What is the Function of Determinant in Expressing a Linear Map?

In summary, the conversation discusses a proof for expressing a linear functional F on a vector space as a function of determinant, F(\phi)=f(\hbox{det}\phi). The proof involves defining two families of functions and showing that F(\psi_{ij})=1 and F(\phi_j) does not depend on i. The key idea is to use these functions to transform other functions with the same determinant without changing the value of F, ultimately concluding that F is equal to the determinant.
  • #1
losiu99
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Homework Statement



Let E be a vector space of finite dimension over [tex]\Gamma[/tex] (char 0), and [tex]F\colon L(E,E)\rightarrow \Gamma[/tex] satisfies
(1) [tex]F(\phi \circ \psi)=F(\phi)F(\psi)[/tex]
(2) [tex]F(\hbox{id})=1[/tex]

Prove F can be expressed as a function of determinant, [tex]F(\phi)=f(\hbox{det}\phi)[/tex].

Homework Equations



Hint: Let [tex]e_\nu[/tex] be a basis and define
[tex]\psi_{ij}e_{\nu}=\begin{cases}e_\nu & \text{if } \nu\neq i\\e_i+\lambda e_j & \text{if }\nu = i\end{cases}[/tex]
[tex]\phi_i e_\nu =\begin{cases}e_\nu & \text{if } \nu \neq i \\ \lambda e_\nu & \text{if }\nu = i\end{cases}[/tex]

Show that [tex]F(\psi_{ij})=1[/tex] and that [tex]F(\phi_j)[/tex] doesn't depend on i.

The Attempt at a Solution


I have proved facts in the hint, but I cannot clearly see how to carry on. I guess it's about using this two families of functions to transform functions having the same determinant without changing the value of F to the point we can conclude F's values are equal. Unfortunetely, I fail miserably trying to accomplish this.
I appreciate any help.
 
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  • #2
A linear functional on a vector space is completely determined by its action on a basis.
What would [itex]det(\psi_{ij})[/itex] and [itex]det(\phi_j)[/itex] be? If F obeys exactly the same laws as the determinant, it is the determinant!
 
  • #3
Well, for the record, F doesn't obey the same laws as a determinant. Nor is it necessarily a linear functional. Any multiplicative function defined in terms of value of determinant with f(id)=1 will satisfy this conditions. Anyway, I believe I have roughly proved all the necessary facts, I think it's just the matter of carefully writing down the proof, making sure I haven't overlooked something. Will do it tomorrow and let you know if I succeed. Thanks for your response.
 

1. What is the function of a determinant?

The determinant is a mathematical function that is used to determine certain properties of a square matrix, such as whether the matrix is invertible or singular. It also provides information about the scaling factor of the matrix and the relationship between the columns or rows of the matrix.

2. How is the determinant calculated?

The determinant of a square matrix can be calculated using various methods, such as the cofactor expansion method or the row reduction method. The most common method is using the cofactor expansion, where the matrix is broken down into smaller submatrices and multiplied by their corresponding cofactors.

3. What is the significance of the determinant in linear algebra?

The determinant is a fundamental concept in linear algebra and is used in various applications, such as solving systems of linear equations, finding eigenvalues and eigenvectors, and determining the area or volume of a parallelogram or parallelepiped. It also plays a crucial role in understanding transformations and their properties.

4. Can the determinant be negative?

Yes, the determinant can be negative. The value of the determinant is determined by the sign of the cofactors used in its calculation. If the number of row or column swaps required to reduce the matrix to its upper triangular form is odd, the determinant will be negative.

5. Are there any real-world applications of the determinant?

Yes, the determinant has numerous real-world applications, such as in computer graphics for 3D transformations, in physics for calculating the moment of inertia of an object, in economics for solving input-output models, and in biology for determining population growth rates. It also has applications in other fields, such as chemistry, engineering, and statistics.

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