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In mathematics, especially vector calculus and differential topology, a closed form is a differential form α whose exterior derivative is zero (dα = 0), and an exact form is a differential form, α, that is the exterior derivative of another differential form β. Thus, an exact form is in the image of d, and a closed form is in the kernel of d.
In multivariate calculus, a differential or differential form is said to be exact or perfect (exact differential), as contrasted with an inexact differential, if it is equal to the general differential for some differentiable function in an orthogonal coordinate system (hence is a multivariable function whose variables are independent, as they are always expected to be when treated in ...
In numerical analysis, a root-finding algorithm is an algorithm for finding zeros, also called "roots", of continuous functions. A zero of a function f is a number x such that f ( x ) = 0 . As, generally, the zeros of a function cannot be computed exactly nor expressed in closed form , root-finding algorithms provide approximations to zeros.
In various areas of mathematics, the zero set of a function is the set of all its zeros. More precisely, if f : X → R {\displaystyle f:X\to \mathbb {R} } is a real-valued function (or, more generally, a function taking values in some additive group ), its zero set is f − 1 ( 0 ) {\displaystyle f^{-1}(0)} , the inverse image of { 0 ...
An illustration of Newton's method. In numerical analysis, the Newton–Raphson method, also known simply as Newton's method, named after Isaac Newton and Joseph Raphson, is a root-finding algorithm which produces successively better approximations to the roots (or zeroes) of a real-valued function.
In particular, if the derivative exists, it must be zero at c. By assumption, f is continuous on [a, b], and by the extreme value theorem attains both its maximum and its minimum in [a, b]. If these are both attained at the endpoints of [a, b], then f is constant on [a, b] and so the derivative of f is zero at every point in (a, b).
Given a simply connected and open subset D of and two functions I and J which are continuous on D, an implicit first-order ordinary differential equation of the form (,) + (,) =,is called an exact differential equation if there exists a continuously differentiable function F, called the potential function, [1] [2] so that
The exact solution is () =, which decays to zero as . However, if the Euler method is applied to this equation with step size h = 1 {\displaystyle h=1} , then the numerical solution is qualitatively wrong: It oscillates and grows (see the figure).