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Unformatted text preview: MATH 220: THE FOURIER TRANSFORM TEMPERED DISTRIBUTIONS Beforehand we constructed distributions by taking the set C c ( R n ) as the set of very nice functions, and defined distributions as continuous linear maps u : C c ( R n ) C (or into reals). While this was an appropriate class when studying just derivatives, we have seen that for the Fourier transform the set of very nice functions is that of Schwartz functions. Thus, we expect that the set S ( R n ) of corresponding distributions should consist of continuous linear maps u : S ( R n ) C . In order to make this into a definition, we need a notion of convergence on S ( R n ). Since C c ( R n ) S ( R n ), it is not unreasonable to expect that if a sequence { j } j =1 converges to some C c ( R n ) inside the space C c ( R n ) (i.e. in the sense of C c ( R n ) convergence), then it should also converge to in the sense of Sconvergence. We shall see that this is the case, which implies that every u S lies in D as well: for if j in C c ( R n ) then j in S , hence u ( j ) u ( ) by the continuity of u as an element of S . Thus, S D , i.e. S is a special class of distributions. In order to motivate the definition of Sconvergence, recall that S = S ( R n ) is the set of functions C ( R n ) with the property that for any multiindices , N n , x is bounded. Here we wrote x = x 1 1 x 2 2 . . .x n n , and = 1 x 1 . . . n x n ; with x j = x j . With this in mind, convergence of a sequence m S , m N , to some S , in S is defined as follows. We say that m converges to in S if for all multiindices , , sup  x ( m )  0 as m , i.e. if x m converges to x uniformly. A tempered distribution u is defined as a continuous linear functional on S (this is written as u S ), i.e. as a map u : S C which is linear: u ( a + b ) = au ( ) + bu ( ) for all a, b C , , S , and which is continuous: if m converges to in S then lim m u ( m ) = u ( ) (this is convergence of complex numbers). As mentioned already, any tempered distribution is a distribution, since C c implies S , and convergence of a sequence in C c implies that in S (recall that convergence of a sequence in C c means that the supports stay inside a fixed compact set and the convergence of all derivatives is uniform). The converse is of course not true; e.g. any continuous function f on R n defines a distribution, but integraltext R n f ( x ) ( x ) dx will not converge for all S if f grows too fast at infinity; e.g. f ( x ) = e  x  2 does not define a tempered distribution. On the other hand, any continuous function f satisfying an estimate  f ( x )  C (1 +  x  ) N for some...
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This note was uploaded on 03/16/2010 for the course CME 303 taught by Professor Vasy during the Fall '10 term at Stanford.
 Fall '10
 Vasy

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