Permutations and Determinants .... Walschap, Theorem 1.3.1 ...

In summary, the author is discussing Theorem 1.3.1 in Gerard Walschap's book "Multivariable Calculus and Differential Geometry" and is seeking help understanding the proof of the theorem. The author explores an example with the determinant function and asks for clarification on how the formula deals with certain terms. The quick reply suggests simplifying the proof and explains the properties of determinants. The author also brings up the definition of determinants in Walschap's book.
  • #1
Math Amateur
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I am reading Gerard Walschap's book: "Multivariable Calculus and Differential Geometry" and am focused on Chapter 1: Euclidean Space ... ...

I need help with an aspect of the proof of Theorem 1.3.1 ...

The start of Theorem 1.3.1 and its proof read as follows:
?temp_hash=f5ded35588963e5ecb2b75591ab2544b.png


I tried to understand how/why

##\text{det} ( v_1, \cdot \cdot \cdot , v_n ) = \sum_{ \sigma } a_{ \sigma (1) 1 } , \cdot \cdot \cdot , a_{ \sigma (n) n } \ \text{det} ( e_{ \sigma (1) } , \cdot \cdot \cdot , e_{ \sigma (n) }##

where the sum runs over all maps ##\sigma \ : \ J_n = \{ 1 , \cdot \cdot \cdot , n \} \to J_n = \{ 1 , \cdot \cdot \cdot , n \}##

... so ...

... I tried an example with ##\text{det} \ : \ (\mathbb{R}^n)^n \to \mathbb{R}## ... ...

so we have ##v_1 = \sum_k a_{ k1 } e_k = a_{11} e_1 + a_{21} e_2##

and

##v_2 = \sum_k a_{ k2 } e_k = a_{12} e_1 + a_{22} e_2##and then we have

##\text{det} ( v_1, v_2 )####= \text{det} ( a_{11} e_1 + a_{21} e_2 , a_{12} e_1 + a_{22} e_2 )####= a_{11} a_{12} \ \text{det} ( e_1, e_1 ) + a_{11} a_{22} \ \text{det} ( e_1, e_2 ) + a_{21} a_{12} \ \text{det} ( e_2, e_1 ) + a_{21} a_{22} \ \text{det} ( e_2, e_2 )####= \sum_{ \sigma } a_{ \sigma (1) 1 } a_{ \sigma (2) 2 } \ \text{det} ( e_{ \sigma (1) }, e_{ \sigma (2) } ) ##

where the sum runs over all maps

##\sigma \ : \ J_n = \{ 1, 2 \} \to J_n = \{ 1 , 2 \}## ...... ... that is the sum runs over the two permutations

##\sigma_1 = \begin{bmatrix} 1 & 2 \\ 1 & 2 \end{bmatrix}## and ##\sigma_2 = \begin{bmatrix} 1 & 2 \\ 2 & 1 \end{bmatrix}##BUT how does the formula ##\sum_{ \sigma } a_{ \sigma (1) 1 } a_{ \sigma (2) 2 } \ \text{det} ( e_{ \sigma (1) }, e_{ \sigma (2) }## ...

... incorporate or deal with the terms involving ##\text{det} ( e_1, e_1 )## and ##\text{det} ( e_2, e_2 )## ... ... ?Hope someone can help ...?

Peter
 

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  • #2
Math Amateur said:
I am reading Gerard Walschap's book: "Multivariable Calculus and Differential Geometry" and am focused on Chapter 1: Euclidean Space ... ...

I need help with an aspect of the proof of Theorem 1.3.1 ...

The start of Theorem 1.3.1 and its proof read as follows:
View attachment 240124

I tried to understand how/why

##\text{det} ( v_1, \cdot \cdot \cdot , v_n ) = \sum_{ \sigma } a_{ \sigma (1) 1 } , \cdot \cdot \cdot , a_{ \sigma (n) n } \ \text{det} ( e_{ \sigma (1) } , \cdot \cdot \cdot , e_{ \sigma (n) }##

where the sum runs over all maps ##\sigma \ : \ J_n = \{ 1 , \cdot \cdot \cdot , n \} \to J_n = \{ 1 , \cdot \cdot \cdot , n \}##

... so ...

... I tried an example with ##\text{det} \ : \ (\mathbb{R}^n)^n \to \mathbb{R}## ... ...

so we have ##v_1 = \sum_k a_{ k1 } e_k = a_{11} e_1 + a_{21} e_2##

and

##v_2 = \sum_k a_{ k2 } e_k = a_{12} e_1 + a_{22} e_2##and then we have

##\text{det} ( v_1, v_2 )####= \text{det} ( a_{11} e_1 + a_{21} e_2 , a_{12} e_1 + a_{22} e_2 )####= a_{11} a_{12} \ \text{det} ( e_1, e_1 ) + a_{11} a_{22} \ \text{det} ( e_1, e_2 ) + a_{21} a_{12} \ \text{det} ( e_2, e_1 ) + a_{21} a_{22} \ \text{det} ( e_2, e_2 )####= \sum_{ \sigma } a_{ \sigma (1) 1 } a_{ \sigma (2) 2 } \ \text{det} ( e_{ \sigma (1) }, e_{ \sigma (2) } ) ##

where the sum runs over all maps

##\sigma \ : \ J_n = \{ 1, 2 \} \to J_n = \{ 1 , 2 \}## ...... ... that is the sum runs over the two permutations

##\sigma_1 = \begin{bmatrix} 1 & 2 \\ 1 & 2 \end{bmatrix}## and ##\sigma_2 = \begin{bmatrix} 1 & 2 \\ 2 & 1 \end{bmatrix}##BUT how does the formula ##\sum_{ \sigma } a_{ \sigma (1) 1 } a_{ \sigma (2) 2 } \ \text{det} ( e_{ \sigma (1) }, e_{ \sigma (2) }## ...

... incorporate or deal with the terms involving ##\text{det} ( e_1, e_1 )## and ##\text{det} ( e_2, e_2 )## ... ... ?Hope someone can help ...?

Peter

Quick reply (I will have more time this evening, if necessary):

The author seems to overcomplicate the case. Just define the determinant as the sum that runs over all permutations in the first place. Then you don't even need to worry what happens with none-injective maps.

As for your question ##\det(e_1,e_1) = 0 = \det(e_2,e_2)##. This easily follows by the defining determinant properties.
 
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  • #3
Math_QED said:
Quick reply (I will have more time this evening, if necessary):

The author seems to overcomplicate the case. Just define the determinant as the sum that runs over all permutations in the first place. Then you don't even need to worry what happens with none-injective maps.

As for your question ##\det(e_1,e_1) = 0 = \det(e_2,e_2)##. This easily follows by the defining determinant properties.


Thanks Math_QED ...

Peter
 
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  • #4
Math Amateur said:
... I tried an example with ##\text{det} \ : \ (\mathbb{R}^n)^n \to \mathbb{R}## ... ...
I am not sure I understand this part but determinants apply only to square matrices; a map from ##\mathbb R^{n^2} \rightarrow \mathbb R ## is not represented by a square matrix.
 
  • #5
WWGD said:
I am not sure I understand this part but determinants apply only to square matrices; a map from ##\mathbb R^{n^2} \rightarrow \mathbb R ## is not represented by a square matrix.

The determinant function maps ##\mathbb{R}^{n^2}## into ##\mathbb{R}##.
 
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  • #6
Math Amateur said:
I am reading Gerard Walschap's book: "Multivariable Calculus and Differential Geometry" and am focused on Chapter 1: Euclidean Space ... ...

I need help with an aspect of the proof of Theorem 1.3.1 ...

The start of Theorem 1.3.1 and its proof read as follows:
View attachment 240124

I tried to understand how/why

##\text{det} ( v_1, \cdot \cdot \cdot , v_n ) = \sum_{ \sigma } a_{ \sigma (1) 1 } , \cdot \cdot \cdot , a_{ \sigma (n) n } \ \text{det} ( e_{ \sigma (1) } , \cdot \cdot \cdot , e_{ \sigma (n) }##

where the sum runs over all maps ##\sigma \ : \ J_n = \{ 1 , \cdot \cdot \cdot , n \} \to J_n = \{ 1 , \cdot \cdot \cdot , n \}##Peter
This is just the definition of multilinearity: it means you can pull out scaling factors. For 2 variables : ## M( c_1a_1+ c_2a_2)= c_1M(a_1+ c_2a_2)= c_1c_2M(a_1+a_2) ## For k variables : ## M( c_1a_1+...+c_ka_k)= c_1M(a_1+ c_2a_2+...+c_ka_k)= c_1c_2M(a_1+a_2+ c_3a_3+...+c_ka_k)=...c_1c_2...c_kM(a_1+a_2+...+a_k) ## According to the exercise, if a map is multilinear, equal to 1 on the ##e_i## and alternating, then it must be the determinant.
 
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  • #7
Thanks for clarifying the issues, WWGD ...

Just for interest ... after proving Theorem 1.3.1 Walschap defines a determinant as follows:
?temp_hash=9befdea79a1534cbb86f4e6afbb5cde6.png
Peter
 

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Last edited:

1. What is the difference between permutations and determinants?

Permutations refer to the different ways in which a set of elements can be arranged, while determinants are a mathematical concept used to calculate the properties of matrices.

2. How are permutations and determinants used in science?

Permutations are used in statistics and probability to calculate the number of possible outcomes in an experiment. Determinants are used in physics and engineering to solve systems of equations and calculate properties of physical systems.

3. Can you give an example of a permutation?

One example of a permutation is the different ways in which a set of letters can be arranged to form a word. For example, the word "cat" can be arranged as "act", "tac", "atc", etc.

4. How do you calculate the determinant of a matrix?

The determinant of a matrix can be calculated by using a specific formula depending on the size of the matrix. For a 2x2 matrix, the determinant is calculated by multiplying the elements in the main diagonal and subtracting the product of the elements in the other diagonal. For larger matrices, there are more complex formulas that involve finding the determinants of smaller submatrices.

5. What is Theorem 1.3.1 in Walschap's book about permutations and determinants?

Theorem 1.3.1 in Walschap's book states that the determinant of a matrix is equal to the product of its eigenvalues. This theorem is important in linear algebra and has many applications in science and engineering.

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