How do I determine the dimension of a space in linear algebra?

In summary, when finding the dimension of a particular space, one needs to find a basis for the space and then check for linear independence. The number of elements in this basis will be the dimension of the space. It is important to note that a basis is by definition linearly independent, so it cannot be checked for linear dependency. Instead, one can find a spanning set and then determine the maximal number of linearly independent elements in it, either through inspection or by using row reduction on a matrix representation of the set.
  • #1
mattmns
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There are these questions in the book that ask us to find the Dimension of a particular space. Do I just find a basis for the space, and then the number of elements in that basis is the dimension for the space? Or is there some trick to finding the dimension? Thanks!-----------
For example, the first one the book asks is: Find the dimension of 2x2 matricies. So a basis for 2x2 matricies is the following set:

[tex]\left\{\left(\begin{array}{cc}1&0\\0&0\end{array}\right), \left(\begin{array}{cc}0&1\\0&0\end{array}\right), \left(\begin{array}{cc}0&0\\1&0\end{array}\right), \left(\begin{array}{cc}0&0\\0&1\end{array}\right)\right\}[/tex]

And this basis has 4 elements, so the dimension of 2x2 matricies is 4.
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Is that basically how these problems go? Thanks.
 
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  • #2
mattmns said:
Do I just find a basis for the space, and then the number of elements in that basis is the dimension for the space?

Dimension if the number of element in a basis whose elements are linearly independent. So find a basis, check for linear dependancy. If it is lin. dep., trash the "spare" elements of your basis.
 
  • #3
Point of order: a basis is by definition linearly independent. You cannot 'find a basis then check for linear dependency'. Find a spanning set then find the maximal number of linearly independent elements in it, either by inspection or by turning it into a matrix question and using row reductions to put it in echelon form.
 

FAQ: How do I determine the dimension of a space in linear algebra?

What is a dimension in linear algebra?

A dimension in linear algebra refers to the number of independent vectors needed to span a vector space. It represents the number of coordinates needed to uniquely identify a point in that space.

How is the dimension of a vector space determined?

The dimension of a vector space can be determined by finding the maximum number of linearly independent vectors in that space. This can be done by creating a matrix with the vectors as columns and using Gaussian elimination to reduce the matrix to its row echelon form. The number of non-zero rows in the reduced matrix will be the dimension of the vector space.

Can a vector space have more than one dimension?

Yes, a vector space can have more than one dimension. In fact, most vector spaces have more than one dimension. For example, the three-dimensional space we live in has three dimensions (length, width, and height).

What is the difference between dimension and rank in linear algebra?

Dimension and rank are closely related concepts in linear algebra, but they are not the same. The dimension of a vector space refers to the number of independent vectors needed to span that space, while the rank of a matrix refers to the number of linearly independent columns or rows in that matrix. In other words, the dimension of a vector space is the number of vectors needed to span it, while the rank of a matrix is the number of columns or rows needed to span its column space or row space, respectively.

Can a vector space have infinite dimensions?

Yes, a vector space can have infinite dimensions. This is often the case in functional analysis, where the dimension of a vector space can represent the number of basis functions needed to span the space. In these cases, the dimension is typically denoted by the symbol ∞ (infinity).

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