Finite Dimensional Vector Spaces - Paul R. Halmos

In summary, Steve was asking if a solutions manual exists for the problems at the end of each section at FDVS - Halmos. The response was that the easiest way to check your work is to see if your answer satisfies the conditions in the problem and if you are unable to tell, then there may be something missing in your understanding of the subject. Steve then asked if it is possible to have a vector space of an infinite set with a larger cardinality than the field on which it is defined. The answer is yes, and it can even have an infinite basis. The conversation then shifted to discussing distributivity on subspaces and how it is not always true, but can be proven to be true in certain cases. The method
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Hello,

I am currently working out of FDVS - Halmos, and I was wondering if a solutions manual (for the problems at the end of each section) existed?

I'd like to be able to check my work.

Thanks,

Steve

P.S Sorry if this is an inappropriate post for this section.
 
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  • #2
The easiest way to check your work, for any problem, is to see if your answer satisifies the conditions in the problem. That is typically far easier than solving the problem initially.
 
  • #3
in general, if you are unable to tell whether your answer to a problem is correct, there is something you are missing in your grasp of the subject. take as a new exercise the problem of understanding what you are doing well enough to decide this for yourself, whether it is checking a numerical answer by a different way of computing it, or just understanding the reasoning of a proof.
 
  • #4
True. However, for problems I am unable to solve and to see how my solutions compare to what the author had in mind, a solutions guide would be ideal. Does a solutions manual exist?

Thanks,

Steve
 
  • #5
you keep asking the same question. the point is if you need it repeated, solutions manuals are of no benefit to anyone. the only use i make of the one in my office is as a doorstop.
 
  • #6
Interesting. Perhaps you can help me with a problem I am having?

Is it possible to have a vector space of an infinite set with a larger cardinality then the field on which it is defined? (example: Real numbers defined over the field of rationals) I understand that the field provides scalars such that the definition of vector spaces is satisfied.

What I am having trouble with: since the scalars help define the span of the vector space, and the rationals have smaller cardinality then the reals, would you 'run out' of rationals to describe the reals with?

Sorry if this isn't exactly articulate, but this stuff is new to me.

Steve
 
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  • #7
That's a very good question. Something you might have neglected to notice is that the basis can be very large too -- uncountable, even. So it's perfectly conceivable that there are infinite vector spaces over even finite fields, and in fact this is possible.

Now, R is certainly a vector space over Q -- however, it cannot have a finite basis, because of the reasons you mentioned (R is simply too large). So, if a basis were to exist, then it would be infinite (uncountable in fact). Proving the existence of such a basis is another matter, but maybe you've seen already that every vector space has a basis.
 
  • #8
Oh ok. That makes sense to me. Thanks.

Well I have your attention;

In general distributivity on subspaces is not true. It is easy to show that this is the case by counter-example. Halmos however then asks us to prove that:

for L,M and N that are subspaces on a vector space show that

L intersect ( M + (L intersect N)) = (L intersect M) + (L intersect N)

Because the addition operation on subspaces may yield a resulting set with more than n (n being the dimension of the whole vector space) elements, we know that if there are more than n elements, those extra elements are repeats of, or linear combinations of the other n elements in the set. So we then drop those extra (?), and this is why in general distributivity does not hold. (The only case it does being where they are all mutually exclusive?)

I think this is at the heart of the idea but for this proof I am not sure how to proceed?
 
  • #9
How do you usually show that two sets are equal? You prove that each contains the other! Have you tried that here?
 

FAQ: Finite Dimensional Vector Spaces - Paul R. Halmos

1. What is a finite dimensional vector space?

A finite dimensional vector space is a mathematical structure that consists of a set of objects called vectors, along with operations of addition and scalar multiplication. The number of vectors in the space is finite, and the vectors can be added and multiplied by scalar values to form new vectors.

2. What are the key properties of a finite dimensional vector space?

The key properties of a finite dimensional vector space include closure under addition and scalar multiplication, associativity and commutativity of addition, distributivity of scalar multiplication over addition, and the existence of an additive identity and inverse. These properties allow for the manipulation and combination of vectors within the space.

3. How do you determine the dimension of a finite dimensional vector space?

The dimension of a finite dimensional vector space is equal to the number of linearly independent vectors in the space. This can be determined by finding a basis for the space, which is a set of vectors that spans the entire space and is linearly independent. The number of vectors in the basis is the dimension of the space.

4. What is the significance of finite dimensional vector spaces in mathematics?

Finite dimensional vector spaces are important in mathematics because they provide a framework for representing and studying various mathematical objects, such as matrices, polynomials, and functions. They also have many applications in fields such as physics, engineering, and computer science.

5. How does Halmos' book cover finite dimensional vector spaces?

In his book "Finite Dimensional Vector Spaces", Paul R. Halmos provides a comprehensive introduction to the theory and applications of finite dimensional vector spaces. He covers topics such as vector operations, linear transformations, eigenvalues and eigenvectors, and inner product spaces. The book also includes numerous examples and exercises to help readers understand and apply the concepts presented.

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