Lin Alg - Find the basis and dimension

In summary, the homogeneous system has a solution space that is zero-dimensional, meaning the basis is empty and the only solution is the trivial one where x1, x2, and x3 are all equal to zero.
  • #1
jinksys
123
0
Find the basis and dimension of the following homogeneous system:
Code:
A =     |1 0 2| |x1|
        |2 1 3| |x2| = [0,0,0]
        |3 1 2| |x3|
My attempt:

Solving the coefficient matrix for RREF, I get the identify matrix.
So, x1=x2=x3=0 and the only solution is a trivial one.

Does that mean there is no basis(empty basis), or that the basis only contains the zero vector and has dimension zero?
 
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  • #2
The dimension is equal to the number of vectors in a basis, so if the solution space is zero-dimensional, the basis is empty.
 
  • #3
vela said:
The dimension is equal to the number of vectors in a basis, so if the solution space is zero-dimensional, the basis is empty.


Thanks for the help!
 

What is the definition of a basis in linear algebra?

A basis in linear algebra is a set of linearly independent vectors that span a vector space. This means that any vector in the space can be written as a unique combination of the basis vectors.

How do you determine if a set of vectors is a basis?

A set of vectors is a basis if they are linearly independent and span the vector space. This can be checked by setting up a system of equations and solving for the coefficients of the linear combination of the vectors. If the only solution is the trivial solution (all coefficients equal to 0), then the vectors are linearly independent and form a basis.

What is the dimension of a vector space?

The dimension of a vector space is the number of vectors in a basis for that space. This is also equal to the number of coordinates needed to uniquely specify any vector in that space.

How do you find the dimension of a vector space?

To find the dimension of a vector space, you can either find a basis for the space and count the number of vectors in the basis, or you can find the rank of the matrix representing the vectors in the space. The rank is equal to the number of linearly independent rows or columns in the matrix, which is also equal to the dimension of the space.

Can a vector space have more than one basis?

Yes, a vector space can have multiple bases. This is because there can be different sets of linearly independent vectors that span the same vector space. However, all bases for the same vector space will have the same number of vectors, which is equal to the dimension of the space.

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