivatives and solving for critical points is therefore often a simple way to find local minima or maxima, which can be useful in [[Optimization (mathematics)|optimization]]. By the [[extreme value theorem]], a continuous function on a [[closed interval]] must attain its minimum and maximum values at least once. If the function is differentiable, the minima and maxima can only occur at critical points or endpoints.
 
This also has applications in graph sketching: once the local minima and maxima of a differentiable function have been found, a rough plot of the graph can be obtained from the observation that it will be either increasing or decreasing between critical points.
 
In higher dimensions, a critical point of a scalar valued function is a point at which the gradient is zero. The second derivative test can still be used to analyse critical points by considering the [[eigenvalue]]s of the [[Hessian matrix]] of second partial derivatives of the function at the critical point. If all of the eigenvalues are positive, then the point is a local minimum; if all are negative, it is a local maximum.  If there are some positive and some negative eigenvalues, then the critical point is a [[saddle point]], and if none of these cases hold (i.e., some of the eigenvalues are zero) then the test is inconclusive.
 
==== Calculus of variations ====
{{Main|Calculus of variations}}
 
One example of an optimization problem is: Find the shortest curve between two points on a surface, assuming that the curve must also lie on the surface. If the surface is a plane, then the shortest curve is a line. But if the surface is, for example, egg-shaped, then the [[Shortest path problem|shortest path]] is not immediately clear. These paths are called [[geodesic]]s, and one of the simplest problems in the calculus of variations is finding geodesics. Another example is: Find the smallest area surface filling in a closed curve in space. This surface is called a [[minimal surface]] and it, too, can be found using the calculus of variations.
 
=== Physics ===
Calculus is of vital importance in physics: many physical processes are described by equations involving derivatives, called [[differential equation]]s. Physics is particularly concerned with the way quantities change and evolve over time, and the concept of the "'''[[time derivative]]'''" &mdash; the rate of change over time &mdash; is essential for the precise definition of several important concepts. In particular, the time derivatives of an object's position are significant in [[Newtonian physics]]:
 
* [[velocity]] is the derivative (with respect to time) of an object's displacement (distance from the original position)
* [[acceleration]] is the derivative (with respect to time) of an object's velocity, that is, the second derivative (with respect to time) of an object's position.
 
For example, if an object's position on a line is given by
 
: <math>x(t) = -16t^2 + 16t + 32 , \,\!</math>
 
then the object's velocity is
 
: <math>\dot x(t) = x'(t) = -32t + 16, \,\!</math>
 
and the object's acceleration is
 
: <math>\ddot x(t) = x''(t) = -32, \,\!</math>
 
which is constant.
 
=== Differential equations ===
{{Main|Differential equation}}
 
A '''differential equation''' is a relation between a collection of functions and their derivatives.  An '''ordinary differential equation''' is a differential equation that relates functions of one variable to their derivatives with respect to that variable.  A '''partial differential equation''' is a differential equation that relates functions of more than one variable to their partial derivatives.  Differential equations arise naturally in the physical sciences, in mathematical modelling, and within mathematics itself.  For example, [[Newton's second law]], which describes the relationship between acceleration and force, can be stated as the ordinary differential equation
:<math>F(t) = m\frac{d^2x}{dt^2}.</math>
The [[heat equation]] in one space variable, which describes how heat diffuses through a straight rod, is the partial differential equation
:<math>\frac{\partial u}{\partial t} = \alpha\frac{\partial^2 u}{\partial x^2}.</math>
Here ''u''(''x'',''t'') is the temperature of the rod at position ''x'' and time ''t'' and α is a constant that depends on how fast heat diffuses through the rod.
 
=== Mean value theorem ===
{{Main|Mean value theorem}}
 
The mean value theorem gives a relationship between values of the derivative and values of the original function.  If ''f''(''x'') is a real-valued function and ''a'' and ''b'' are numbers with {{nobreak|''a'' < ''b''}}, then the mean value theorem says that under mild hypotheses, the slope between the two points (''a'',''f''(''a'')) and (''b'',''f''(''b'')) is equal to the slope of the tangent line to ''f'' at some point ''c'' between ''a'' and ''b''.  In other words,
:<math>f'(c) = \frac{f(b) - f(a)}{b - a}.</math>
In practice, what the mean value theorem does is control a function in terms of its derivative.  For instance, suppose that ''f'' has derivative equal to zero at each point.  This means that its tangent line is horizontal at every point, so the function should also be horizontal.  The mean value theorem proves that this must be true: The slope between any two points on the graph of ''f'' must equal the slope of one of the tangent lines of ''f''.  All of those slopes are zero, so any line from one point on the graph to another point will also have slope zero.  But that says that the function does not move up or down, so it must be a horizontal line.  More complicated conditions on the derivative lead to less precise but still highly useful information about the original function.
 
=== Taylor polynomials and Taylor series ===
{{Main|Taylor polynomial|Taylor series}}
 
The derivative gives the best possible linear approximation, but this can be very different from the original function.  One way of improving the approximation is to take a quadratic approximation.  That is to say, the linearization of a real-valued function ''f''(''x'') at the point ''x''<sub>0</sub> is a linear polynomial {{nobreak|''a'' + ''b''(''x'' − ''x''<sub>0</sub>)}}, and it may be possible to get a better approximation by considering a quadratic polynomial {{nobreak|''a'' + ''b''(''x'' − ''x''<sub>0</sub>) + ''c''(''x'' − ''x''<sub>0</sub>)<sup>2</sup>}}.  Still better might be a cubic polynomial {{nobreak|''a'' + ''b''(''x'' − ''x''<sub>0</sub>) + ''c''(''x'' − ''x''<sub>0</sub>)<sup>2</sup> + ''d''(''x'' − ''x''<sub>0</sub>)<sup>3</sup>}}, and this idea can be extended to arbitrarily high degree polynomials.  For each one of these polynomials, there should be a best possible choice of coefficients ''a'', ''b'', ''c'', and ''d'' that makes the approximation as good as possible.
 
In the [[Neighbourhood (mathematics)|neighbourhood]] of ''x''<sub>0</sub>, for ''a'' the best possible choice is always ''f''(''x''<sub>0</sub>), and for ''b'' the best possible choice is always ''f<nowiki>'</nowiki>''(''x''<sub>0</sub>).  For ''c'', ''d'', and higher-degree coefficients, these coefficients are determined by higher derivatives of ''f''.  ''c'' should always be ''f<nowiki>''</nowiki>''(''x''<sub>0</sub>)/2, and ''d'' should always be ''f<nowiki>'''</nowiki>''(''x''<sub>0</sub>)/3[[factorial|!]].  Using these coefficients gives the '''Taylor polynomial''' of ''f''.  The Taylor polynomial of degree ''d'' is the polynomial of degree ''d'' which best approximates ''f'', and its coefficients can be found by a generalization of the above formulas.  [[Taylor's theorem]] gives a precise bound on how good the approximation is.  If ''f'' is a polynomial of degree less than or equal to ''d'', then the Taylor polynomial of degree ''d'' equals ''f''.
 
The limit of the Taylor polynomials is an infinite series called the '''Taylor series'''.  The Taylor series is frequently a very good approximation to the original function.  Functions which are equal to their Taylor series are called [[analytic function]]s.  It is impossible for functions with discontinuities or sharp corners to be analytic, but there are [[smooth function]]s which are not analytic.
 
=== Implicit function theorem ===
{{Main|Implicit function theorem}}
 
Some natural geometric shapes, such as [[circle]]s, cannot be drawn as the [[graph of a function]].  For instance, if {{nobreak|''f''(''x'',''y'') {{=}} ''x''<sup>2</sup> + ''y''<sup>2</sup> − 1}}, then the circle is the set of all pairs (''x'',''y'') such that {{nobreak|''f''(''x'',''y'') {{=}} 0}}.  This set is called the zero set of ''f''.  It is not the same as the graph of ''f'', which is a [[cone (geometry)|cone]].  The implicit function theorem converts relations such as {{nobreak|''f''(''x'',''y'') {{=}} 0}} into functions.  It states that if ''f'' is [[continuously differentiable]], then around most points, the zero set of ''f'' looks like graphs of functions pasted together.  The points where this is not true are determined by a condition on the derivative of ''f''.  The circle, for instance, can be pasted together from the graphs of the two functions <math>\pm\sqrt{1-x^2}</math>.  In a neighborhood of every point on the circle except (−1,0) and (1,0), one of these two functions has a graph that looks like the circle.  (These two functions also happen to meet (−1,0) and (1,0), but this is not guaranteed by the implicit function theorem.)
 
The implicit function theorem is closely related to the [[inverse function theorem]], which states when a function looks like graphs of [[invertible function]]s pasted together.
 
== See also ==
{{sisterlinks|Differential calculus}}
* [[Differential (calculus)]]
* [[Differential geometry]]
* [[Numerical differentiation]]
* [[Techniques for differentiation]]
* [[List of calculus topics]]
 
== References ==
{{Reflist}}
 
*{{cite book | author=J. Edwards | title=Differential Calculus
| publisher= MacMillan and Co.| location=London | year=1892
|url=http://books.google.com/books?id=unltAAAAMAAJ&pg=PA1#v=onepage&q&f=false}}
 
[[Category:Differential calculus| ]]
 
{{Link FA|de}}
{{Link FA|lmo}}
{{Link FA|mk}}

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Jap Marine carries a full line of nylon bias ply trailer tires. These 'Particular Trailer' (ST) tires have been constructed for higher high velocity sturdiness and bruise resistance underneath heavy loads. Trailer tire building varies considerably from automotive tires, due to this fact it is important to decide on the correct tire for your towing application. Typically, trailer tires have the identical load vary (or ply) from bead to bead and are bias ply construction. This enables for a stiffer aspect wall which offers safer towing by helping to scale back trailer sway issues. This might result in increased trailer sway and loss of management.

Harry W. Millis, an impartial tire-industry analyst in Cleveland, mentioned the tires would be handiest in tracking tires within trucking corporations' warehouses and for warranty purposes. "The query now's how much information there will likely be on the chip and the way the data shall be read," he mentioned. "However for now, evidently the potential is gigantic. It may very well be a terrific boon to tire managers in controlling value." The new tires developed by Goodyear even have strong potential for the passenger-car tire market, some business analysts suggest. By enabling car house owners to do a greater job of monitoring the inflation level of the tire and other efficiency data, a automobile owner can extend the life of a passenger automobile tire by 10 % as well.

Considering of taking your SUV or Pickup truck via the woods? You will not get far without the proper of tires. Off Street tires had been specifically designed for one thing and one thing only. That factor is off highway driving. Yes they made a tire specifically for driving within the grime. read extra Mickey Thompson Tires Get As much as 10% Off Mickey Thompson tires infuse passion for creating the proper truck tires into their line of premium off road tires, renowned for reliability, high quality, and toughness. Dick Cepek tires are synonymous with the off-highway industry because of their history of aggressive truck tires that by no means fail to ship very good efficiency and quality. Tremendous Swamper Vampire ATV Tires Free Transport on Set of four Baja Claw TTC Tires by Mickey Thompson

days in the past Mavis Discount Tire, Fishkill days ago Mavis Discount Tire, Greenwich days in the past Mavis Low cost Tire, Franklin Sq. (Hemp tpk) days in the past Mavis Low cost Tire, Pleasantville Only 2000 miles but these tires far exceeded my expectations. They're quiet, they deal with very nicely and I'm very completely satisfied. They are wider than the inventory 20 inch tires and shorter however deal with a lot better than the originals. The 255/50's are cheaper then the stick measurement but really feel so much better." - YORKTOWN HEIGHTS, NY days in the past Mavis Low cost Tire, Massapequa Park days in the past Mavis Low cost Tire, Franklin Square (Franklin ave) days ago Mavis Discount Tire, Cicero Thus far so good. Excellent tires for the value. A very glad buyer." - YONKERS, NY days in the past Mavis Discount Tire, Webster Tire put on and Aging