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A matrix difference equation is a difference equation in which the value of a vector (or sometimes, a matrix) of variables at one point in time is related to its own value at one or more previous points in time, using matrices. [1] [2] The order of the equation is the maximum time gap between any two indicated values of the variable vector. For ...
The finite difference of higher orders can be defined in recursive manner as Δ n h ≡ Δ h (Δ n − 1 h). Another equivalent definition is Δ n h ≡ [T h − I ] n. The difference operator Δ h is a linear operator, as such it satisfies Δ h [ α f + β g ](x) = α Δ h [ f ](x) + β Δ h [g](x). It also satisfies a special Leibniz rule:
where () is an vector of functions of an underlying variable , ˙ is the vector of first derivatives of these functions, and () is an matrix of coefficients. In the case where A {\displaystyle \mathbf {A} } is constant and has n linearly independent eigenvectors , this differential equation has the following general solution,
The classical finite-difference approximations for numerical differentiation are ill-conditioned. However, if f {\displaystyle f} is a holomorphic function , real-valued on the real line, which can be evaluated at points in the complex plane near x {\displaystyle x} , then there are stable methods.
In vector calculus the derivative of a vector y with respect to a scalar x is known as the tangent vector of the vector y, . Notice here that y : R 1 → R m . Example Simple examples of this include the velocity vector in Euclidean space , which is the tangent vector of the position vector (considered as a function of time).
The most noteworthy property of cosine similarity is that it reflects a relative, rather than absolute, comparison of the individual vector dimensions. For any positive constant and vector , the vectors and are maximally similar. The measure is thus most appropriate for data where frequency is more important than absolute values; notably, term ...
The components of a vector are often represented arranged in a column. By contrast, a covector has components that transform like the reference axes. It lives in the dual vector space, and represents a linear map from vectors to scalars. The dot product operator involving vectors is a good example of a covector.
Normally, a matrix represents a linear map, and the product of a matrix and a column vector represents the function application of the corresponding linear map to the vector whose coordinates form the column vector. The change-of-basis formula is a specific case of this general principle, although this is not immediately clear from its ...