A distribution on is a continuous linear functional on the space of test functions . It has order of a distribution at most if, for every compact set , there is such that
The order of a distribution is the least such . When saying “of order ” means merely that this estimate holds, the wording allows a smaller least order.
For each such , the cutoff function theorem supplies a nonnegative equal to one near . If and , then everywhere. Positivity of the linear functional therefore gives
Here is finite and independent of . This is precisely the local bound for an order-zero distribution, so the given positive distribution is continuous on the space of test functions. The zero distribution also satisfies the estimate.
The distributional derivative of is defined by
It is a distribution because is a continuous map of the space of test functions into itself. If represents a regular distribution, integration by parts gives
The boundary term vanishes because has compact support. Thus the distributional derivative agrees with the ordinary derivative.
For , the assumed order of a distribution estimate gives
Consequently .
On each interval , , the function is on and on . Its ordinary derivative exists off and is the locally integrable function
Assign arbitrary values at the endpoints. For a test function , split at these corners and apply integration by parts on each piece. The adjacent boundary terms cancel because is continuous, and only finitely many pieces meet the compact support of . Hence
The distributional derivative is exactly the regular distribution of the ordinary derivative defined almost everywhere. There are no Dirac delta distributions in this first distributional derivative: corners change slope, while a jump of the function itself would create a point mass.
The bound proves uniform convergence of the series by the Weierstrass M-test. Each summand is continuous and satisfies , so its sum , the Takagi function, is continuous and has period one.
For , both and are integers, giving . Therefore
The corners of occur on the grid . When , that grid is contained in , so the interior of contains no corner of . Its slope on this interval is consequently or .
The interval is a half of . All the old slopes persist on this half, and the new summand contributes one more slope in . Thus
If had a finite derivative at , write with . Since and both endpoints tend to ,
Indeed ; when , its remainder is zero. This is the secants straddling a differentiability point argument, including dyadic . But the displayed increments prevent from converging. Hence , using periodicity to cover all real .
For the distributional derivative, set , using from part (iii). The change of variable in the distributional derivative pairing gives ; these are the Rademacher functions. Uniform convergence implies weak convergence of distributions, and the continuity of distributional differentiation allows termwise differentiation in that topology. Explicitly,
This series assertion is a distributional convergence statement. Its remainder satisfies the concrete estimate
It supplies a well-defined distributional derivative even though the ordinary derivative exists nowhere.
Choose with . Evenness of changes the symmetrically truncated integral into
The mean value theorem gives . Since is continuous, it is bounded on . Thus the Cauchy principal value exists and
This expression is linear in the test function and satisfies
For each fixed compact support, choose one once and for all. The estimate proves that the even-weight principal-value distribution has order of a distribution at most one.
If , the mean value theorem also gives . Hence is absolutely integrable near zero and has compact support. The truncated integrals tend to its ordinary Lebesgue integral, so
No. Consider the continuous even function
The formulas agree at , and at zero. Part (i) defines its even-weight principal-value distribution. However,
If this were a regular distribution represented by , testing on either side of zero would force almost everywhere there, by uniqueness of the locally integrable function representing a regular distribution. Changing the value at zero cannot remove the displayed divergence, contradicting local integrability of .
More generally, the exact condition is
Necessity is the preceding argument; sufficiency follows because is then a locally integrable function and its truncated integrals converge absolutely. The value alone provides no such rate of decay.

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