Search results
Results From The WOW.Com Content Network
The image of a function f(x 1, x 2, …, x n) is the set of all values of f when the n-tuple (x 1, x 2, …, x n) runs in the whole domain of f.For a continuous (see below for a definition) real-valued function which has a connected domain, the image is either an interval or a single value.
The rectangular region at the bottom of the body is the domain of integration, while the surface is the graph of the two-variable function to be integrated. In mathematics (specifically multivariable calculus), a multiple integral is a definite integral of a function of several real variables, for instance, f(x, y) or f(x, y, z).
The (unproved) Jacobian conjecture is related to global invertibility in the case of a polynomial function, that is a function defined by n polynomials in n variables. It asserts that, if the Jacobian determinant is a non-zero constant (or, equivalently, that it does not have any complex zero), then the function is invertible and its inverse is ...
In single-variable calculus, operations like differentiation and integration are made to functions of a single variable. In multivariate calculus, it is required to generalize these to multiple variables, and the domain is therefore multi-dimensional. Care is therefore required in these generalizations, because of two key differences between 1D ...
In cases where the function in question has multiple roots, it can be difficult to control, via choice of initialization, which root (if any) is identified by Newton's method. For example, the function f ( x ) = x ( x 2 − 1)( x − 3)e −( x − 1) 2 /2 has roots at −1, 0, 1, and 3. [ 18 ]
The logarithm must be taken to base e since the two terms following the logarithm are themselves base-e logarithms of expressions that are either factors of the density function or otherwise arise naturally. The equation therefore gives a result measured in nats. Dividing the entire expression above by log e 2 yields the divergence in bits.
Newton's method to find zeroes of a function of multiple variables is given by + = [()] (), where [()] is the left inverse of the Jacobian matrix of evaluated for .. Strictly speaking, any method that replaces the exact Jacobian () with an approximation is a quasi-Newton method. [1]
Note that in the one-variable case, the Hessian condition simply gives the usual second derivative test. In the two variable case, (,) and (,) are the principal minors of the Hessian. The first two conditions listed above on the signs of these minors are the conditions for the positive or negative definiteness of the Hessian.