In a linear space, 0 times an element of the space need not be 0?

In summary: So in summary, the property 0x = 0 is implicitly assumed to be true in all vector spaces, including Banach spaces, and does not need to be explicitly mentioned in the definition of a linear space. It can be derived from the distributivity property, which states that multiplying a vector by the sum of two scalars is the same as multiplying the vector by each scalar separately and then adding the results.
  • #1
Fractal20
74
1

Homework Statement


Hello, we are starting to get to Banach spaces and thus linear normed spaces in a functional analysis class and I am realizing that I don't have much experience or intuition with these spaces. So I was reading over the requirements for a linear space in my notes and was surprised that there was not a property that 0[itex]\cdot[/itex]x = 0. Is this just implicitly assumed to be true, or is this really not a property of a linear space?

To be more precise, I have an intuitive understanding of what a linear space means if we are considering Euclidean vectors, but if it is just some abstract space that follows the rules of a linear space, then I don't really know what it means to multiply by a scalar. For example, I know what the output of multiplying a vector by a scalar will be but in a more abstract setting it doesn't seem like such a rule needs to be given, only the property that the result will still be in the space. So I am trying to not make the mistake of applying what I know about normal Euclidean vectors to more general concepts.

Homework Equations


The properties of a linear space

The Attempt at a Solution

 
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  • #2
Yes, if x is any vector then 0x=0 (where the first 0 is the zero scalar and the second is the zero vector). That's a property of ALL vector spaces and a Banach space is a type of vector space. So they may not have felt a need to specifically mention it.
 
  • #3
So vector space is interchangeable with linear space?
 
  • #4
Fractal20 said:
So vector space is interchangeable with linear space?

I would think so.
 
  • #5
The property ##0x = 0## does not need to be listed as part of the definition, as it can be derived from the distributivity property: a vector space must satisfy ##(a+b)x = ax + bx## for all scalars ##a## and ##b## and all vectors ##x##. Choosing ##a=b=0##, this implies that ##(0+0)x = 0x + 0x##. As ##0 + 0 = 0## is true in any field by definition of the additive identity ##0##, the left hand side simplifies to ##0x##, and we have ##0x = 0x + 0x##. Subtracting ##0x## from both sides, we get ##0 = 0x##.
 

FAQ: In a linear space, 0 times an element of the space need not be 0?

What does it mean for an element to be "linear" in a space?

A linear element in a space is one that follows the rules of linearity, meaning it can be multiplied by a scalar and added to other elements in the space.

Why is it possible for 0 times an element in a linear space to not be 0?

This is because the scalar 0 can be considered a "trivial" scalar, meaning it has no effect on the element it is being multiplied by. Therefore, the resulting element may not be the same as the original element, but it is still considered a valid element in the linear space.

Can you give an example of 0 times an element in a linear space not being 0?

Yes, consider the linear space of real numbers with the operation of addition. If we have the element 5, then 0 times 5 would equal 0, as expected. However, if we have the element 0, then 0 times 0 would still equal 0, but it is not the same element as the original 0. Therefore, 0 times an element in this linear space may not always result in the same element.

Does this concept only apply to real numbers or can it be generalized to other linear spaces?

This concept can be generalized to other linear spaces as well. As long as the space follows the rules of linearity, the concept of 0 times an element not always resulting in 0 will apply.

How is this concept relevant in scientific research?

In scientific research, linear spaces are often used to model and analyze data. Understanding the concept of 0 times an element not always being 0 is important in accurately interpreting and manipulating the data in these spaces. It can also help in finding alternative approaches or solutions to a problem by considering the potential effects of different scalars on elements in the space.

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