MHB Why is the characteristic different from 2?

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The discussion centers on the implications of the characteristic of a polynomial, specifically when the characteristic of the underlying field is not equal to 2. The theorem presented involves a symmetric quadratic form that can be transformed into a specific canonical form through linear transformation. Participants clarify that the condition "ch K ≠ 2" is essential for the theorem's validity, as the result does not hold when the characteristic is 2. The connection to the spectral theorem for real symmetric matrices is also highlighted, emphasizing the role of eigenvalues in this context. Ultimately, the discussion underscores the significance of field characteristics in polynomial transformations.
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Hello! (Wave)

In my notes, there is the following theorem:

Theorem:

Let $Q(X_1, X_2, \dots, X_n)=\sum_{i,j=1}^n a_{ij} X_i X_j$

$$a_{ij}=a_{ji}, \ \ \ \ ch K \neq 2 ( \text{ Characteristic of polynomial })$$

symmetric quadratic form of $n$ variables.

There is a linear transformation of the coordinates, so that, finally, $Q(X_1, X_2, \dots, X_n)$ is transformated to the form:

$$Q'(X_1, X_2, \dots, X_n)=\sum_{i=1}^r l_i X_i^2, i=1,2, \dots, r, l_i \neq 0$$How do we conclude that $ch K \neq 2$ ? (Thinking)
 
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evinda said:
Hello! (Wave)

In my notes, there is the following theorem:

Theorem:

Let $Q(X_1, X_2, \dots, X_n)=\sum_{i,j=1}^n a_{ij} X_i X_j$

$$a_{ij}=a_{ji}, \ \ \ \ ch K \neq 2 ( \text{ Characteristic of polynomial })$$

symmetric quadratic form of $n$ variables.

There is a linear transformation of the coordinates, so that, finally, $Q(X_1, X_2, \dots, X_n)$ is transformated to the form:

$$Q'(X_1, X_2, \dots, X_n)=\sum_{i=1}^r l_i X_i^2, i=1,2, \dots, r, l_i \neq 0$$How do we conclude that $ch K \neq 2$ ? (Thinking)

Hey! (Happy)Can you clarify what you mean by $\text{ch K}$? (Wondering)I can tell you that the spectral theorem for real symmetric matrices says that:
$$A = B^T D B$$
where $D$ is a diagonal matrix of real eigenvalues and $B$ is an orthogonal matrix of eigenvectors. (Nerd)

It follows that:
$$Q = x^T A x = x^T (B^T D B) x = (Bx)^T D (Bx)$$

In other words, $B$ identifies the linear transformation and the $l_i$ are the eigenvalues. (Mmm)
 
evinda said:
Hello! (Wave)

In my notes, there is the following theorem:

Theorem:

Let $Q(X_1, X_2, \dots, X_n)=\sum_{i,j=1}^n a_{ij} X_i X_j$

$$a_{ij}=a_{ji}, \ \ \ \ ch K \neq 2 ( \text{ Characteristic of polynomial })$$

symmetric quadratic form of $n$ variables.

There is a linear transformation of the coordinates, so that, finally, $Q(X_1, X_2, \dots, X_n)$ is transformated to the form:

$$Q'(X_1, X_2, \dots, X_n)=\sum_{i=1}^r l_i X_i^2, i=1,2, \dots, r, l_i \neq 0$$How do we conclude that $ch K \neq 2$ ? (Thinking)
The statement $\text{ch}\,K \ne2$ is not a conclusion, it is a condition. In fact, the result fails if the characteristic of the underlying field (not the polynomial!) is $2$.
 
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