Proving Determinant of Matrix Unchanged by Adding Columns

In summary, the conversation discusses how row operations do not change the determinant of a matrix, and how this can be proven using examples and geometric reasoning. Additionally, the conversation introduces a matrix A that is formed by adding a multiple of one row to another, and shows that its determinant is equal to the determinant of the original matrix.
  • #1
Unusualskill
35
1
Any1 can explain in an easy way?I know is something to do with the facts that row operations do not change the determinant of a matrix and also determinant of A=determinant of A transpose.But just dk how to prove in full sentence. Appreciate any1 help.
 
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  • #2
I'll explain by example. Let's say u is some vector in R^2 and v is a multiple of it. Then suppose we form a 2 by 2 matrix (u, v) by taking u as the first column and v as the second. The determinant is 0 because the columns are linearly dependent. We can write it as
det(u, v) = 0 = det(v, u).

Now, suppose we have a matrix (v, w). We want to see what happens when we add a multiple of v to another column, like this: (v, w+u). We can use the fact that the determinant is linear in each variable.

det(v, w + u) = det(v, w) + det(v, u) = det(v, w) + 0 = det(v,w).

The same thing will happen if we look at n by n matrices because you're adding something that's going to be linearly dependent.

Geometrically, this is like the geometry theorem that you can shear a parallelogram without changing its area. It's always base times height, so if you don't change the height, it stays the same. Try to see how that relates to my argument by drawing the parallelogram spanned by some vectors v and w, then the one spanned by v and w+u. Remember that the meaning of the determinant is that it is the signed area of this parallelogram (or volume in higher dimensions).
 
  • #3
Suppose ##M## is any ##n \times n## matrix, and ##1 \leq i,j \leq n## with ##i \neq j##.

If ##I## is the ##n \times n## identity matrix and ##J## is the ##n \times n## matrix with a ##1## in row ##i##, column ##j##, and ##0## everywhere else, then define ##A = I + J##. It's easy to check that ##AM## is the matrix formed by replacing the ##i##'th row of ##M## with the sum of the ##i##'th and ##j##'th rows. Note that ##A## is either upper triangular (if ##i > j##) or lower triangular (if ##i < j##), so its determinant is the product of its diagonal elements, which are all ##1##. Therefore ##\det(A) = 1##. Consequently, ##\det(AM) = \det(A)\det(M) = \det(M)##.
 

What is a determinant of a matrix?

A determinant is a numerical value that can be calculated from a square matrix. It represents the scaling factor of the transformation represented by the matrix.

Why would we want to prove that the determinant of a matrix remains unchanged by adding columns?

This proof is important because it shows that the determinant is a property of the matrix itself, not just the numbers within it. It allows us to make generalizations about matrices and their determinants.

What is the process for proving that the determinant of a matrix is unchanged by adding columns?

The proof involves using the properties of determinants, such as linearity and the fact that the determinant of an identity matrix is 1. By using these properties, we can show that the determinant of the original matrix and the matrix with added columns are equal.

Does this proof only apply to square matrices?

Yes, this proof only applies to square matrices, as determinants are only defined for square matrices.

What are some real-world applications of this proof?

This proof is used in various fields of science and mathematics, such as engineering, physics, and computer science. It is especially useful in applications involving linear transformations and systems of linear equations.

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