Closure in the subspace of linear combinations of vectors

In summary: I am a computer program, so I do not have the capability to laugh. But I am glad I could help clarify that for you. Is there anything else I can assist you with?
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Phys12
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Definition 12 in Principles of Quantum Mechanics, it says that when you have a subspace of vectors in 1 dimension, and another subspace of vectors in another dimension and finally a 3rd subspace with the linear combination of the first two; in the last case, closure will be lost. Why is that?
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This is the exact definition and I've summarized it, as I understand it above. Why is it, that for elements in the third subspace, closure will be lost? Wouldn't you still get another vector (when you add two vectors in that subspace), that's still a linear combination of the vectors in the first two subspaces? So, if the first subspace is of all vectors in the x_hat direction and the second subspace is of vectors in the y_hat direction. Then the third subspace pretty contains all the vectors in 2D and when you add two of them, it will give you another, that can always be written as a linear combination of x_hat and y_hat. What am I missing here?
 
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Phys12 said:
Summary:: Definition 12 in Principles of Quantum Mechanics, it says that when you have a subspace of vectors in 1 dimension, and another subspace of vectors in another dimension and finally a 3rd subspace with the linear combination of the first two; in the last case, closure will be lost. Why is that?

View attachment 266632

This is the exact definition and I've summarized it, as I understand it above. Why is it, that for elements in the third subspace, closure will be lost? Wouldn't you still get another vector (when you add two vectors in that subspace), that's still a linear combination of the vectors in the first two subspaces? So, if the first subspace is of all vectors in the x_hat direction and the second subspace is of vectors in the y_hat direction. Then the third subspace pretty contains all the vectors in 2D and when you add two of them, it will give you another, that can always be written as a linear combination of x_hat and y_hat. What am I missing here?

I think this is a question about English, not math. "But for the elements (3)" should be read as "If not for the elements (3)".
 
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George Jones said:
I think this is a question about English, not math. "But for the elements (3)" should be read as "If not for the elements (3)".
Ah, I see, thanks! LOL
 

1. What is closure in the subspace of linear combinations of vectors?

Closure in the subspace of linear combinations of vectors refers to the property of a set of vectors to contain all possible linear combinations of those vectors. In other words, if we take any two vectors from the set and combine them using scalar multiplication and addition, the resulting vector will also be in the set.

2. Why is closure important in linear algebra?

Closure is important because it allows us to perform operations on vectors and still stay within the same subspace. This is crucial for solving systems of linear equations, finding bases for vector spaces, and other important applications in linear algebra.

3. How can we determine if a set of vectors has closure in a subspace?

To determine if a set of vectors has closure in a subspace, we can use the span and linear independence properties. If the set of vectors spans the subspace and is linearly independent, then it has closure in that subspace.

4. Can a set of vectors have closure in one subspace but not in another?

Yes, a set of vectors can have closure in one subspace but not in another. This is because closure is dependent on the specific subspace in question and the set of vectors may not satisfy the necessary conditions for closure in a different subspace.

5. How does closure relate to the dimension of a subspace?

The dimension of a subspace is directly related to closure. If a set of vectors has closure in a subspace, then the dimension of that subspace is equal to the number of vectors in the set. In other words, the number of vectors in a set with closure is the minimum number of vectors needed to span that subspace.

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