Definition of the Determinant of a Matrix

In summary, the conversation discusses different ways of defining the determinant, such as through its properties as a multilinear alternating function, or as the product of eigenvalues of a linear transformation. The motivation behind these definitions and their relation to each other is also explored. Ultimately, the determinant can be seen as a factor of signed scale of n-parallelograms embedded in the target vector space versus the domain, or as an alternating multilinear map using the Levi-Civita symbol or Laplace's expansion by minors.
  • #1
zeta12ti
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How could one derive a definition of the determinant from some of its basic properties such as det of product = product of dets or the determinant of a transpose is the determinant of the untransposed matrix?

Upon instigating research on determiniants, all I've found are definitions that either only cover lower dimensional matrices or define the determinant to be some strange expression with little motivation.
 
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  • #2
I think the determinant is the only mutilinear alternating function in the vector space - up to a constant. If you require the map to have a value of 1 on the identity matrix then you get the determinant.

Try proving this - say by induction.
 
  • #3
lavinia said:
I think the determinant is the only mutilinear alternating function in the vector space - up to a constant. If you require the map to have a value of 1 on the identity matrix then you get the determinant.
To clarify, Lavinia means that the top exterior power of a vector space is one-dimensional. I.e. for an n-dimensional vector space V, the vector space of alternating, multilinear functions f:V^n->R is one-dimensional. This is only true for the top dimension.
 
  • #4
zhentil said:
To clarify, Lavinia means that the top exterior power of a vector space is one-dimensional. I.e. for an n-dimensional vector space V, the vector space of alternating, multilinear functions f:V^n->R is one-dimensional. This is only true for the top dimension.

Yes. this is the same statement.

the determinant is a polynomial in the matrix entries. I wonder how this all translates into the properties of this polynomial.
 
  • #5
lavinia said:
Yes. this is the same statement.

the determinant is a polynomial in the matrix entries. I wonder how this all translates into the properties of this polynomial.

How about this as another way to think of the determinant. Consider all functions on square matrices that are constant under conjugation.

[tex] f(X) = f(AXA ^-1) [/tex]

for all square matrices X and invertible square matrices ,A.

For instance the trace is such a function.

Define the determinant to be that function which equals the product of the diagonal entries on any diagonal matrix.
 
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  • #6
Thanks for the responses. However, I have a question.
Is the determinant uniquely determined by its multilinearity and the properties I mentioned above?
I am hoping that the determinant can be made into a type of analogue of the complex abosolute value with as few other conditions as possible (alternating multilinear is a bit too...esoteric for my tastes)
 
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  • #7
zeta12ti said:
Thanks for the responses. However, I have a question.
Is the determinant uniquely determined by its multilinearity and the properties I mentioned above?
I am hoping that the determinant can be made into a type of analogue of the complex abosolute value with as few other conditions as possible (alternating multilinear is a bit too...esoteric for my tastes)

alternating multilinear on any n-vectors uniquely determines the determinant up to a constant.
 
  • #8
I like how it's covered in Linear Algebra Done Wrong, which is free and easy to find online. Once he gets to the definition of determinant I think the coverage is pretty standard but the motivation behind the definition is nicely included.
 
  • #9
zeta12ti said:
Thanks for the responses. However, I have a question.
Is the determinant uniquely determined by its multilinearity and the properties I mentioned above?
I am hoping that the determinant can be made into a type of analogue of the complex abosolute value with as few other conditions as possible (alternating multilinear is a bit too...esoteric for my tastes)

An equivalent definition is that the determinant is the product of the eigenvalues of the linear transformation that corresponds to the matrix (ie., it is the factor of signed scale of n-paralleletopes embedded in the target vector space versus the domain). This definition is a bit more intuitive and easier to work with geometrically. If you're looking for a formula that refers directly to the entries of an arbitrary matrix representation of the linear transformation, then you are left with the alternating multilinear map, embodied in the Levi-Civita symbol or Laplace's expansion by minors (equivalent, just re-ordered).
The relation between the two definitions is derived through basic geometric algebra (the wedge product).
 
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  • #10
^The product of eigenvalues is not equivalent or helpful is the matrix does not have any eigenvalues.
 

FAQ: Definition of the Determinant of a Matrix

1. What is the definition of the determinant of a matrix?

The determinant of a matrix is a numerical value that is computed from the elements of a square matrix. It is used to determine important properties of the matrix, such as whether it is invertible or singular.

2. How is the determinant of a matrix calculated?

The determinant of a matrix can be calculated using various methods, such as the cofactor expansion method or the row reduction method. The most commonly used method is the cofactor expansion method, which involves multiplying each element in a row or column by its corresponding cofactor and then summing the results.

3. What is the significance of the determinant of a matrix?

The determinant of a matrix is an important mathematical concept that has many applications in fields such as linear algebra, calculus, and physics. It is used to determine the volume of a parallelepiped in higher dimensions, find the inverse of a matrix, and solve systems of linear equations, among other things.

4. How can the determinant of a matrix be used to determine if a matrix is invertible?

A matrix is invertible if and only if its determinant is non-zero. This means that if the determinant of a matrix is equal to zero, the matrix is not invertible and does not have an inverse. This property is useful in solving systems of linear equations and finding the inverse of a matrix.

5. Can the determinant of a matrix be negative?

Yes, the determinant of a matrix can be negative, positive, or zero. The sign of the determinant depends on the order of the matrix and the values of its elements. For example, a 2x2 matrix with a negative determinant means that the matrix is a reflection or a rotation, while a positive determinant indicates a scaling or a shear transformation.

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