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Introduction to Linear Algebra with Mathematica

Preface


The inverse Fourier transform is traditionally understood as a limit process that reconstructs a function f from its frequency-domain representation ℱ[f]. In the classical setting of 𝔏¹(ℝ) or 𝔏²(ℝ), this inversion is expressed through an oscillatory integral whose convergence is subtle and, in general, not absolutely guaranteed. Cesàro summation—specifically the (C,1) method—provides a powerful regularization technique that stabilizes this inversion process (see section).

 

Inverse Fourier transform

In the previous section, we showed that the Fourier operator maps 𝔏¹(ℝ) into &Cfrl;₀(ℝ), the space of continuous functions vanishing at infinity. Generally speaking, the image of arbitrary function with respect to the Fourier transformation does not belong to 𝔏¹.    
Example 1: Let us consider the piecewise continuous function \[ f(x) = \begin{cases} e^{-x} , &\quad \mbox{for } \ x > 0 , \\ 0, &\quad \mbox{otherwise} . \end{cases} \] Its Fourier transform exists because f ∈ 𝔏¹(ℝ): \[ \hat{f} (\xi ) = \int_0^{\infty} e^{-x} e^{-{\bf j}\xi x} {\text d}x = \frac{-{\bf j}}{\xi - {\bf j}} = \frac{1}{1 + {\bf j}\xi} . \]
Integrate[Exp[-x]*Exp[-I*x*t], {x, 0, Infinity}, Assumptions -> {t \[Element] Reals}]
-(I/(-I + t))
Its magnitude is \[ \left\vert \hat{f}(\xi ) \right\vert = \left\vert \frac{1}{1 + {\bf j}\xi} \right\vert = \frac{1}{\sqrt{1 + \xi^2}} . \] So the Fourier integral |ℱ[f]| decay at infinity as |ξ|−1 and hence is not integrable. Therefore, we cannot define its inverse Fourier transform.    ■
End of Example 1
This example shows that the Fourier transform of a function could be not integrable, so its inverse Fourier integral diverges. A remedy for this situation lies in regularization of improper integrals. Recall that improper integral is defined as the limit:
\[ \int_{-\infty}^{\infty} f(x)\,{\text d}x = \lim_{N, M \to +\infty} \int_{-N}^{M} f(x)\,{\text d}x , \]
when numbers N,M approach +∞ independently. If the limit above exists when N = M, the improper integral is said to be integrable in Cauchy principal value sense---this was the first regularization of integrals introduced by the French mathematician Augustin-Louis Cauchy around 1814–1825. Fourier transformation and its inverse are built on the Caucy principal.

For instance, the function

\[ Q(\xi ) = \begin{cases} \frac{1}{\xi} , & \quad \mbox{if }\ |\xi | \geqslant 1 , \\ \xi , & \quad \mbox{if }\ |\xi | < 1 , \end{cases} \]
is not integrable (in neither Lebesgue nor Riemann sense). However, its Cauchy principal value exists and equals
\[ \mbox{V.P.} \int_{-\infty}^{\infty} Q(\xi ) \,{\text d}\xi = 0 . \]
For a function f ∈ 𝔏¹(ℝ), the Fourier transform is defined by
\[ \hat {f}(\xi ) = \int_{\mathbb{R}}f(x)\,e^{-{\bf j}x\xi }\, {\text d}x. \]
Formally, the inverse transform is
\[ f(x) = \frac{1}{2\pi} \int_{\mathbb{R}} \hat{f}(\xi )\,e^{{\bf j}x\xi }\, {\text d}\xi . \]
However, this integral is not absolutely convergent in general. One therefore introduces the truncated inverse transform
\[ S_R f(x) = \frac{1}{2\pi } \int_{-R}^R \hat{f}(\xi )\,e^{{\bf j}x\xi }\, {\text d}\xi , \]
and interprets the inversion formula as the limit
\[ \lim _{R\rightarrow \infty }S_Rf(x). \]
The operator SR is convolution with the Dirichlet kernel of the Fourier transform:
\[ S_R f(x) = \left( f*D_R \right) (x),\qquad D_R (t) = \frac{1}{2\pi }\int _{-R}^Re^{{\bf j}t\xi }\, {\text d}\xi = \frac{\sin (Rt)}{\pi t}. \]
The kernel DR is oscillatory, nonpositive, and lacks good localization. As in Fourier series, these properties lead to divergence phenomena, Gibbs oscillations, and failure of pointwise convergence for general 𝔏¹ functions.

A function f(x) satisfies the Dini condition at a point x₀ if the function \( \displaystyle \quad \varphi (u) = \frac{f( x_0 + u) - f(x_0 )}{u} \quad \) belongs to class 𝔏¹(−δ, δ) for some δ > 0.
This condition is named after Ulisse Dini (1845–1918), an Italian mathematician, politician, and educator.
Theorem 1: If function f ∈ 𝔏¹ and satisfies Dini's condition at some point x, then at this point the following idenity is valid \[ f(x) = \frac{1}{2\pi} \int_{-\infty}^{\infty} \hat{f}(\xi )\, e^{{\bf j}\xi x} {\text d}\xi . \] Here \( \displaystyle \quad \hat{f} (\xi ) = \int f(x)\,e^{-{\bf j}\xi x} {\text d} x \ \) is the Fourier transform of function f and integrals are evaluated according to the Cauchy principal value.
If function f is absolutely Lebesgue integrable, then its Fourier transform F(ξ) = ℱ[f] is bounded and continuous function on whole line ℝ. Moreover, according to Riemann--Lebesgue lemma, we have \[ \lim_{|\xi | \to\infty} F(\xi ) = \lim_{|\xi | \to\infty} \int_{\mathbb{R}} f(x)\,e^{-{\bf j}\xi x} {\text d} x = 0 . \]

Let \[ f_N (x) = \frac{1}{2\pi} \int_{-N}^N F(\xi )\,e^{{\bf j}\xi x} {\text d} \xi , \] where N is some positive number. Substituting instead of F(ξ) its Fourier integral, we obtain \[ f_N (x) = \frac{1}{2\pi} \int_{-N}^N {\text d} \xi \int_{-\infty}^{\infty} f(u)\,e^{-{\bf j}\xi \left( u-x \right)} {\text d} u . \] Since \[ \left\vert f(u)\,e^{-{\bf j}\xi \left( u-x \right)} \right\vert \leqslant \left\vert f(u) \right\vert \] we can exchange order of integration and perform integration with respect to ξ. Hence, improper integral converges uniformly over ξ and we get \begin{align*} f_N (x) &= \frac{1}{2\pi} \int_{-\infty}^{\infty} f(u)\,{\text d}u \int_{-N}^N {\text d} \xi \,e^{-{\bf j}\xi \left( u-x \right)} \\ &= \frac{1}{2\pi} \int_{-\infty}^{\infty} {\text d}u\,f(u)\left. \frac{e^{-{\bf j}\xi \left( u-x \right)}}{{\bf j} \left( u-x \right)} \right\vert_{\xi = -M}^{\xi = N} \\ &= \frac{1}{\pi} \int_{-\infty}^{\infty} f(u)\,\frac{\sin N\left( u-x \right)}{u-x}\,{\text d} u . \end{align*} Changing variable of integration as t = ux in the latter, we obtain \[ f_N (x) = \frac{1}{\pi} \int_{-\infty}^{\infty} f(x+t)\,\frac{\sin Nt}{t}\,{\text d} t . \] It is well-known that \[ \int_0^{\infty} \frac{\sin \alpha x}{x}\,{\text d}x = \mbox{sign}\alpha \cdot \frac{\pi}{2} , \]

Integrate[Sin[a*x]/x, {x, 0, Infinity}]
(a \[Pi])/(2 Sqrt[a^2])
where sign(α) is defined by \[ \mbox{sign}(\alpha ) = \begin{cases} \phantom{-}1 , & \quad \mbox{for } \ \alpha > 0 , \\ \phantom{-}0 , & \quad \mbox{for } \ \alpha = 0 , \\ -1 , & \quad \mbox{for } \ \alpha < 0 . \end{cases} \] Then we derive \begin{align*} \int_{-\infty}^{\infty} \frac{\sin Nt}{t}\,{\text d}t &= \int_{-\infty}^{0} \frac{\sin Nt}{t}\,{\text d}t + \int_{0}^{\infty} \frac{\sin Nt}{t}\,{\text d}t \\ &= 2 \int_{0}^{\infty} \frac{\sin Nt}{t}\,{\text d}t = 2\cdot \mbox{sign}(N)\cdot \frac{\pi}{2} = \pi . \end{align*} Multiplying by f(x) and dividing by π, we obtain \[ f(x) = \frac{1}{\pi} \int_0^{\infty} \frac{\sin Nt}{t}\,{\text d} t . \] The second step of the proof includes establishing the identity \[ \lim_{N\to\infty} f_N (x) = f(x) \] subject that Dini's condition holds.

Subtraction yields \[ f_N (x) - f(x) = \frac{1}{\pi} \int_{-\infty}^{\infty} \left[ f(x+t) - f(x) \right] \frac{\sin Nt}{t}\,{\text d} t . \] This integral can be broken into three parts: \begin{align*} f_N (x) - f(x) &= \frac{1}{\pi} \int_{|t| \le A} \frac{f(x+t) - f(x)}{t}\, \sin (Nt)\,{\text d}t \\ &\quad + \frac{1}{\pi} \int_{|t| > A} \frac{f(x+t)}{t}\,\sin (Nt)\,{\text d}t - \frac{f(x)}{\pi} \int_{|t| > A} \frac{\sin Nt}{t}\,{\text d}t . \tag{P1.1} \end{align*} For any ε > 0, one can find the value of A > 1 such that the second term in Eq.(P1.1) will be less than ε/3 in modulos because \[ \left\vert \frac{\sin Nt}{t} \right\vert \leqslant 1 \qquad\mbox{for } \ |t| \ge 1 , \] and function f(x + t) ∈ 𝔏¹ in any variable (x or t). The last integral in Eq.(P1.1) aslo could be made less than ε/3 in absolute value for some A because the improper integral \[ \int_{-\infty}^{\infty} \frac{\sin Nt}{t}\,{\text d}t \] converges uniformly with respect to N ≥ 1. Dini's condition guarantees absolute integrability of function \[ \psi (t) - \begin{cases} \frac{f(x+t) - f(x)}{t} , &\quad |t| \le A , \\ 0, &\quad |t| > A . \end{cases} \] Then from the Riemann-Lebesgue lemma, it follows that \[ \lim_{N\to\infty} \int_{-\infty}^{\infty} \psi (t)\,\sin Nt\,{\text d}t = 0 \] This means that the absolute value of the first integral in Eq.(P1.1)can also be made less than ε/3. This means that for any ε > 0 we can find number N₀ starting from which we observe \[ \left\vert f_N (x) - f(x) \right\vert < \varepsilon . \] Hence, we obtain \[ \lim_{N\to\infty} f_N (x) = f(x) , \]

   
Example 2:    ■
End of Example 2
Let S be a non-empty set of real numbers.
  1. The set S is bounded above if there is a number M such that Mx for all xS. The number M is called an upper bound of S.
  2. The set S is bounded below if there exists a number m such that mx for all xS. The number m is called a lower bound of S.
  3. The set S is bounded if it is bounded above and below. Equivalently S is bounded if there exists a number r such that |x| ≤ r for all xS. The number r is called a bound for S.

Let S be a non-empty set of real numbers.
  1. Suppose that S is bounded above. A number β is the supremum of S if β is an upper bound of S and there is no number less than β that is an upper bound of S. We write β = sup S.
  2. Suppose that S is bounded below. A number α is the infimum of S if α is a lower bound of S and there is no number greater than α that is a lower bound of S. We write α = inf S.
Let S be a non-empty set of real numbers that is bounded above, and let b be an upper bound of S. Then b = sup S if and only if for all ε > 0 there exists xS such that x ∈ (b − ε, b]. We often refer to sup S as the least upper bound of S and to inf S as the greatest lower bound of S.
A partition of an interval [𝑎, b] is a set of points {x₀, x₁, … , xₙ} such that 𝑎 = x₀ < x₁ < ⋯ < xₙ = b.
With these definitions in hand we can define the variation of a function.
Let f : [𝑎, b] → ℝ be a function and let [c, d] be any closed subinterval of [𝑎, b]. If the set \[ S = \left\{ \sum_{i=1}^n \left\vert f( x_i ) - f(x_{i-1})\right\vert \ : \ \left\{ x_i \ : \ 1 \leqslant i \leqslant n \right\} \ \mbox{ is a partition of } [c,d]\right\} \] is bounded then the variation of f on [c, d] is defined to be V(f, [c, d]) = sup S. If S is unbounded then the variation of f is said to be ∞. A function f is of bounded variation on [c, d] if V(f, [c, d]) is finite.
   
Example 3: The Dirichlet function is a mathematical, non-continuous function defined on the real numbers, assigning a value of 1 to rational numbers and 0 to irrational numbers. It acts as an indicator function for rational numbers and is used as a "pathological" example to demonstrate everywhere-discontinuity and exceptions in integration, as it is not Riemann integrable.

We show that the Dirichlet function f defined by \[ f(x) = \begin{cases} 1, & \quad \mbox{if $x$ is rational}, \\ 0, & \quad \mbox{if $x$ is irrational}. \end{cases} \] is not of bounded variation on any interval.

Let n ∈ ℤ and n > 0. Let [𝑎, b] be a closed interval in ℝ. We construct a partition P = {x₀, x₁, … , xn+2} of [𝑎, b] such that V(f, [𝑎, b]) ≥ ∑n+2i=1 |f(xi) − f(xi−1)| > n as follows.

Recall that by definition x₀ = 𝑎. It is known that between any two real numbers there is a rational number and an irrational number. Take x₁ to be an irrational number between 𝑎 and b. Then take x₂ to be a rational number between x₁ and b. Continue like this, taking x2i+1 to be an irrational number between x2i and b, and x2i to be a rational number between x2i−1 and b. Finally xn+2 = b. Thus, we have created a partition that begins with 𝑎 and then alternates between rational and irrational numbers, until it finally ends with b. Now consider the sum ∑n+2i=1 |f(xi) − f(xi−1)|, which we know is at most the variation of f on [𝑎, b]. Hence, \begin{align*} V(f, [a,b]) &\ge \sum_{i=1}^{n+2} \left\vert f(x_i ) - f(x_{i-1}) \right\vert \\ &\ge \sum_{i=2}^{n+1} \left\vert f(x_i ) - f(x_{i-1}) \right\vert \\ &= \left\vert f(x_2 ) - f(x_1 ) \right\vert + \cdots + \left\vert f(x_{n+1}) - f(x_n ) \right\vert \\ &= \left\vert 1-0 \right\vert + \left\vert 0-1 \right\vert + \cdots + \left\vert 1-0 \right\vert \\ &= 1 + 1 + 1 + \cdots + 1 \\ &= n . \end{align*} Thus, V(f, [𝑎, b]) is arbitrarily large, and so V(f, [𝑎, b]) = ∞.    ■

End of Example 3
Functions of bounded variation (BV) were first introduced by the French mathematician Camille Jordan in 1881. The following theorem (formulated without a proof) characterizes these functions.
Theorem 2: If f : [𝑎, b] → ℝ is a function of bounded variation, then there exist two increasing functions f₁ and f₂ such that f = f₁ − f₂.
   
Example 4: The function f defined by \[ f(x) = \begin{cases} \sqrt[3]{x}\,\sin\left(\pi /x \right) , & \quad \mbox{for }\ x \ne 0 , \\ 0 , & \quad \mbox{if } \ x = 0 , \end{cases} \] is not of bounded variation on [0, 1].

We begin by making a partition of [0, 1]. We assume, without loss of generality, that the number of partition points is even, so n is even. We make our partition as follows:     xₙ = 1, xn−(2k+1) = 1/(k+3), xn−2k = 2/(2k+3), for k = 1, 2, … and x₀ = 0.

Then \begin{align*} V(f, [0,1]) &\ge \sum_{i=1}^n \left\vert f(x_i ) - f(x_{i-1}) \right\vert \\ &\ge \sum_{i=1}^{n/2} \left\vert f(x_i ) - f(x_{i-1}) \right\vert . \end{align*} That is to say that we remove every other interval from the sum. Over the remaining intervals, our function has some convenient properties. The intervals we are now considering have the form [1/(k + 3), 2/(2k + 3)]. Also notice that \( \displaystyle \quad |f (2/(2k + 3))| = \sqrt[3]{2/(2k + 3)} \quad \) and    f(1/(k + 3)) = 0. Because \( \displaystyle \ \sqrt[3]{x} \ \) is an increasing function, sin(π/x) is the only component affecting when f is increasing or decreasing. Knowing how the sine function behaves we can see that f is monotone on each interval of the form [1/(k + 3), 2/((2k + 3)]. Thus, \begin{align*} V(f, [0,1]) &\ge \sum_{i=1}^{n/2} \left\vert f(x_{2i}) - f(x_{2i-1}) \right\vert \\ &= \sum_{i=1}^n \left\vert f(1/(k+3)) - f(2/(2k+3)) \right\vert \\ &= \sum_{i=1}^n \left\vert \sqrt[3]{\frac{2}{2k+3}} \right\vert = \sum_{i=1}^n \frac{\sqrt[3]{2}}{\sqrt[3]{2k+3}} . \end{align*} Notice that each term is smaller than the last, since the denominator is getting larger as k increases. Hence, \[ \sum_{i=1}^n \frac{\sqrt[3]{2}}{\sqrt[3]{2k+3}} \ge \sum_{i=1}^n \frac{1}{\sqrt[3]{k+3}} = \sum_{i=2}^{n+1} \frac{1}{\sqrt[3]{k}} . \] This is a p-series, with p = 1/3, so we know that it diverges. This means that by choosing n large enough we can make the sum \[ \sum_{i=1}^n \frac{\sqrt[3]{2}}{\sqrt[3]{2k+3}} \] as large as we like and thus V(f, [0, 1]) = ∞. Thus, though the function is continuous it is not of bounded variation on [0, 1].    ■

End of Example 4
Theorem 3: If f ∈ 𝔏¹ and f has bounded variation on interval [x₀−δ, x₀+δ] for some δ>0, then \[ \frac{f(x_0 + 0) + f(x_0 -0)}{2} = \frac{1}{2\pi}\,\mbox{V.P.} \int_{-\infty}^{\infty} \hat{f}(\xi )\,e^{{\bf j}\xi x_0} {\text d}\xi . \] In particular, if function f(x) is continuous at x₀, then f(x₀) = ½[f(x₀+0) + f(x₀−0)].
   
Example 5:    ■
End of Example 5
Theorem 6:
   
Example 7:    ■
End of Example 7
   
Example 5:    ■
End of Example 5
   

 

 

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