Weierstrass transform
In mathematics, the Weierstrass transform of a function , named after Karl Weierstrass, is a "smoothed" version of obtained by averaging the values of , weighted with a Gaussian centered at "x". Specifically, it is the function defined by the convolution of with the Gaussian function The factor 1/√(4π) is chosen so that the Gaussian will have a total integral of 1, with the consequence that constant functions are not changed by the Weierstrass transform. Instead of one also writes . Note that need not exist for every real number , when the defining integral fails to converge. The Weierstrass transform is  intimately related to the heat equation (or, equivalently, the diffusion equation with constant diffusion coefficient). If the function describes the initial temperature at each point of an infinitely long rod that has constant thermal conductivity equal to 1, then the temperature distribution of the rod "t" = 1 time units later will be given by the function "F". By using values of "t" different from 1, we can define the generalized Weierstrass transform of . The generalized Weierstrass transform provides a means to approximate a given integrable function arbitrarily well with analytic functions. Names. Weierstrass used this transform in his original  proof of the Weierstrass approximation theorem. It is also known as the Gauss transform or Gauss–Weierstrass transform after Carl Friedrich Gauss and as the Hille transform after Einar Carl Hille who studied it extensively. The generalization "W" mentioned below is known in signal analysis as a Gaussian filter and in image processing (when implemented on R) as a Gaussian blur. Transforms of some important functions. As mentioned above, every constant function is its own Weierstrass transform. The Weierstrass transform of any polynomial is a polynomial of the same degree, and in fact same leading coefficient (the asymptotic growth is  unchanged). Indeed, if denotes the (physicist's) Hermite polynomial of degree "n", then the Weierstrass transform of (/2) is simply . This can be shown by exploiting the fact that the generating function for the Hermite polynomials is closely related to the Gaussian kernel used in the definition of the Weierstrass transform. The Weierstrass transform of the function "e" (where "a" is an arbitrary constant) is "e" "e". The function "e" is thus an eigenfunction of the Weierstrass transform. (This is, in fact, more generally true for "all" convolution transforms.) Setting "a"="bi" where "i" is the imaginary unit, and applying Euler 's identity, one sees that the Weierstrass transform of the function cos("bx") is "e" cos("bx") and the Weierstrass transform of the function sin("bx") is "e" sin("bx"). The Weierstrass transform of the function "e" is In particular, by choosing "a" negative, it is evident that the Weierstrass transform of a Gaussian function is again a Gaussian function, but a "wider" one. General properties. The Weierstrass transform assigns to each function "f" a new function "F"; this assignment is linear. It is also translation-invariant, meaning that the transform of the function "f"("x" + "a") is "F"("x" + "a"). Both of these  facts are more generally true for any integral transform defined via convolution. If the transform "F"("x") exists for the real numbers "x" = "a" and "x" = "b", then it also exists for all real values in between and forms an analytic function there; moreover, "F"("x") will exist for all complex values of "x" with "a" ≤ Re("x") ≤ "b" and forms a holomorphic function on that strip of the complex plane. This is the formal statement of the "smoothness" of "F" mentioned above. If "f" is integrable over the whole real axis (i.e. "f" ∈ L(R)), then so is  its Weierstrass transform "F", and if furthermore "f"("x") ≥ 0 for all "x", then also "F"("x") ≥ 0 for all "x" and the integrals of "f" and "F" are equal. This expresses the physical fact that the total thermal energy or heat is conserved by the heat equation, or that the total amount of diffusing material is conserved by the diffusion equation. Using the above, one can show that for 0 < "p" ≤ ∞ and "f" ∈ L(R), we have "F" ∈ L(R) and ||"F"|| ≤ ||"f"||. The Weierstrass transform consequently yields a bounded operator W : L(R) → L(R ). If "f" is sufficiently smooth, then the Weierstrass transform of the "k"-th derivative of "f" is equal to the "k"-th derivative of the Weierstrass transform of "f". There is a formula relating the Weierstrass transform "W" and the two-sided Laplace transform "L". If we define then Low-pass filter. We have seen above that the Weierstrass transform of cos("bx") is "e" cos("bx"), and analogously for sin("bx"). In terms of signal analysis, this suggests that if the signal "f" contains the frequency "b" (i.e. contains a summand which is a combination of sin("bx") and cos("bx")), then the transformed signal "F  will contain the same frequency, but with an amplitude multiplied by the factor e". This has the consequence that higher frequencies are reduced more than lower ones, and the Weierstrass transform thus acts as a low-pass filter. This can also be shown with the continuous Fourier transform, as follows. The Fourier transform analyzes a signal in terms of its frequencies, transforms convolutions into products, and transforms Gaussians into Gaussians. The Weierstrass transform is convolution with a Gaussian and is therefore "multiplication" of the Fourier transformed signal with a Gaussian, followed by application of the inverse Fourier transform. This multiplication  with a Gaussian in frequency space blends out high frequencies, which is another way of describing the "smoothing" property of the Weierstrass transform. The inverse transform. The following formula, closely related to the Laplace transform of a Gaussian function, and a real analogue to the Hubbard–Stratonovich transformation, is relatively easy to establish: Now replace "u" with the formal differentiation operator "D" = "d"/"dx" and utilize the Lagrange shift operator (a consequence of the Taylor series formula and the definition of the exponential function), to obtain to thus obtain the following formal expression for the Weierstrass transform "W", where the  operator on the right is to be understood as acting on the function "f"("x") as The above formal derivation glosses over details of convergence, and the formula "W" = "e" is thus not universally valid; there are several functions "f" which have a well-defined Weierstrass transform, but for which "e""f"("x") cannot be meaningfully defined. Nevertheless, the rule is still quite useful and can, for example, be used to derive the Weierstrass transforms of polynomials, exponential and trigonometric functions mentioned above. The formal inverse of the Weierstrass transform is thus given by Again, this formula is not universally valid  but can serve as a guide. It can be shown to be correct for certain classes of functions if the right-hand side operator is properly defined. One may, alternatively, attempt to invert the Weierstrass transform in a slightly different way: given the analytic function apply "W" to obtain once more using a fundamental property of the (physicists') Hermite polynomials . Again, this formula for "f"("x") is at best formal, since one didn't check whether the final series converges. But if, for instance, "f" ∈ L(R), then knowledge of all the derivatives of "F" at "x" = 0 suffices to  yield the coefficients "a"; and to thus reconstruct as a series of Hermite polynomials. A third method of inverting the Weierstrass transform exploits its connection to the Laplace transform mentioned above, and the well-known inversion formula for the Laplace transform. The result is stated below for distributions. Generalizations. We can use convolution with the Gaussian kernel formula_13 (with some ) instead of formula_14, thus defining an operator , the generalized Weierstrass transform. For small values of is very close to , but smooth. The larger , the more this operator averages out and changes . Physically, corresponds to following the heat (or diffusion) equation  for time units, and this is additive, formula_15 corresponding to "diffusing for time units, then time units, is equivalent to diffusing for time units". One can extend this to by setting to be the identity operator (i.e. convolution with the Dirac delta function), and these then form a one-parameter semigroup of operators. The kernel formula_13 used for the generalized Weierstrass transform is sometimes called the Gauss–Weierstrass kernel, and is Green's function for the diffusion equation formula_17 on . The Weierstrass transform can also be defined for certain classes of distributions or "generalized functions". For example, the Weierstrass transform  of the Dirac delta is the Gaussian formula_18. In this context, rigorous inversion formulas can be proved, e.g., formula_19 where is any fixed real number for which exists, the integral extends over the vertical line in the complex plane with real part , and the limit is to be taken in the sense of distributions. Furthermore, the Weierstrass transform can be defined for real- (or complex-) valued functions (or distributions) defined on . We use the same convolution formula as above but interpret the integral as extending over all of and the expression as the square of the Euclidean length of the  vector ; the factor in front of the integral has to be adjusted so that the Gaussian will have a total integral of 1. More generally, the Weierstrass transform can be defined on any Riemannian manifold: the heat equation can be formulated there (using the manifold's Laplace–Beltrami operator), and the Weierstrass transform is then given by following the solution of the heat equation for one time unit, starting with the initial "temperature distribution" . Related transforms. If one considers convolution with the kernel instead of with a Gaussian, one obtains the Poisson transform which smoothes and averages a given function  in a manner similar to the Weierstrass transform.