Completing the square using a matrix in quadratic form

In summary, the conversation discusses completing the square using a symmetric matrix to define a given quadratic form. The method involves finding the eigenvalues and eigenvectors of the matrix and using them to reduce the form into a sum and difference of squares. This can also be done by finding the reduced row echelon form of the matrix and using it to complete the square, or by using a form of Cholesky Decomposition.
  • #1
PhizKid
477
1

Homework Statement


Complete the square using the symmetric matrix that defines the given quadratic form: ##x^2 - 4xy + 6xz + 2xt + 4y^2 + 2yz + 4yt + 5z^2 - 6zt - t^2## and write this quadratic as the sum and difference of squares after completing the square using the matrix.

The Attempt at a Solution



So first I found the 4x4 matrix, using x as the first column, y as the 2nd, z as the 3rd, and t as the 4th. Also x is 1st order, y is 2nd order, z is 3rd, and t is 4th:

http://i2.minus.com/inT7VWSkOu3GH.png

If I want this in reduced row form, I have to switch some rows, name the 2nd and 3rd rows. What implications will this have on my work in completing the square?

Also, I couldn't find anything online about how to complete the square using matrices.
 
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  • #2
You don't want to reduce it. You want to find the eigenvalues and eigenvectors of the matrix and those are not "preserved" by row operations.
 
  • #3
The assignment says to row eliminate into the row echelon form and use this row echelon form of the matrix to complete the square. Is there a method to complete the square using the row echelon form?
 
  • #4
PhizKid said:
The assignment says to row eliminate into the row echelon form and use this row echelon form of the matrix to complete the square. Is there a method to complete the square using the row echelon form?

If I were doing the problem I would write
[tex] Q_4(x,y,z,t) = x^2 - 4xy + 6xz + 2xt + 4y^2 + 2yz + 4yt + 5z^2 - 6zt - t^2[/tex]
as
[tex]Q_4(x,y,z,t) = u_1(x,y,z,t)^2 + Q_3(y,z,t)[/tex]
by noting that
[tex] S_n = a_{11}x_1^2 + 2 a_{12} x_1 x_2 + \cdots + 2 a_{1n} x_1 x_n \\
= \left( \sqrt{a_{11}}x_1 + \frac{a_{12}}{\sqrt{a_{11}}} x_2 +
\cdots + \frac{a_{1n}}{\sqrt{a_{11}}} x_n \right)^2 - S_{n-1}, [/tex]
where ##S_{n-1}## is a quadratic form not involving ##x_1##. It has the form
[tex] S_{n-1} = (a_{12}^2 /a_{11}) x_2^2 + \cdots + (a_{1n}^2/a_{11})x_n^2

+ 2 (a_{12}a_{13}/a_{11}) x_2 x_3 + \cdots + 2(a_{1,n-1}a_{1n}/a_{11})x_{n-1} x_n[/tex]
So,
[tex] u_1 = x - 2 y + 3z + t, [/tex]
and you can figure out what is ##Q_3(y,z,t)##. Do the same type of operation on ##Q_3##. If you never take the square root of a negative number throughout, you will reduce your quadratic form to a sum of squares. If you encounter the square root of a negative number at some point, you get a difference of (real) squares.

This method is attempting to write your 4x4 matrix ##A## in the form ##A = U^T U,## where ##U## is an upper-triangular matrix. I suppose you could regard ##U^T## as a matrix in row-echelon form, so I suppose that could be what the question is asking about.
 
  • #5
To do this by finding RREF, you want to use the form

EnT...E2TE1TDE1E2...En

Where D = diag(eigenvalues), which is found by solving En...E2E1AE1TE2T...EnT = ARREF. In order to find D, take the column & row reductions one at a time - noting that you have to do AEET for every row manipulation E. (E is an elementary matrix s.t. det(E) = 1)
 
  • #6
Honors LinAlg said:
To do this by finding RREF, you want to use the form

EnT...E2TE1TDE1E2...En

Where D = diag(eigenvalues), which is found by solving En...E2E1AE1TE2T...EnT = ARREF. In order to find D, take the column & row reductions one at a time - noting that you have to do AEET for every row manipulation E. (E is an elementary matrix s.t. det(E) = 1)

Using the method in post #4 (which is essentially Cholesky Decomposition, adapted to an indefinite matrix) you do not ever need to find eigenvalues, so is much faster and much more efficient, and possibly much more accurate as well.
 

1. What is the purpose of completing the square using a matrix in quadratic form?

The purpose of completing the square using a matrix in quadratic form is to solve quadratic equations quickly and efficiently. It is a method that involves using matrices to transform a quadratic equation into a perfect square trinomial, making it easier to solve for the unknown variable.

2. How do you complete the square using a matrix in quadratic form?

To complete the square using a matrix in quadratic form, you need to follow these steps:

  1. Write the quadratic equation in the form of ax² + bx + c = 0.
  2. Create a matrix A with the coefficients of x², x, and the constant term in the first row.
  3. Find the inverse of matrix A, denoted as A⁻¹.
  4. Multiply both sides of the equation by A⁻¹.
  5. Complete the square on the left side of the equation, leaving the right side as A⁻¹c.
  6. Factor the perfect square trinomial on the left side and simplify.
  7. Solve for the unknown variable.

3. Can completing the square using a matrix in quadratic form be used for all types of quadratic equations?

Yes, completing the square using a matrix in quadratic form can be used for all types of quadratic equations, including those with complex solutions. However, it may not always be the most efficient method for solving quadratic equations.

4. What are the benefits of using a matrix in completing the square for quadratic equations?

Using a matrix in completing the square for quadratic equations can make the process more systematic and less prone to errors. It also allows for the use of matrix operations, which can simplify the equation and make it easier to solve.

5. Are there any limitations to using a matrix in completing the square for quadratic equations?

One limitation of using a matrix in completing the square for quadratic equations is that it may not be the most efficient method for solving simple or factorable quadratic equations. In such cases, traditional methods such as factoring or the quadratic formula may be more straightforward and quicker. Additionally, this method may be more challenging to understand for those who are unfamiliar with matrices and their operations.

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