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Cubic equation. Graph of a cubic function with 3 real roots (where the curve crosses the horizontal axis at y = 0 ). The case shown has two critical points. Here the function is and therefore the three real roots are 2, -1 and -4. In algebra, a cubic equation in one variable is an equation of the form in which a is not zero.
Multiplicative function. In number theory, a multiplicative function is an arithmetic function f ( n) of a positive integer n with the property that f (1) = 1 and whenever a and b are coprime . An arithmetic function f ( n) is said to be completely multiplicative (or totally multiplicative) if f (1) = 1 and f ( ab) = f ( a) f ( b) holds for all ...
Calculus. In vector calculus, the Jacobian matrix ( / dʒəˈkoʊbiən /, [ 1][ 2][ 3] / dʒɪ -, jɪ -/) of a vector-valued function of several variables is the matrix of all its first-order partial derivatives. When this matrix is square, that is, when the function takes the same number of variables as input as the number of vector components ...
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) .
Vector-valued function. A vector-valued function, also referred to as a vector function, is a mathematical function of one or more variables whose range is a set of multidimensional vectors or infinite-dimensional vectors. The input of a vector-valued function could be a scalar or a vector (that is, the dimension of the domain could be 1 or ...
In the formulation given above, the scalars x n are replaced by vectors x n and instead of dividing the function f(x n) by its derivative f ′ (x n) one instead has to left multiply the function F(x n) by the inverse of its k × k Jacobian matrix J F (x n). [15] [16] [17] This results in the expression
In mathematics, specifically set theory, the Cartesian product of two sets A and B, denoted A × B, is the set of all ordered pairs (a, b) where a is in A and b is in B. [1] In terms of set-builder notation, that is [2] [3] A table can be created by taking the Cartesian product of a set of rows and a set of columns.
The multivariate normal distribution is said to be "non-degenerate" when the symmetric covariance matrix is positive definite. In this case the distribution has density [5] where is a real k -dimensional column vector and is the determinant of , also known as the generalized variance.