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Introduces the definition of a linear functional in a normed space and the norm of a functional.

Many systems in engineering can be characterized as linear systems, taking inputs in one signal space into outputs in another. The most common type of system maps into a space of scalars, defined as maps f : X R . We will study these maps (known in the mathematics literature as functionals ) during the next few lectures.

Definition 1 A functional f on X is linear if for all x , y X , a , b R , we have that f ( a x + b y ) = a f ( x ) + b f ( y ) .

Example 1 In the case X = R n , all linear functionals can be written using the form f ( x ) = i = 1 n a i x i for some set of scalars { a 1 , ... , a n } R .

Example 2 For a Hilbert space X , f ( x ) = x , y for any y X is a linear functional.

Example 3 For the space X = C ( [ 0 , 1 ] ) , the sampling functionals f ( x ) = x ( t 0 ) = x , δ ( t - t 0 ) are linear.

Example 4 f ( x ) = x is not a linear functional (since the triangle inequality is not a strict equality).

Linear functionals are particularly amenable to analysis.

Theorem 1 If a linear functional on a normed space X is continuous at a point x 0 X , then it is continuous on the entire space X .

Assume f ( x ) is contiuous at x 0 . Let { x n } be a sequence of points converging to x . Then,

| f ( x n ) - f ( x ) | = | f ( x n + x 0 - x 0 ) - f ( x ) | = | f ( x n + x 0 ) - f ( x 0 ) - f ( x ) | = | f ( x n - x + x 0 ) - f ( x 0 ) | .

It is easy to show that { x n - x + x 0 } is a sequence that converges to x 0 . Therefore, f ( x n - x + x 0 ) has to converge to f ( x 0 ) as f is continuous at x 0 :

| f ( x n - x + x 0 ) - f ( x 0 ) | n 0 .

This implies that the sequence { f ( x n ) } converges to f ( x ) and so f is continuous at x . Since x was arbitrary, then f is continuous on X .

Fact 1 For f ( x ) a linear functional, what is the value of f ( 0 ) ? For any such f ,

f ( 0 ) = f ( 0 · x ) = 0 · f ( x ) = 0 .

Definition 2 A linear functional f on a normed space X is bounded if there exists a constant M < such that | f ( x ) | M x for all x X . The smallest such element is denoted as the norm of the function f :

f = inf x X { M : | f ( x ) | M x , x X } .

There are several ways in which we can write this norm:

f = sup x X , x 0 | f ( x ) | x = sup x X , x = 1 | f ( x ) | = sup x X , x 1 | f ( x ) |

Before continuing, we should verify that the defined f is a valid norm.

  • f = 0 f = z ( x ) = 0 for all x X .
  • f 0 : M has to be positive since | f ( x ) | and x are always positive.
  • α f = | α | f : can be trivially shown.
  • f 1 + f 2 f 1 + f 2 : by definition,
    f 1 + f 2 = sup x 1 | ( f 1 + f 2 ) ( x ) | = sup x 1 | f 1 ( x ) + f 2 ( x ) | sup x 1 ( | f 1 ( x ) | + | f 2 ( x ) | ) , sup x 1 | f 1 ( x ) | + sup x 1 | f 2 ( x ) | = f 1 + f 2 .

The following theorem can save us much work in checking for continuity of linear functionals.

Theorem 2 A linear functional on a normed space is bounded if and only if it is continuous.

( ) Assume f is bounded, and let M be such that | f ( x ) | M x for all x X . Then for a sequence { x n } 0 we have | f ( x n ) | M x n .Therefore, | f ( x ) | 0 = f ( 0 ) , which implies that f is continuous at x = 0 . Using Theorem  [link] , f is continuous on X .

( ) Assume f is continuous. If we set ϵ = 1 there exist δ > 0 such that if x - 0 < δ then | f ( x ) - f ( 0 ) | < 1 , i.e.

x < δ | f ( x ) | < 1 .

Since f is linear, we can write this as

x < 1 | f ( x ) | < 1 δ .

So, f 1 δ < and f is bounded.

Example 5 Let X be a space of finitely nonzero sequences with x = max i | x i | . Define a functional f as

f ( x ) = i = 1 i x i

f is linear because

f ( a x + b y ) = i = 1 i ( a x + b y ) i = i = 1 i ( a x i + b y i ) = i = 1 ( a i x i + b i y i ) = a i = 1 i x i + b i = 1 i y i = a f ( x ) + b f ( y )

If we want to show that f is unbounded, we must show that for every M > 0 there exists x X such that | f ( x ) | > M x . Let x = ( 1 , 1 , . . . , 1 M times , 0 , 0 , . . . ) X , where M is the smallest integer greater or equal to M . Then,

f ( x ) = i = 1 M i · 1 i = 1 M 1 = M M .

Therefore, f is unbounded.

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Source:  OpenStax, Signal theory. OpenStax CNX. Oct 18, 2013 Download for free at http://legacy.cnx.org/content/col11542/1.3
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