To what is X_{-m}+α(a)Y_{-m} equal?

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In summary: I believe "a" is the variable in the equation and "t" is the time. Is that correct?Yes, "a" is the variable in the equation and "t" is the time.
  • #1
mathmari
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Hey! :eek:

Let $F$ be an integral domain with characteristic $2$. Let $a\in F[t]$ and $a \notin F$. Let $\alpha (a)$ be a root of the equation $x^2+ax+1=0$. We define the two sequences $X_m(a), Y_m(a) \in F[t], m \in \mathbb{Z}$ as followed:
$$X_m(a)+\alpha (a)Y_m(a)=(\alpha (a))^m=(a+\alpha (a))^{-m}$$

Lemma.

Let $F$ be an integral domain with characteristic $p=2$. Let $a \in F[t], a \notin F$. $X_m(s)$ (resp. $Y_m(a)$) is equal to the polynomial that we obtain if we substitute $t$ with $a$ at $X_m(t)$ (resp. $Y_m(t)$).
  • The degree of the polynomial $X_m(t)$ is $m-2$, if $m \geq 2$.
  • The degree of the polynomial $Y_m(t)$ is $m-1$, if $m \geq 2$.
  • $X_{-m}=X_m(a)+aY_m(a)$
  • $Y_{-m}(a)=Y_m(a)$
To prove this lemma I have done the following:

For the first two sentences about the degree I used induction on $m$. Is this correct?

As for the last two relations:

$$X_{-m}+\alpha (a)Y_{-m}=(a+\alpha (a))^m=((a+\alpha (a))^{-m})^{-1}=(X_m(a)+\alpha (a)Y_m(a))^{-1}=\frac{1}{X_m(a)+\alpha (a)Y_m(a)}$$

How could we continue?
 
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  • #2
Could we maybe do it as follows:

$$X_{-m}(a)+\alpha (a)Y_{-m}(a)=(a+\alpha (a))^m=(a+\alpha (a))^{2m}(a+\alpha (a))^{-m}=[(a+\alpha (a))^2]^m(X_m(a)+\alpha (a)Y_m(a))=[a^2+(\alpha (a))^2]^m(X_m(a)+\alpha (a)Y_m(a))=[a^2+a\alpha (a)+1]^m(X_m(a)+\alpha (a)Y_m(a))$$

But how could we continue?

Or is this the wrong way?
 
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  • #3
I have also an other idea:

$$X_{-m}(a)+\alpha (a)Y_{-m}(a)=(a+\alpha (a))^m=X_m(a)+(a+\alpha (a))Y_m(a)\\ =X_m(a)+aY_m(a)+\alpha (a)Y_m(a) \\ \Rightarrow X_{-m}(a)=X_m(a)+aY_m(a)\ \ , \ \ Y_{-m}(a)=Y_m(a)$$

Is this correct?
 
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  • #4
Hi,

I don't have the solution, in fact, I can not even try to do it right now, but just as a comment, you can't take inverses (rise to -1), because you are working in an integral domain, not in a field.

And using induction is OK if you haven't made any mistakes. :)

I will try to solve it if I can have some rest this weekend =P
 
  • #5
Fallen Angel said:
you can't take inverses (rise to -1), because you are working in an integral domain, not in a field.

At my last idea, at post #3, I don't inverse the elemnts to $-1$. Is this correct to show it in that way?
Fallen Angel said:
And using induction is OK if you haven't made any mistakes. :)

I did it as follows:

Base case: For $m=2$ we have $X_2(a)+\alpha (a)Y_2(a)=(\alpha (a))^2=a\alpha (a)+1$. So $X_2(a)=1, Y_2(a)=a$. That means that $\text{deg}(X_2(t))=0=2-2$ and $\text{deg}(Y_2(t))=1=2-1$.

Inductive hypothesis: We suppose that it holds for $m=k$, i.e., $\text{deg}(X_k(t))=k-2$ and $\text{deg}(Y_k(t))=k-1$.

Inductive step: We will show that it holds for $n=k+1$, i.e., $\text{deg}(X_{k+1}(t))=k-1$ and $\text{deg}(Y_{k+1}(t))=k$.
$$X_{k+1}(a)+\alpha (a)Y_{k+1}(a)=(a+\alpha (a))^{-(k+1)}=(a+\alpha (a))^{-k}(a+\alpha (a))^{-1}=(X_k(a)+\alpha (a)Y_k(a))(a+\alpha (a))^{-1}=(X_k(a)+\alpha (a)Y_k(a))\alpha (a)=\alpha (a)X_k(a)+\alpha (a)^2Y_k(a)=\alpha (a)X_k(a)+(a\alpha (a)+1)Y_k(a)=Y_k(a)+\alpha (a)[X_k(a)+aY_k(a)] \\ \Rightarrow X_{k+1}(a)=Y_k(a), Y_{k+1}(a)=X_k(a)+aY_k(a) \\ \Rightarrow \text{deg}(X_{k+1}(a))=\text{deg}(Y_k(a))=k-1 \ \ , \ \ \text{deg}(Y_{k+1}(a))=\max \{\text{deg}(X_k(a)), \text{deg}(aY_k(a))\}=\max \{k-2, 1+k-1\}=k$$
I am a little confused about "a" and "t".
 
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FAQ: To what is X_{-m}+α(a)Y_{-m} equal?

1. What is the meaning of the equation X_{-m}+α(a)Y_{-m}?

The equation X_{-m}+α(a)Y_{-m} represents the sum of two variables, X_{-m} and α(a)Y_{-m}. The symbols within the equation, such as α, a, and m, represent specific values or parameters that are used in the equation.

2. How is the equation X_{-m}+α(a)Y_{-m} used in scientific research?

The equation X_{-m}+α(a)Y_{-m} is commonly used in statistical analyses, particularly in regression analysis. It is used to model the relationship between two variables, where X_{-m} represents the dependent variable and Y_{-m} represents the independent variable. The addition of α(a) allows for the inclusion of a third variable, making the equation more versatile in analyzing complex relationships.

3. What is the difference between X_{-m}+α(a)Y_{-m} and X_{-m}Y_{-m}?

The difference between X_{-m}+α(a)Y_{-m} and X_{-m}Y_{-m} lies in the inclusion of the α(a) term. While X_{-m}Y_{-m} only considers the two variables X_{-m} and Y_{-m}, X_{-m}+α(a)Y_{-m} allows for the inclusion of a third variable, α(a). This makes the latter equation more flexible and useful in analyzing complex relationships between variables.

4. How is the value of α(a) determined in the equation X_{-m}+α(a)Y_{-m}?

The value of α(a) in the equation X_{-m}+α(a)Y_{-m} is determined through statistical methods, such as regression analysis. The goal is to find the value of α(a) that best fits the data and provides the most accurate representation of the relationship between the variables X_{-m} and Y_{-m}. This value may also be determined through trial and error in some cases.

5. Can the equation X_{-m}+α(a)Y_{-m} be used for all types of data?

The equation X_{-m}+α(a)Y_{-m} can be used for many types of data, but it may not be suitable for all types of data. For example, if the data is not linear, the equation may not accurately represent the relationship between the variables. Additionally, the equation may not be appropriate for data that has a non-normal distribution. It is important to carefully consider the data and the assumptions of the equation before using it in research.

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