Matrices Math: What is a Determinant?

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In summary, the determinant of a square matrix indicates how the transformation applied to the matrix affects the dimensions of the transformed object.
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yuenkf
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  1. sorry. erm.. what does the determinant means or functions in matrices , math? thanks..
 
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Example:
Define a square matrix(just for simplicity) A with a1 and b1 in the top row and and a2 and b2 in the second row and a in the first columm and bs in the second columm.The determinant of the matrix would be
det(A)=(a1*b2-b1*a2)
Which you could also write with two vertical lines, like the abs value.
 
  • #3
why we want to get the determinant?
 
  • #4
One can show that linear equation systems have solutions for exapmle. It comes n handy all the time. Another example would be that one can write many equation in quantum mechanocs much more elegant that way.
 
  • #5
yuenkf said:
why we want to get the determinant?
There's a theorem that says that the following statements about an arbitrary ##n\times n##-matrix ##A## are equivalent:

(a) ##\det A\neq 0##.
(b) A is invertible
(c) The rows of A are linearly independent elements of ##\mathbb R^n##.
(d) The columns of A are linearly independent elements of ##\mathbb R^n##.

So if you're interested in finding out if any of the statements (b)-(d) is true, you just compute ##\det A##.

The case n=2 is easy to prove. If you define a,b,c,d by ##A=\begin{pmatrix}a & b\\ c & d\end{pmatrix}## you can show that if ##A## has an inverse, it has to be
$$\frac{1}{ad-bc}\begin{pmatrix}d & -b\\ -c & a\end{pmatrix}.$$ So if ##ad-bc## (the determinant of A) is zero, then ##A## isn't invertible.
 
  • #6
yuenkf said:
why we want to get the determinant?

In applications, the determinant can used (in 2-D) to compute the "oriented" area of a parallelogram whose sides are given by 2 row vectors. In higher dimensions, it can be used to compute an "oriented" volume.

Thinking a square matrix M applied as a linear transformation to a vector x, T(x) = Mx, the determinant of M indicates how T expands or contracts volumes (when T is applied to each vector defining a side of a pareallelepiped to produce a transformed parallelepiped).
 
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FAQ: Matrices Math: What is a Determinant?

What is a determinant?

A determinant is a mathematical concept that is used to determine the properties of a square matrix. It is a single value that can be calculated from the elements of the matrix and is commonly denoted by |A| or det(A). It is used to solve systems of linear equations, find the inverse of a matrix, and determine whether a matrix is invertible or not.

How is a determinant calculated?

The calculation of a determinant depends on the size of the matrix. For a 2x2 matrix, it is calculated by multiplying the elements in the first row and column and subtracting the product of the elements in the second row and column. For larger matrices, the process can become more complex, but there are various methods such as the cofactor expansion method or the Gaussian elimination method that can be used to calculate determinants.

What is the significance of a determinant?

Determinants have many applications in mathematics and science. They are used to solve systems of linear equations, find the area or volume of a parallelogram or parallelepiped, and determine whether a matrix is invertible or not. In physics, they are used to calculate the moment of inertia of objects and in computer graphics, they are used to transform and rotate objects in three-dimensional space.

Can a determinant be negative?

Yes, a determinant can be negative. The sign of a determinant depends on the number of row or column swaps that are performed during its calculation. If an odd number of swaps are performed, the determinant will be negative, and if an even number of swaps are performed, the determinant will be positive.

What is the relationship between determinants and eigenvalues?

Determinants and eigenvalues are closely related concepts. The determinant of a square matrix is equal to the product of all its eigenvalues. This relationship is used in many areas of mathematics, such as in the calculation of characteristic polynomials and the diagonalization of matrices.

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