Fermat's theorem (stationary points)
Fermat's theorem (stationary points) This article is about Fermat's theorem concerning the maximums and minimums of functions. For other theorems also named after Pierre de Fermat, see Fermat's theorem. This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Find sources: "Fermat's theorem" stationary points – news · newspapers · books · scholar · JSTOR (July 2019) (Learn how and when to remove this template message) In mathematics, Fermat's theorem (also known as interior extremum theorem) is a method to find local maxima and minima of differentiable functions on open sets by showing that every local extremum of the function is a stationary point (the function's derivative is zero at that point). Fermat's theorem is a theorem in real analysis, named after Pierre de Fermat.
By using Fermat's theorem, the potential extrema of a function {displaystyle displaystyle f} , with derivative {displaystyle displaystyle f'} , are found by solving an equation in {displaystyle displaystyle f'} . Fermat's theorem gives only a necessary condition for extreme function values, as some stationary points are inflection points (not a maximum or minimum). The function's second derivative, if it exists, can sometimes be used to determine whether a stationary point is a maximum or minimum.
Contents 1 Statement 1.1 Corollary 1.2 Extension 2 Applications 3 Intuitive argument 4 Proof 4.1 Proof 1: Non-vanishing derivatives implies not extremum 4.2 Proof 2: Extremum implies derivative vanishes 5 Cautions 5.1 Continuously differentiable functions 5.2 Pathological functions 6 See also 7 Notes 8 References 9 External links Statement One way to state Fermat's theorem is that, if a function has a local extremum at some point and is differentiable there, then the function's derivative at that point must be zero. In precise mathematical language: Let {displaystyle fcolon (a,b)rightarrow mathbb {R} } be a function and suppose that {displaystyle x_{0}in (a,b)} is a point where {displaystyle f} has a local extremum. If {displaystyle f} is differentiable at {displaystyle displaystyle x_{0}} , then {displaystyle f'(x_{0})=0} .
Another way to understand the theorem is via the contrapositive statement: if the derivative of a function at any point is not zero, then there is not a local extremum at that point. Formally: If {displaystyle f} is differentiable at {displaystyle x_{0}in (a,b)} , and {displaystyle f'(x_{0})neq 0} , then {displaystyle x_{0}} is not a local extremum of {displaystyle f} . Corollary The global extrema of a function f on a domain A occur only at boundaries, non-differentiable points, and stationary points. If {displaystyle x_{0}} is a global extremum of f, then one of the following is true: boundary: {displaystyle x_{0}} is in the boundary of A non-differentiable: f is not differentiable at {displaystyle x_{0}} stationary point: {displaystyle x_{0}} is a stationary point of f Extension In higher dimensions, exactly the same statement holds; however, the proof is slightly more complicated. The complication is that in 1 dimension, one can either move left or right from a point, while in higher dimensions, one can move in many directions. Thus, if the derivative does not vanish, one must argue that there is some direction in which the function increases – and thus in the opposite direction the function decreases. This is the only change to the proof or the analysis.
The statement can also be extended to differentiable manifolds. If {displaystyle f:Mto mathbb {R} } is a differentiable function on a manifold {displaystyle M} , then its local extrema must be critical points of {displaystyle f} , in particular points where the exterior derivative {displaystyle df} is zero.[1] Applications See also: Maxima and minima Fermat's theorem is central to the calculus method of determining maxima and minima: in one dimension, one can find extrema by simply computing the stationary points (by computing the zeros of the derivative), the non-differentiable points, and the boundary points, and then investigating this set to determine the extrema.
One can do this either by evaluating the function at each point and taking the maximum, or by analyzing the derivatives further, using the first derivative test, the second derivative test, or the higher-order derivative test.
Intuitive argument Intuitively, a differentiable function is approximated by its derivative – a differentiable function behaves infinitesimally like a linear function {displaystyle a+bx,} or more precisely, {displaystyle f(x_{0})+f'(x_{0})(x-x_{0}).} Thus, from the perspective that "if f is differentiable and has non-vanishing derivative at {displaystyle x_{0},} then it does not attain an extremum at {displaystyle x_{0},} " the intuition is that if the derivative at {displaystyle x_{0}} is positive, the function is increasing near {displaystyle x_{0},} while if the derivative is negative, the function is decreasing near {displaystyle x_{0}.} In both cases, it cannot attain a maximum or minimum, because its value is changing. It can only attain a maximum or minimum if it "stops" – if the derivative vanishes (or if it is not differentiable, or if one runs into the boundary and cannot continue). However, making "behaves like a linear function" precise requires careful analytic proof.
More precisely, the intuition can be stated as: if the derivative is positive, there is some point to the right of {displaystyle x_{0}} where f is greater, and some point to the left of {displaystyle x_{0}} where f is less, and thus f attains neither a maximum nor a minimum at {displaystyle x_{0}.} Conversely, if the derivative is negative, there is a point to the right which is lesser, and a point to the left which is greater. Stated this way, the proof is just translating this into equations and verifying "how much greater or less".
The intuition is based on the behavior of polynomial functions. Assume that function f has a maximum at x0, the reasoning being similar for a function minimum. If {displaystyle displaystyle x_{0}in (a,b)} is a local maximum then, roughly, there is a (possibly small) neighborhood of {displaystyle displaystyle x_{0}} such as the function "is increasing before" and "decreasing after"[note 1] {displaystyle displaystyle x_{0}} . As the derivative is positive for an increasing function and negative for a decreasing function, {displaystyle displaystyle f'} is positive before and negative after {displaystyle displaystyle x_{0}} . {displaystyle displaystyle f'} doesn't skip values (by Darboux's theorem), so it has to be zero at some point between the positive and negative values. The only point in the neighbourhood where it is possible to have {displaystyle displaystyle f'(x)=0} is {displaystyle displaystyle x_{0}} .
The theorem (and its proof below) is more general than the intuition in that it doesn't require the function to be differentiable over a neighbourhood around {displaystyle displaystyle x_{0}} . It is sufficient for the function to be differentiable only in the extreme point.
Proof Proof 1: Non-vanishing derivatives implies not extremum Suppose that f is differentiable at {displaystyle x_{0}in (a,b),} with derivative K, and assume without loss of generality that {displaystyle K>0,} so the tangent line at {displaystyle x_{0}} has positive slope (is increasing). Then there is a neighborhood of {displaystyle x_{0}} on which the secant lines through {displaystyle x_{0}} all have positive slope, and thus to the right of {displaystyle x_{0},} f is greater, and to the left of {displaystyle x_{0},} f is lesser.
The schematic of the proof is: an infinitesimal statement about derivative (tangent line) at {displaystyle x_{0}} implies a local statement about difference quotients (secant lines) near {displaystyle x_{0},} which implies a local statement about the value of f near {displaystyle x_{0}.} Formally, by the definition of derivative, {displaystyle f'(x_{0})=K} means that {displaystyle lim _{varepsilon to 0}{frac {f(x_{0}+varepsilon )-f(x_{0})}{varepsilon }}=K.} In particular, for sufficiently small {displaystyle varepsilon } (less than some {displaystyle varepsilon _{0}} ), the quotient must be at least {displaystyle K/2,} by the definition of limit. Thus on the interval {displaystyle (x_{0}-varepsilon _{0},x_{0}+varepsilon _{0})} one has: {displaystyle {frac {f(x_{0}+varepsilon )-f(x_{0})}{varepsilon }}>K/2;} one has replaced the equality in the limit (an infinitesimal statement) with an inequality on a neighborhood (a local statement). Thus, rearranging the equation, if {displaystyle varepsilon >0,} then: {displaystyle f(x_{0}+varepsilon )>f(x_{0})+(K/2)varepsilon >f(x_{0}),} so on the interval to the right, f is greater than {displaystyle f(x_{0}),} and if {displaystyle varepsilon <0,} then: {displaystyle f(x_{0}+varepsilon )
If {displaystyle f'(x_{0})=K>0} and {displaystyle fin C^{1},} then by continuity of the derivative, there is some {displaystyle varepsilon _{0}>0} such that {displaystyle f'(x)>K/2} for all {displaystyle xin (x_{0}-varepsilon _{0},x_{0}+varepsilon _{0})} . Then f is increasing on this interval, by the mean value theorem: the slope of any secant line is at least {displaystyle K/2,} as it equals the slope of some tangent line.
However, in the general statement of Fermat's theorem, where one is only given that the derivative at {displaystyle x_{0}} is positive, one can only conclude that secant lines through {displaystyle x_{0}} will have positive slope, for secant lines between {displaystyle x_{0}} and near enough points.
Conversely, if the derivative of f at a point is zero ( {displaystyle x_{0}} is a stationary point), one cannot in general conclude anything about the local behavior of f – it may increase to one side and decrease to the other (as in {displaystyle x^{3}} ), increase to both sides (as in {displaystyle x^{4}} ), decrease to both sides (as in {displaystyle -x^{4}} ), or behave in more complicated ways, such as oscillating (as in {displaystyle x^{2}sin(1/x)} , as discussed below).
One can analyze the infinitesimal behavior via the second derivative test and higher-order derivative test, if the function is differentiable enough, and if the first non-vanishing derivative at {displaystyle x_{0}} is a continuous function, one can then conclude local behavior (i.e., if {displaystyle f^{(k)}(x_{0})neq 0} is the first non-vanishing derivative, and {displaystyle f^{(k)}} is continuous, so {displaystyle fin C^{k}} ), then one can treat f as locally close to a polynomial of degree k, since it behaves approximately as {displaystyle f^{(k)}(x_{0})(x-x_{0})^{k},} but if the k-th derivative is not continuous, one cannot draw such conclusions, and it may behave rather differently.
Pathological functions The function {displaystyle sin(1/x)} – it oscillates increasingly rapidly between {displaystyle -1} and {displaystyle 1} as x approaches 0. Consequently, the function {displaystyle f(x)=(1+sin(1/x))x^{2}} oscillates increasingly rapidly between 0 and {displaystyle 2x^{2}} as x approaches 0. If one extends this function by defining {displaystyle f(0)=0} then the extended function is continuous and everywhere differentiable (it is differentiable at 0 with derivative 0), but has rather unexpected behavior near 0: in any neighborhood of 0 it attains 0 infinitely many times, but also equals {displaystyle 2x^{2}} (a positive number) infinitely often.
Continuing in this vein, one may define {displaystyle g(x)=(2+sin(1/x))x^{2}} , which oscillates between {displaystyle x^{2}} and {displaystyle 3x^{2}} . The function has its local and global minimum at {displaystyle x=0} , but on no neighborhood of 0 is it decreasing down to or increasing up from 0 – it oscillates wildly near 0.
This pathology can be understood because, while the function g is everywhere differentiable, it is not continuously differentiable: the limit of {displaystyle g'(x)} as {displaystyle xto 0} does not exist, so the derivative is not continuous at 0. This reflects the oscillation between increasing and decreasing values as it approaches 0.
See also Optimization (mathematics) Maxima and minima Derivative Extreme value arg max Adequality Notes ^ This intuition is only correct for continuously differentiable {displaystyle left(C^{1}right)} functions, while in general it is not literally correct—a function need not be increasing up to a local maximum: it may instead be oscillating, so neither increasing nor decreasing, but simply the local maximum is greater than any values in a small neighborhood to the left or right of it. See details in the pathologies. References ^ "Is Fermat's theorem about local extrema true for smooth manifolds?". Stack Exchange. August 11, 2015. Retrieved 21 April 2017. External links "Fermat's Theorem (stationary points)". PlanetMath. "Proof of Fermat's Theorem (stationary points)". PlanetMath.
hide vte Pierre de Fermat Work Fermat's Last TheoremFermat numberFermat's principleFermat's little theoremFermat polygonal number theoremFermat pseudoprimeFermat pointFermat's theorem (stationary points)Fermat's theorem on sums of two squaresFermat's spiralFermat's right triangle theorem Related List of things named after Pierre de FermatWiles's proof of Fermat's Last TheoremFermat's Last Theorem in fictionFermat PrizeFermat's Last Tango (2000 musical)Fermat's Last Theorem (popular science book) Categories: Theorems in real analysisDifferential calculusTheorems in calculus
Si quieres conocer otros artículos parecidos a Fermat's theorem (stationary points) puedes visitar la categoría Differential calculus.
Deja una respuesta