Weird linear algebra question

In summary: And then if you have a matrix times a vector, that goes from a matrix space to a vector space! So in summary, the vector operations in one ℝ space can indeed create vectors in another ℝ space, as long as the linear transformation allows for it.
  • #1
daiviko
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So, I'm studying for my linear algebra midterm and I came up with kind of an interesting question that I pose to all of you brilliant people on physics forums.

Let's say you have a linear transformation T(x)=Ax, with A being an nxm matrice. Apparently, for this equation to hold, x must be a member of ℝm.

Maybe this is a ******** argument but if ℝm-1 is a subset/subspace (forgot the exact terminology) of ℝm then wouldn't the vector (2,1) in ℝ2 be (2,1,0) in ℝ3? vector operations with vectors in ℝm (or at least as far as I know) can't create vectors in ℝm+1, right?

I have no idea if I really made my question clear at all, but I'm curious to hear what you guys have to say regardless.
 
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  • #2
daiviko said:
So, I'm studying for my linear algebra midterm and I came up with kind of an interesting question that I pose to all of you brilliant people on physics forums.

Let's say you have a linear transformation T(x)=Ax, with A being an nxm matrice. Apparently, for this equation to hold, x must be a member of ℝm.

Maybe this is a ******** argument but if ℝm-1 is a subset/subspace (forgot the exact terminology) of ℝm then wouldn't the vector (2,1) in ℝ2 be (2,1,0) in ℝ3?

Not necessarily. What's to say the vector in ℝ3 isn't (0, 2, 1) or (2, 0, 1)? In fact, it could be infinitely many different things, even using the same basis. Saying that ℝ2 is a subset of ℝ3 is analogous to saying that a given plane in a 3-D space is a subset of that 3-D space (granted it has to have some additional properties to be a subspace, like including zero). There are infinitely many different planes in ℝ3 that would qualify as a subspace, and any point in ℝ3 is inside a plane, so really (2,1) could represent infinitely many different points in ℝ3
vector operations with vectors in ℝm (or at least as far as I know) can't create vectors in ℝm+1, right? .

Yes they can! What if T(x) = Ax, where A is a matrix and x is a scalar? That goes from ℝ to a matrix space!
 
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What is linear algebra?

Linear algebra is a branch of mathematics that deals with the study of linear equations and their representations in vector spaces. It involves the manipulation and analysis of vectors, matrices, and linear transformations.

Why is linear algebra important?

Linear algebra is used in various fields of science and engineering, including physics, computer science, and economics. It provides a powerful framework for solving complex problems and understanding the relationships between different variables in a system.

What are the basic concepts of linear algebra?

Some of the basic concepts of linear algebra include vectors, matrices, linear transformations, and vector spaces. Other important concepts include eigenvalues and eigenvectors, determinants, and systems of linear equations.

What is the difference between a vector and a matrix?

A vector is a one-dimensional array of numbers, while a matrix is a two-dimensional array of numbers. Vectors are often used to represent quantities that have magnitude and direction, while matrices can be used to represent transformations or systems of equations.

How is linear algebra used in data analysis?

Linear algebra is a fundamental tool for data analysis, especially in the fields of machine learning and data science. It is used to manipulate and analyze large datasets, perform dimensionality reduction, and build predictive models based on linear relationships between variables.

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