Linear Algebra: The vector space R and Rank

In summary, two m x n matrices A and B are equivalent (written A ~e B) if there exist invertible matrices U and V (sizes m x m and n x n) such that A = UBV. To prove the properties of equivalence, part i) states that A ~e A for all m x n matrices A, which can be shown by setting A = UAV and using the fact that (UA)V = UAV = A. Part ii) states that if A ~e B, then B ~e A, which follows directly from the definition of equivalence. Lastly, part iii) states that if A ~e B and B ~e C, then A ~e C, which can be shown by combining the
  • #1
rad0786
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Two m x n matrices A and B are called EQUIVALENT (writen A ~e B if there exist invertible matracies U and V (sizes m x m and n x n) such that A = UBV
a) prove the following properties of equivalnce
i) A ~e A for all m x n matracies A
ii) If A ~e B, then B ~e A
iii) A ~e B and B~e C, then A~e C

--------------

Lets just do part i) for now...

It seems ovicus that "A ~e A for all m x n matracies A"... but.. here's how i would do it

A = m x n
U = m x m
V = n x n

So

A = UAV
A = (UA)V UA = (m X m)(m X n) = (m X n)
A = (UA)(V) UAV = (m X n)(n x n) = (m x n)
A = A

How does that sound? Is that how you prove this? Or do i have the wrong idea?

Please help

thanks
 
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  • #2
What is your idea? How can A=m x n? That makes no sense. A is an mxn matrix, but not equal to m x n, whatever that means.
Find U and V, invertible matrices such that A=UAV, that's all you need to do.
You can't start by setting A=UAV, as you do, since that is assuming the answer. I don't even know what your argument is trying to do since you haven't used any words to explain any of your steps.
As for the other two: if you actually write out what you need to show, and what you are given, then it is a simple manipulation of matrices.
 

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of objects called vectors, along with operations such as addition and scalar multiplication. In linear algebra, the real number system R is often used as the underlying field for vector spaces.

2. How is the vector space R defined?

In linear algebra, the vector space R is defined as the set of all real numbers, along with two operations: addition and multiplication. Addition of two real numbers results in another real number, and multiplication of a real number by a scalar results in another real number.

3. What is the rank of a vector space?

The rank of a vector space is the number of linearly independent vectors in that space. In other words, it is the maximum number of vectors that can be combined to form any other vector in the space. The rank of the vector space R is infinite, as there is no limit to the number of linearly independent real numbers.

4. How is the rank of a matrix related to the rank of its corresponding vector space?

The rank of a matrix is equal to the rank of its corresponding vector space, as the columns of the matrix can be seen as vectors in the vector space. The rank of a matrix is also equal to the number of linearly independent columns in the matrix.

5. Can the rank of a vector space be greater than the dimension of the space?

No, the rank of a vector space cannot be greater than the dimension of the space. This is because the dimension of a vector space represents the maximum number of linearly independent vectors that can span the space, and the rank cannot exceed this number.

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