Studying Linear Algebra: Rn vs. Rm

In summary, the author mentions that sometimes vectors are described as being in Rn and sometimes in Rm, but does not explain why this is the case.
  • #1
zenith92
10
0
Hello, so I'm currently busy my (first) linear algebra course. We use the book Linear Algebra and it's Application third edition Update (red cover). I noticed that in sections 1.4 and section 1.5 (don't know about the rest yet) that they sometimes describe vectors that are in Rn and sometimes in Rm, now I don't know if there's supposed to be a difference or if they just randomly switch between these two letters. I couldn't think of any reasoning behind the different uses, so maybe someone here knows?

Example: A system of linear equations is said to be homogeneous if it can be written in the form Ax = 0, where A is an m x n matrix and 0 is the zero vector in Rm. Such a system Ax = 0 always has at least one solution, namely x = 0 (the zero vector in Rn).
 
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  • #2
If you have an equation like
A x = b
where A is an m x n matrix (in other words, A has m rows and n columns),
then x must be a vector with n elements and b must be a vector with m elements. Otherwise, the dimensions are not compatible and the matrix multiplication doesn't make any sense.
That is what "x is in Rm and 0 is in Rn" means.
 
Last edited:
  • #3
AlephZero said:
If you have an equation like
A x = b
where A is an m x n matrix (in other words, A has m rows and n columns),
then x must be a vector with n elements and b must be a vector with m elements. Otherwise, the dimensions are not compatible and the matrix multiplication doesn't make any sense.
That is what "x is in Rm and 0 is in Rn" means.

Oh wow, makes much more sense now thanks a lot. Now I have to go re-read some stuff, though haha
 

1. What is the difference between Rn and Rm in Linear Algebra?

Rn and Rm both refer to vector spaces in Linear Algebra. Rn represents an n-dimensional vector space, while Rm represents an m-dimensional vector space. This means that Rn has n components or dimensions, while Rm has m components or dimensions. In other words, Rn and Rm have different sizes or dimensions.

2. Why is it important to distinguish between Rn and Rm in Linear Algebra?

It is important to distinguish between Rn and Rm in Linear Algebra because they have different properties and operations. The size or dimension of a vector space affects the type of operations that can be performed on its vectors. For example, vectors in R3 (3-dimensional space) can be added, subtracted, and multiplied by a scalar, while vectors in R4 (4-dimensional space) cannot be multiplied by a scalar. Therefore, understanding the difference between Rn and Rm is crucial for correctly applying mathematical concepts in Linear Algebra.

3. How do I determine whether to use Rn or Rm in a Linear Algebra problem?

The choice between Rn and Rm depends on the context of the problem and the information given. If the problem involves vectors with a specific number of components or dimensions, then Rn or Rm can be determined accordingly. Additionally, if the problem involves operations that are only applicable to vectors of a certain size or dimension, then Rn or Rm can be chosen based on that. It is important to carefully analyze the problem and consider the properties and operations of Rn and Rm before making a decision.

4. Can Rn and Rm be used interchangeably in Linear Algebra?

No, Rn and Rm cannot be used interchangeably in Linear Algebra. As mentioned before, they have different properties and operations, and using them interchangeably can lead to incorrect solutions. For example, if a problem involves finding the dot product of two vectors, one in R2 and the other in R3, using Rn instead of R3 would result in an erroneous solution. Therefore, it is important to use Rn and Rm appropriately according to the problem at hand.

5. Is there a limit to the number of dimensions in Rn and Rm?

No, there is no limit to the number of dimensions in Rn and Rm. These vector spaces can have any number of dimensions, as long as they are finite. This means that Rn and Rm can have a finite number of components or dimensions, but not an infinite number. This is because infinite-dimensional vector spaces have different properties and are not considered in the context of Linear Algebra.

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