Proof about linear space, product equal to zero

In summary: This is a difficult question. I would recommend you to create a new thread if you have a new question.
  • #1
trenekas
61
0
Hello! I have a problem with one proof. The task is:

Suppose that X is linear space, x belongs to X and λ is real number. Proof if λx=0 so λ=0 or x=0. And there are conditions. Can use only this properties μ also is real number:

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I tryed to prove that but completely fails.
Let λ=0. According to d, λx=0
Let x=0. From c and g => λ(0+0)=λ0+λ0. And then i don't know what to do next. And also i think need to prove that if λ and x is not equal to zero when the product of them also not equal to zero.

Thanks for helping!
 
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  • #2
Hint: if ##\lambda \neq 0##, then ##\lambda## has a multiplicative inverse.
 
  • #3
jbunniii said:
Hint: if ##\lambda \neq 0##, then ##\lambda## has a multiplicative inverse.
Understand :) Thank you a lot
 
  • #4
sorry but after few hours i have little doubt about my proof :blushing: so if i missunderstand just say.

if λ=0 then λx=0 according to (d).
if λ isn't equal to zero, then there is a multiplicative inverse. λx=0, λ not equal to zero and μ=1/λ . when μ(λx)=(μλ)x=1x=x. And there is one way, x=0. So if λx=0 then λ=0 or x=0.

its good or not? thanks for answer :)
 
  • #5
trenekas said:
sorry but after few hours i have little doubt about my proof :blushing: so if i missunderstand just say.

if λ=0 then λx=0 according to (d).
if λ isn't equal to zero, then there is a multiplicative inverse. λx=0, λ not equal to zero and μ=1/λ . when μ(λx)=(μλ)x=1x=x. And there is one way, x=0. So if λx=0 then λ=0 or x=0.

its good or not? thanks for answer :)
Yes, I think the logic is right. You have shown that if ##\lambda x = 0## and ##\lambda \neq 0##, then ##x = 0##. But you should try to state it more clearly, and specify which vector space properties you are using. Something like the following:

Suppose ##\lambda x = 0## and ##\lambda \neq 0##. Then ##\lambda## has a multiplicative inverse, call it ##\mu = \lambda^{-1}##. By property (e), ##\mu(\lambda x) = (\mu \lambda) x = 1x = x##, where the last equality is true because of property (d). But ##\lambda x = 0##, so this means...
 
  • #6
jbunniii said:
Yes, I think the logic is right. You have shown that if ##\lambda x = 0## and ##\lambda \neq 0##, then ##x = 0##. But you should try to state it more clearly, and specify which vector space properties you are using. Something like the following:

Suppose ##\lambda x = 0## and ##\lambda \neq 0##. Then ##\lambda## has a multiplicative inverse, call it ##\mu = \lambda^{-1}##. By property (e), ##\mu(\lambda x) = (\mu \lambda) x = 1x = x##, where the last equality is true because of property (d). But ##\lambda x = 0##, so this means...
thanks dude very much. Its difficult to me all that write in english because my english isn't very good. in my native language i will try do the best. :)))
 
  • #7
Hello. I don't want to create another thread so i ask here.

There is task:

"A" is set of Euclidean space and x is "A" point of contact (x belong to Ā, don't know how is calling the set of all points of contact in english.). Need to prove that there is sequence of A elements which converges to x.

I don't have the idea how that prove. I found one theorem but it say that there is sequence of set elements which converges to x when x is borderline point.

If something not understand i'll try to say more cleary if it will be possible. Thanks.
 
  • #8
trenekas said:
Hello. I don't want to create another thread so i ask here.

There is task:

"A" is set of Euclidean space and x is "A" point of contact (x belong to Ā, don't know how is calling the set of all points of contact in english.). Need to prove that there is sequence of A elements which converges to x.

I don't have the idea how that prove. I found one theorem but it say that there is sequence of set elements which converges to x when x is borderline point.

If something not understand i'll try to say more cleary if it will be possible. Thanks.

It's better to create a new thread if you have a new question, unless it is very closely related to the existing thread.

In English, ##\bar{A}## is usually called the closure of ##A##, and the elements of ##\bar{A}## are sometimes called the points of closure of ##A##. The closure ##\bar{A}## consists of ##A## and all limit points of ##A##.

Here is a hint to get you started: if ##x \in \bar{A}## then either ##x \in A## or ##x## is a limit point of ##A##.

If ##x \in A## then it's trivial to create a sequence in ##A## which converges to ##x##.

If ##x## is a limit point of ##A##, then every neighborhood of ##x## contains an element of ##A## that is different from ##x##. Use that fact to construct a sequence converging to ##x##.
 
  • #9
omg :D thank you very much :))) you are awesome!
 

FAQ: Proof about linear space, product equal to zero

What is a linear space?

A linear space, also known as a vector space, is a mathematical concept that represents a collection of objects (vectors) that can be added and multiplied by constants. It follows specific axioms such as closure under addition and scalar multiplication, and has a zero vector and additive inverse.

What does "product equal to zero" mean in linear space?

In linear space, the product equal to zero refers to the dot product or inner product of two vectors being equal to zero. This means that the two vectors are orthogonal or perpendicular to each other, with an angle of 90 degrees between them.

How is the proof about linear space and product equal to zero useful?

The proof about linear space and product equal to zero is useful in various areas of mathematics, such as linear algebra and geometry. It helps in determining whether two vectors are orthogonal, which has applications in fields such as physics, engineering, and computer graphics.

Can a linear space have more than one zero vector?

No, a linear space can only have one zero vector. This is because the zero vector is a unique vector that satisfies the axioms of a linear space. Any other vector with the same properties as the zero vector would just be a scalar multiple of the zero vector.

How is the proof about linear space and product equal to zero related to linear independence?

The proof about linear space and product equal to zero is related to linear independence because two non-zero vectors are linearly independent if and only if their dot product is equal to zero. This means that they are not parallel and have a non-zero angle between them, making them independent of each other in the linear space.

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