Minimum Absolute Value of a nxn Matrix Determinant

In summary, the question is about arranging a square n by n matrix with entries 1 to n^2 in a way that minimizes the absolute value of the determinant. The solution, without proof, is that when n >= 3, the minimum absolute value is 0.
  • #1
skymariner
2
0
The following matrix problem occurred to me. I figured out the answer and would like to pose the problem. It's easy but would be best for an undergrad math major. The question: Consider a square n by n matrix with entries 1, 2, ..., n squared. Find a way to arrange these entries so that the absolute value of the determinate of this matrix is a minimum.
 
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  • #2
"a square n by n matrix with entries 1, 2, ..., n squared"

could you explain this a little better? Do you mean that the matrix have entries 1,2,3,...,n^2, and do all appear and only once?
 
  • #3
Yes, the entries consist of all the numbers 1 to n^2 and each number occurs only once.
 
  • #4
Let's see it for a 2x2 matrix. Since a transposition of rows and columns does not change the value of the matrix, and a transposition of row or columns changes the sign, it is sufficient to consider how many different rows we can form. For a 2x2 matrix, we can form the first row in 3 ways (pairing one fixed element with the other 3 elements) and the second row can be formed in the 2 possible permutations of the remaining elements, so 3x2 = 6 possible determinants. Here they are:
[tex]
\left|\begin{array}{cc}
1 & 2 \\

3 & 4
\end{array}\right| = 4 - 6 = -2
[/tex]

[tex]
\left|\begin{array}{cc}
1 & 2 \\

4 & 3
\end{array}\right| = 3 - 8 = -5
[/tex]

[tex]
\left|\begin{array}{cc}
1 & 3 \\

2 & 4
\end{array}\right| = 4 - 6 = -2
[/tex]

[tex]
\left|\begin{array}{cc}
1 & 3 \\

4 & 2
\end{array}\right| = 2 - 12 = -10
[/tex]

[tex]
\left|\begin{array}{cc}
1 & 4 \\

2 & 3
\end{array}\right| = 3 - 8 = -5
[/tex]

[tex]
\left|\begin{array}{cc}
1 & 4 \\

3 & 2
\end{array}\right| = 2 - 12 = -10
[/tex]

I don't see how to generalize it at this point. I would think we need to add the biggest numbers [itex]n^{2}, (n - 1)^{2}, \ldots, n^{2} - n +1[/itex] along the main diagonal, then the next along the third to the main diagonal and start inserting the smallest elements along the odd diagonals.
 
  • #5
I don't want to spoil other people's fun with this so I'll give the answer without a proof:
When n >= 3, the minimum absolute value of the determinant is 0.
 

Related to Minimum Absolute Value of a nxn Matrix Determinant

What is the minimum absolute value of a nxn matrix determinant?

The minimum absolute value of a nxn matrix determinant is the smallest possible value that the determinant of a square matrix with n rows and n columns can have. It is typically denoted as det(A) and can be calculated using various methods such as Gaussian elimination or Cramer's rule.

How is the minimum absolute value of a nxn matrix determinant calculated?

The minimum absolute value of a nxn matrix determinant can be calculated using various methods such as Gaussian elimination or Cramer's rule. These methods involve manipulating the elements of the matrix and performing mathematical operations to determine the determinant value.

Why is the minimum absolute value of a nxn matrix determinant important?

The minimum absolute value of a nxn matrix determinant is important because it provides information about the properties of the matrix. It can indicate whether the matrix is invertible, singular, or has a zero determinant. It is also used in various mathematical applications and algorithms, such as solving linear equations and finding eigenvalues.

How does the size of the matrix affect the minimum absolute value of the determinant?

The size of the matrix, represented by the value of n, directly affects the minimum absolute value of the determinant. As the size of the matrix increases, the minimum absolute value of the determinant can also increase, decrease, or remain the same, depending on the values of the matrix elements and the method used to calculate the determinant.

What are some real-world applications of the minimum absolute value of a nxn matrix determinant?

The minimum absolute value of a nxn matrix determinant has various real-world applications, such as in engineering, physics, and economics. It is used in solving systems of linear equations, determining the stability of a physical system, and analyzing data in economic models. It is also used in computer graphics and image processing algorithms.

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