In mathematics, the derivative is a fundamental construction of differential calculus and admits many possible generalizations within the fields of mathematical analysis, combinatorics, algebra, geometry, etc.
The Fréchet derivative defines the derivative for general normed vector spaces . Briefly, a function , an open subset of , is called Fréchet differentiable at if there exists a bounded linear operator such that
Functions are defined as being differentiable in some open neighbourhood of , rather than at individual points, as not doing so tends to lead to many pathological counterexamples.
The Fréchet derivative is quite similar to the formula for the derivative found in elementary one-variable calculus, and simply moves A to the left hand side. However, the Fréchet derivative A denotes the function .
In multivariable calculus, in the context of differential equations defined by a vector valued function Rn to Rm, the Fréchet derivative A is a linear operator on R considered as a vector space over itself, and corresponds to the best linear approximation of a function. If such an operator exists, then it is unique, and can be represented by an m by n matrix known as the Jacobian matrix Jx(ƒ) of the mapping ƒ at point x. Each entry of this matrix represents a partial derivative, specifying the rate of change of one range coordinate with respect to a change in a domain coordinate. Of course, the Jacobian
matrix of the composition g°f is a product of corresponding Jacobian matrices:
Jx(g°f) =Jƒ(x)(g)Jx(ƒ). This is a higher-dimensional statement of the chain rule.
For real valued functions from Rn to R (scalar fields), the Fréchet derivative corresponds to a vector field called the total derivative. This can be interpreted as the gradient but it is more natural to use the exterior derivative.
The convective derivative takes into account changes due to time dependence and motion through space along a vector field, and is a special case of the total derivative.
For vector-valued functions from R to Rn (i.e.
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In mathematics, a real-valued function is called convex if the line segment between any two distinct points on the graph of the function lies above the graph between the two points. Equivalently, a function is convex if its epigraph (the set of points on or above the graph of the function) is a convex set. A twice-differentiable function of a single variable is convex if and only if its second derivative is nonnegative on its entire domain.
The goal of this course is to give an introduction to the theory of distributions and cover the fundamental results of Sobolev spaces including fractional spaces that appear in the interpolation theor
Covers the concept of functional derivatives and their calculation process with examples.
Explores differential calculation and the notion of derivative in advanced analysis II.
Le contenu de ce cours correspond à celui du cours d'Analyse I, comme il est enseigné pour les étudiantes et les étudiants de l'EPFL pendant leur premier semestre. Chaque chapitre du cours correspond
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