Proving Inequalities Involving Vector Space Dimensions

In summary, the conversation is about proving two inequalities involving subspaces U and W of a vector space V with dimension n. The first inequality is dimV >= dim(U + W) and the second is dim(U + W)>= dimU and dim(U + W)>= dimW. The speaker is looking for hints in proving these inequalities and presents their own solution, but also asks if it is a regular proof and if dimU + dimW is necessarily smaller or equal to dimV. The expert summarizer dismisses the complicated solution and provides a simpler approach using the fact that U + W is a subspace of V. A bonus question is also given to prove that dim U + dim W = dim(U + W) + dim(U
  • #1
Marin
193
0
Hi all!

I´m trying to prove following two inequalities but I somehow got stuck:

U, W are subspaces of V with dimV = n

1) dimV >= dim(U+W)

2) dim(U+W)>=dimU and dim(U+W)>=dimW


Could you give me some hints?

thanks in advance!
 
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  • #2
Just notice that if U and W are subspaces of V, then U+W is too. So pick a basis, and see what you can do with it. The same principle works for the second part also
 
  • #3
ok, let u_1,...,u_n be a basis of U and w_1,...,w_m be a basis of W.

Every vector x of U+W can be therefore expressed as a lin combination of some u_1,..u_n,w_1,..,w_n , which implies that u_1,..u_n,w_1,..,w_n is a generating set of U+W. But we know that the basis of U+W must be a minimal generating set, so letting p be its dimension this yields: p<=m+n, or dim(U+W)<= dimU + dimV. Now we consider w element of W, which we also find in W+U, but cannot be spanned by u_1,...,u_n only, so dimW<=dim(W+U), where W is a subspace of W+U. but W+U is also a subspace of V, so in the end, we have

dim(U+W)<= dimV

is this a regular proof, or I did something wrong?

and another question: dimU + dimW should not necesserily be smaller or equal to dimV, should it?
 
  • #4
You're making it much more complicated than it needs to be.

Since U + W is a subspace of V, it follows immediately that dim(U + W) ≤ dim V. The same idea works for the second part.

As for your last question, dim U + dim W is not necessarily less than or equal to dim V (take U = V and W = V, for example).

Here's a bonus question for you: Show that dim U + dim W = dim(U + W) + dim(U ∩ W).
 

What is a vector space?

A vector space is a mathematical concept that describes a collection of objects, called vectors, that can be added together and multiplied by scalars (usually numbers) to produce new vectors. It is a fundamental concept in linear algebra and has applications in many fields, such as physics, engineering, and computer science.

What is the dimension of a vector space?

The dimension of a vector space is the number of vectors in a basis, which is a set of linearly independent vectors that span the entire space. It determines the number of coordinates needed to uniquely describe any vector in the space. For example, the dimension of a three-dimensional space is 3, as three linearly independent vectors are needed to span the entire space.

How do you find the dimension of a vector space?

To find the dimension of a vector space, you can use the rank-nullity theorem, which states that the dimension of a vector space is equal to the sum of the rank of its basis and the dimension of its nullspace. The rank is the number of linearly independent vectors in the basis, and the nullspace is the set of vectors that are mapped to zero by the linear transformation. Alternatively, you can use Gaussian elimination to find the basis vectors and count the number of linearly independent vectors.

What is the difference between a vector space and a subspace?

A vector space is a collection of vectors that satisfies certain properties, such as closure under addition and scalar multiplication. A subspace is a subset of a vector space that also satisfies these properties. In other words, a subspace is a smaller vector space within a larger vector space. The dimension of a subspace is always less than or equal to the dimension of its parent vector space.

Can a vector space have an infinite dimension?

Yes, a vector space can have an infinite dimension, meaning that the number of vectors in its basis is infinite. Examples of such vector spaces include the space of all real numbers and the space of all polynomials. These infinite-dimensional vector spaces have important applications in mathematics and physics.

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