In mathematics, a positive-definite function is, depending on the context, either of two types of function.
Most common usage
A positive-definite function of a real variable x is a complex-valued function \( {\displaystyle f:\mathbb {R} \mapsto \mathbb {C} } \) such that for any real numbers x1, …, xn the n×n matrix
\( {\displaystyle A=\left(a_{i,j}\right)_{i,j=1}^{n}~,\quad a_{i,j}=f(x_{i}-x_{j})} \)
is positive semi-definite (which requires A to be Hermitian; therefore f(−x) is the complex conjugate of f(x)).
In particular, it is necessary (but not sufficient) that
\( f(0) \geq 0~, \quad |f(x)| \leq f(0) \)
(these inequalities follow from the condition for n = 1, 2.)
A function is negative definite if the inequality is reversed. A function is semidefinite if the strong inequality is replaced with a weak (≤, ≥ 0).
Examples
Bochner's theorem
Main article: Bochner's theorem
Positive-definiteness arises naturally in the theory of the Fourier transform; it can be seen directly that to be positive-definite it is sufficient for f to be the Fourier transform of a function g on the real line with g(y) ≥ 0.
The converse result is Bochner's theorem, stating that any continuous positive-definite function on the real line is the Fourier transform of a (positive) measure.[1]
Applications
In statistics, and especially Bayesian statistics, the theorem is usually applied to real functions. Typically, n scalar measurements of some scalar value at points in R d {\displaystyle R^{d}} R^{d} are taken and points that are mutually close are required to have measurements that are highly correlated. In practice, one must be careful to ensure that the resulting covariance matrix (an n-by-n matrix) is always positive-definite. One strategy is to define a correlation matrix A which is then multiplied by a scalar to give a covariance matrix: this must be positive-definite. Bochner's theorem states that if the correlation between two points is dependent only upon the distance between them (via function f()), then function f() must be positive-definite to ensure the covariance matrix A to be positive-definite. See Kriging.
In this context, Fourier terminology is not normally used and instead it is stated that f(x) is the characteristic function of a symmetric probability density function (PDF).
Generalization
Main article: Positive-definite function on a group
One can define positive-definite functions on any locally compact abelian topological group; Bochner's theorem extends to this context. Positive-definite functions on groups occur naturally in the representation theory of groups on Hilbert spaces (i.e. the theory of unitary representations).
In dynamical systems
A real-valued, continuously differentiable function f is positive-definite on a neighborhood of the origin, D, if } f(0)=0 and \( {\displaystyle f(x)>0} \) for every non-zero \( x\in D \).[2][3] This definition is in conflict with the one above.
See also
Positive-definite kernel
References
Christian Berg, Christensen, Paul Ressel. Harmonic Analysis on Semigroups, GTM, Springer Verlag.
Z. Sasvári, Positive Definite and Definitizable Functions, Akademie Verlag, 1994
Wells, J. H.; Williams, L. R. Embeddings and extensions in analysis. Ergebnisse der Mathematik und ihrer Grenzgebiete, Band 84. Springer-Verlag, New York-Heidelberg, 1975. vii+108 pp.
Notes
Bochner, Salomon (1959). Lectures on Fourier integrals. Princeton University Press.
Verhulst, Ferdinand (1996). Nonlinear Differential Equations and Dynamical Systems (2nd ed.). Springer. ISBN 3-540-60934-2.
Hahn, Wolfgang (1967). Stability of Motion. Springer.
Undergraduate Texts in Mathematics
Graduate Studies in Mathematics
Hellenica World - Scientific Library
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