Preimage of Linear Functional and Closedness of Subspace

In summary, the problem asks to show that the set A, defined as the inverse image of the linear functional f with output 0, is a closed linear subspace. While the linearity of A can be easily proven, showing its closedness depends on the continuity of f. If f is continuous, the proof using sequences can be utilized, but if not, a different approach is needed. Alternatively, if one knows about topology, the pre-image of a closed set can be used to show the closedness of A.
  • #1
aaaa202
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Homework Statement


Let f be a linear functional and set A=f-1({0})
Show that A is a closed linear subspace.

Homework Equations


The linearity comes from the fact that if f(a)=0 and f(b)=0 then f(βa+γb)=βf(a)+γf(b)=0
But how do we know it is closed? Do we show every sequence in A is convergent inside A or how do you show closedness for a space like this?

The Attempt at a Solution

 
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  • #2
aaaa202 said:

Homework Statement


Let f be a linear functional and set A=f-1({0})
Show that A is a closed linear subspace.

Homework Equations


The linearity comes from the fact that if f(a)=0 and f(b)=0 then f(βa+γb)=βf(a)+γf(b)=0
But how do we know it is closed? Do we show every sequence in A is convergent inside A or how do you show closedness for a space like this?

The Attempt at a Solution


You can't prove it's closed unless f is continuous. If the vector space is infinite dimensional then you have to assume that, if it's finite dimensional then all linear functionals are continuous.
 
  • #3
Also, the proof depends on your definition of closed. As you thought, the proof using sequences will work quite well (and you will need the continuity of f because it implies that ##\lim f(a_n) = f(\lim a_n)##).

Informational note (aka side-track): If you know something about topology, there is also a nice characterization of continuous functions which states that the pre-image of a closed set is closed - that would give an even faster proof since {0} is closed.
 

Related to Preimage of Linear Functional and Closedness of Subspace

1. What is a preimage linear functional?

A preimage linear functional is a mathematical concept in functional analysis that assigns a real number to each point in a vector space. It is a linear transformation that maps from a vector space to its underlying field of scalars, and can be represented by a linear equation.

2. How is a preimage linear functional different from a regular linear functional?

Unlike a regular linear functional, which maps from a vector space to its underlying field of scalars, a preimage linear functional maps from a vector space to the set of real numbers. This means that the preimage linear functional is a more general concept and can operate on a wider range of vector spaces.

3. What is the importance of preimage linear functionals?

Preimage linear functionals are important in functional analysis because they allow for the study of vector spaces in terms of their underlying field of scalars. They also provide a way to analyze the properties and behavior of linear transformations, which are fundamental in many areas of mathematics and science.

4. How do you calculate the preimage of a linear functional?

To calculate the preimage of a linear functional, you need to first determine the underlying field of scalars, which is typically the set of real numbers. Then, you can use the linear equation that represents the functional to find the preimage of a given point in the vector space by solving for the input variable.

5. What are some real-world applications of preimage linear functionals?

Preimage linear functionals have many applications in areas such as physics, engineering, and economics. For example, they can be used to model physical systems, analyze financial data, and optimize engineering designs. They are also important in computer science for tasks such as image processing and data compression.

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