Are Planes Passing Through the Origin Vector Spaces or Subspaces?

In summary: Therefore, subspace Rn and Euclidean vector space are the same thing. In summary, a subspace is always a space that is immersed in a bigger space. A two-dimensional plane in a 3-dimensional space is not a subspace unless it passes through the origin. Rn and Rm are both vector spaces, and Rn is also an Euclidean vector space if its structure is defined as dot product.
  • #1
Cinitiator
69
0

Homework Statement


Is a set of n-tuples which must respect the conditions of closure under addition and closure under scalar multiplication a vector space or a vector subspace?

That is, in a 3-dimensional space, are planes which pass by the origin considered to be subspaces of the 3-dimensinal space in question? Or are they considered to be vector spaces?

The place where I was reading about it said that subspace of R n and Euclidean vector space are the same thing, but I'm not sure whether it's true or not. I probably misunderstood something.

Homework Equations


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The Attempt at a Solution


Posting here, as well as Googling.
 
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  • #2
A subspace is always space. What makes a space a subspace is that it is "immersed" in some bigger space. An Euclidean plane is a vector space in its own right, but a subspace in 3D space, etc.
 
  • #3
Cinitiator said:

Homework Statement


Is a set of n-tuples which must respect the conditions of closure under addition and closure under scalar multiplication a vector space or a vector subspace?

That is, in a 3-dimensional space, are planes which pass by the origin considered to be subspaces of the 3-dimensinal space in question? Or are they considered to be vector spaces?

The place where I was reading about it said that subspace of R n and Euclidean vector space are the same thing, but I'm not sure whether it's true or not. I probably misunderstood something.

Homework Equations


-

The Attempt at a Solution


Posting here, as well as Googling.

A two-dimensional plane in a 3-dimensional space is not, itself, a subspace unless it passes through the origin. If it misses the origin entirely, then closure under addition and/or multplication by a scalar fails.

RGV
 
  • #4
Ray Vickson said:
A two-dimensional plane in a 3-dimensional space is not, itself, a subspace unless it passes through the origin. If it misses the origin entirely, then closure under addition and/or multplication by a scalar fails.

RGV

Thanks for your help.

I have another question: Would it be correct to say that R3 and R2 are both vector spaces?
 
Last edited:
  • #5
Yes, R^3 and R^2 are both vector spaces.

R^n for every positive integer is also a vector space.

Check out the Linear Algebra wikibook, and read up on the chapter on Vector Spaces.
http://en.wikibooks.org/wiki/Linear_Algebra
 
  • #6
Cinitiator said:
The place where I was reading about it said that subspace of R n and Euclidean vector space are the same thing, but I'm not sure whether it's true or not. I probably misunderstood something.

Subspace Rn is an Euclidean vector space iff, in addition to satisfying closure under addition and multiplication with the inclusion of zero element, its structure (inner product) is defined as dot product.
 

Related to Are Planes Passing Through the Origin Vector Spaces or Subspaces?

1. 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 use of mathematical structures such as matrices, vectors, and linear transformations to solve problems in various fields, including physics, engineering, computer science, and economics.

2. What are the applications of linear algebra?

Linear algebra has a wide range of applications in different fields, such as data analysis, computer graphics, cryptography, and quantum mechanics. It is used to solve systems of equations, perform data analysis and dimension reduction, and model real-world problems.

3. What are the basic concepts in linear algebra?

The basic concepts in linear algebra include vectors, matrices, linear transformations, determinants, and eigenvalues and eigenvectors. These concepts are used to represent and solve linear equations and systems, as well as to perform operations such as matrix multiplication and inversion.

4. What is the difference between a vector and a matrix?

A vector is a one-dimensional array of numbers or values, while a matrix is a two-dimensional array of numbers or values. Vectors are often used to represent points or directions in space, while matrices are used to represent linear transformations or systems of linear equations.

5. How is linear algebra used in machine learning?

Linear algebra is a fundamental tool in machine learning, as it is used to represent and manipulate data in the form of vectors and matrices. It is used to perform operations such as gradient descent, which is a common optimization algorithm used in machine learning. Additionally, many machine learning algorithms are based on linear algebra concepts, such as principal component analysis and singular value decomposition.

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