Find a basis of a linear system

In summary, the OP found a basis for a linear system and verified that it was correct. He also found that the matrix A is determinant-correct.
  • #1
Kavorka
95
0
Find a basis for the solution space of the linear system

x1-x2-2x3+x4 = 0
-3x1+3x2+x3-x4 = 0
2x1-2x2+x3 = 0

I created a matrix (not augmented, will be 0 on right side no matter what row operations) and brought it to reduced echelon form. x2 and x4 were free variables and I set them to the variables t and s respectively. I found that:

x1 = t - (1/5)s
x3 = (-2/5)s

All of this I'm fairly certain I did right, I just wanted to make sure I am stating the basis correctly because this is the first problem I've done where I've found a basis for a linear system. Stating all the x's in terms of s and t I get (forgive weird formatting not sure how to do matrices on here):

[x1]\\\\\\[1]\\\\\\\[-1/5]
[x2]\\=\t\[1]\+\s\[0]
[x3]\\\\\\[0]\\\\\\\[-2/5]
[x4]\\\\\\[0]\\\\\\\[1]

Forming the 2 dimensional basis:
[1] [-1]
[1] [0]
[0] [-2]
[0], [5]
 
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  • #2
Kavorka said:
Find a basis for the solution space of the linear system

x1-x2-2x3+x4 = 0
-3x1+3x2+x3-x4 = 0
2x1-2x2+x3 = 0

I created a matrix (not augmented, will be 0 on right side no matter what row operations) and brought it to reduced echelon form. x2 and x4 were free variables and I set them to the variables t and s respectively. I found that:

x1 = t - (1/5)s
x3 = (-2/5)s

All of this I'm fairly certain I did right, I just wanted to make sure I am stating the basis correctly because this is the first problem I've done where I've found a basis for a linear system. Stating all the x's in terms of s and t I get (forgive weird formatting not sure how to do matrices on here):

[x1]\\\\\\[1]\\\\\\\[-1/5]
[x2]\\=\t\[1]\+\s\[0]
[x3]\\\\\\[0]\\\\\\\[-2/5]
[x4]\\\\\\[0]\\\\\\\[1]

Forming the 2 dimensional basis:
[1] [-1]
[1] [0]
[0] [-2]
[0], [5]
You can check this for yourself. If A is the matrix of coefficients of your system above, and x1 and x2 are your two basis vectors, verify that Ax1 = 0 and that Ax2 = 0. If either of these doesn't come out to 0, then definitely you have made a mistake. If they both come out to zero, then A times any linear combination of the two vectors will also be zero.

Edit: you have a sign error in your second basis vector.
 
Last edited:
  • #3
You have four equation in three unknowns so one would initially expect the solution space to be 4- 3= 1 dimensional. Of course, if the equations are not independent that may not be true. However, I believe these are independent so your answer cannot be correct.
 
  • #4
HallsofIvy said:
However, I believe these are independent so your answer cannot be correct.
The three equations are dependent. The OP has a sign error in one of his basis vectors.
 
  • #5
Oops. Thanks.
 
  • #6
HallsofIvy said:
Oops. Thanks.
Happens to us all...
 
  • #7
From latexhelp:
Matrices: A matrix can easily be typed using the matrix, pmatrix, bmatrix or vmatrix environments. For example, the pmatrix environment starts with \bеgin{pmatrix} and ends with \еnd{pmatrix}. Columns are separated by & and rows are separated by \\ . Here is an example:

https://www.physicsforums.com/file://\\begin{pmatrix }
1 & 2 & 3 & 4\\
a & b & c & d\\
x & y & z & w
\end{pmatrix}


This yields
$$
\begin{pmatrix}
1 & 2 & 3 & 4\\
a & b & c & d\\
x & y & z & w
\end{pmatrix}
$$
The matrix, bmatrix and vmatrix environments produce similar results. Only the delimiters are different. See this web page for more examples.
 
Last edited by a moderator:
  • #8
I like the bmatrix environment, which uses brackets around the matrix rather than big parentheses.

https://www.physicsforums.com/file:////begin%7Bpmatrix }
1 & 2 & 3 & 4\\
a & b & c & d\\
x & y & z & w
\end{bmatrix}


This yields
$$
\begin{bmatrix}
1 & 2 & 3 & 4\\
a & b & c & d\\
x & y & z & w
\end{bmatrix}
$$

The vmatrix environment is useful for rendering determinants.
\begin{vmatrix]
1 & 2 & 3\\
a & b & c\\
x & y & z
\end{vmatrix}
which renders as
$$
\begin{vmatrix}1 & 2 & 3\\
a & b & c\\
x & y & z
\end{vmatrix}$$
 
Last edited by a moderator:
  • #9
Thanks everyone! I noticed the sign error, and when multiplying my basis vectors by the original matrix I get a null matrix in both cases. I am just wondering, in back-checking your answer to say positively that your basis is correct, is this the only step you need to perform?
 
  • #10
I seem to remember you also need to show the basis vectors are independent, which in this case is a piece of cake.
 

Related to Find a basis of a linear system

1. What is a basis of a linear system?

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

2. How do you find a basis of a linear system?

To find a basis of a linear system, you can use the Gaussian elimination method or the reduced row echelon form method. Both methods involve performing row operations on the augmented matrix of the linear system until it is in a form where the basis vectors can be easily identified.

3. Why is finding a basis of a linear system important?

Finding a basis of a linear system is important because it helps in solving systems of linear equations, finding the rank and nullity of a matrix, and determining the dimension of a vector space. It also allows for a more efficient representation of vectors in a vector space.

4. Can a linear system have more than one basis?

Yes, it is possible for a linear system to have more than one basis. This is because there can be multiple sets of linearly independent vectors that span the same vector space. However, all bases for a given vector space will have the same number of basis vectors, known as the dimension of the vector space.

5. How do you know if a set of vectors is a basis for a linear system?

To know if a set of vectors is a basis for a linear system, you can check if the vectors are linearly independent and if they span the entire vector space. If both conditions are satisfied, then the set of vectors is a basis for the linear system.

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