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Mean Value Theorem For Divided Differences
In mathematical analysis, the mean value theorem for divided differences generalizes the mean value theorem to higher derivatives. Statement of the theorem For any ''n'' + 1 pairwise distinct points ''x''0, ..., ''x''''n'' in the domain of an ''n''-times differentiable function ''f'' there exists an interior point : \xi \in (\min\,\max\) \, where the ''n''th derivative of ''f'' equals ''n'' ! times the ''n''th divided difference at these points: : f _0,\dots,x_n= \frac. For ''n'' = 1, that is two function points, one obtains the simple mean value theorem. Proof Let P be the Lagrange interpolation polynomial for ''f'' at ''x''0, ..., ''x''''n''. Then it follows from the Newton form of P that the highest term of P is f _0,\dots,x_nx-x_)\dots(x-x_1)(x-x_0). Let g be the remainder of the interpolation, defined by g = f - P. Then g has n+1 zeros: ''x''0, ..., ''x''''n''. By applying Rolle's theorem first to g, then to g', a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mathematical Analysis
Analysis is the branch of mathematics dealing with continuous functions, limit (mathematics), limits, and related theories, such as Derivative, differentiation, Integral, integration, measure (mathematics), measure, infinite sequences, series (mathematics), series, and analytic functions. These theories are usually studied in the context of Real number, real and Complex number, complex numbers and Function (mathematics), functions. Analysis evolved from calculus, which involves the elementary concepts and techniques of analysis. Analysis may be distinguished from geometry; however, it can be applied to any Space (mathematics), space of mathematical objects that has a definition of nearness (a topological space) or specific distances between objects (a metric space). History Ancient Mathematical analysis formally developed in the 17th century during the Scientific Revolution, but many of its ideas can be traced back to earlier mathematicians. Early results in analysis were i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mean Value Theorem
In mathematics, the mean value theorem (or Lagrange theorem) states, roughly, that for a given planar arc between two endpoints, there is at least one point at which the tangent to the arc is parallel to the secant through its endpoints. It is one of the most important results in real analysis. This theorem is used to prove statements about a function on an interval starting from local hypotheses about derivatives at points of the interval. More precisely, the theorem states that if f is a continuous function on the closed interval , b/math> and differentiable on the open interval (a,b), then there exists a point c in (a,b) such that the tangent at c is parallel to the secant line through the endpoints \big(a, f(a)\big) and \big(b, f(b)\big), that is, : f'(c)=\frac. History A special case of this theorem for inverse interpolation of the sine was first described by Parameshvara (1380–1460), from the Kerala School of Astronomy and Mathematics in India, in his commentari ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Divided Difference
In mathematics, divided differences is an algorithm, historically used for computing tables of logarithms and trigonometric functions. Charles Babbage's difference engine, an early mechanical calculator, was designed to use this algorithm in its operation. Divided differences is a recursive division process. Given a sequence of data points (x_0, y_0),\ldots,(x_, y_), the method calculates the coefficients of the interpolation polynomial of these points in the Newton form. Definition Given ''n'' + 1 data points :(x_0, y_0),\ldots,(x_, y_) where the x_k are assumed to be pairwise distinct, the forward divided differences are defined as: : _k:= y_k, \qquad k \in \ : _k,\ldots,y_:= \frac, \qquad k\in\,\ j\in\. To make the recursive process of computation clearer, the divided differences can be put in tabular form, where the columns correspond to the value of ''j'' above, and each entry in the table is computed from the difference of the entries to its immediate lower ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lagrange Interpolation Polynomial
In numerical analysis, the Lagrange interpolating polynomial is the unique polynomial of lowest degree that interpolates a given set of data. Given a data set of coordinate pairs (x_j, y_j) with 0 \leq j \leq k, the x_j are called ''nodes'' and the y_j are called ''values''. The Lagrange polynomial L(x) has degree \leq k and assumes each value at the corresponding node, L(x_j) = y_j. Although named after Joseph-Louis Lagrange, who published it in 1795, the method was first discovered in 1779 by Edward Waring. It is also an easy consequence of a formula published in 1783 by Leonhard Euler. Uses of Lagrange polynomials include the Newton–Cotes method of numerical integration and Shamir's secret sharing scheme in cryptography. For equispaced nodes, Lagrange interpolation is susceptible to Runge's phenomenon of large oscillation. Definition Given a set of k + 1 nodes \, which must all be distinct, x_j \neq x_m for indices j \neq m, the Lagrange basis for polynomials of deg ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Newton Polynomial
In the mathematical field of numerical analysis, a Newton polynomial, named after its inventor Isaac Newton, is an polynomial interpolation, interpolation polynomial for a given set of data points. The Newton polynomial is sometimes called Newton's divided differences interpolation polynomial because the coefficients of the polynomial are calculated using Newton's divided differences method. Definition Given a set of ''k'' + 1 data points :(x_0, y_0),\ldots,(x_j, y_j),\ldots,(x_k, y_k) where no two ''x''''j'' are the same, the Newton interpolation polynomial is a linear combination of Newton basis polynomials :N(x) := \sum_^ a_ n_(x) with the Newton basis polynomials defined as :n_j(x) := \prod_^ (x - x_i) for ''j'' > 0 and n_0(x) \equiv 1. The coefficients are defined as :a_j := [y_0,\ldots,y_j] where :[y_0,\ldots,y_j] is the notation for divided differences. Thus the Newton polynomial can be written as :N(x) = [y_0] + [y_0,y_1](x-x_0) + \cdots + [y_0,\ldots,y ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rolle's Theorem
In calculus, Rolle's theorem or Rolle's lemma essentially states that any real-valued differentiable function that attains equal values at two distinct points must have at least one stationary point somewhere between them—that is, a point where the first derivative (the slope of the tangent line to the graph of the function) is zero. The theorem is named after Michel Rolle. Standard version of the theorem If a real-valued function is continuous on a proper closed interval , differentiable on the open interval , and , then there exists at least one in the open interval such that f'(c) = 0. This version of Rolle's theorem is used to prove the mean value theorem, of which Rolle's theorem is indeed a special case. It is also the basis for the proof of Taylor's theorem. History Although the theorem is named after Michel Rolle, Rolle's 1691 proof covered only the case of polynomial functions. His proof did not use the methods of differential calculus, which at that point ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stolarsky Mean
In mathematics, the Stolarsky mean is a generalization of the logarithmic mean. It was introduced by Kenneth B. Stolarsky in 1975. Definition For two positive real numbers ''x'', ''y'' the Stolarsky Mean is defined as: : \begin S_p(x,y) & = \lim_ \left(\right)^ \\0pt& = \begin x & \textx=y \\ \left(\right)^ & \text \end \end Derivation It is derived from the mean value theorem, which states that a secant line, cutting the graph of a differentiable function f at ( x, f(x) ) and ( y, f(y) ), has the same slope as a line tangent to the graph at some point \xi in the interval ,y/math>. : \exists \xi\in ,y f'(\xi) = \frac The Stolarsky mean is obtained by : \xi = f'^\left(\frac\right) when choosing f(x) = x^p. Special cases *\lim_ S_p(x,y) is the minimum. *S_(x,y) is the geometric mean. *\lim_ S_p(x,y) is the logarithmic mean. It can be obtained from the mean value theorem by choosing f(x) = \ln x. *S_(x,y) is the power mean with exponent \frac. *\lim_ S_p(x,y) is the i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
