Prove Lagrange Polynomials Basis of $\mathbb{R}_{n}[X]

In summary, the basis for a linear map from a set of vectors to a set of real numbers is found when it is shown that the matrix is linearly independent.
  • #1
God's Pen
12
0
hello everyone:smile:
for [tex]
i=1,2,...,(n+1)
[/tex] let [tex]P_{i}(X)=\frac{\prod_{1\leq j\leq n+1,j\neq i}(X-a_j)}{\prod_{1\leq j\leq n+1,j\neq i}(a_i-a_j)}[/tex]
prove that [tex]
(P_1,P_2,...P_{n+1})
[/tex] is basis of [tex]
\mathbb{R}_{n}[X]
[/tex].
i already have an answer but i don't understand some of it.
...
we have [tex]B=\{P_1, \cdots , P_{n+1} \} \subset \mathbb{R}_n[x][/tex] and [tex]\dim_{\mathbb{R}} \mathbb{R}[x]=n+1.[/tex] so, in order to show that [tex]B[/tex] is a basis for [tex]\mathbb{R}_n[x],[/tex] we only need

to show that [tex]B[/tex] is linearly independent.for any [tex]i[/tex] we have [tex]P_i(a_i)=1[/tex] and [tex]P_j(a_i)=0,[/tex] for [tex]i \neq j.[/tex] now, to show that [tex]B[/tex] is linearly independent, suppose that [tex]\sum_{j=1}^{n+1}c_j P_j(x)=0,[/tex] for some
[tex]c_j \in \mathbb{R}.[/tex] put [tex]x=a_i[/tex] to get [tex]c_i=0. \ \Box[/tex]
...
what i don't understand,
why should we have [tex]P_j(X)[/tex] and not [tex]P_i(X)[/tex]
why we had to find [tex]P_j(a_i)[/tex] ?
and why we have [tex]\sum_{j=1}^{n+1}[/tex] and not [tex]\sum_{i=1}^{n+1}[/tex] ?
thank you so much.
 
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  • #2
Hmmm..er...I don't know if I have understood your question properly ...but let me try to answer...


Your question is..why have we used j instead of i in the summation? Well..as far as I can see...i and j are just dummy variables ..it does not matter whether you use i or j or k...

You can very well use i if you want...but then you have to always remember that i denotes a general element...then..to prove the coefficients as zero, you can't put x=a_(i) as i is now reserved for the general element..you have to use some other letter in that case...


Now, as for the question as to why we had to find P_(j)(a_i)...by putting x=a_(i), you are reducing all elements of the sum to zero except the term with index j=i. This term is
c_i * P_(i)(a_i)=c_i.
 
  • #3
krishna mohan said:
Hmmm..er...I don't know if I have understood your question properly ...but let me try to answer...


Your question is..why have we used j instead of i in the summation? Well..as far as I can see...i and j are just dummy variables ..it does not matter whether you use i or j or k...

You can very well use i if you want...but then you have to always remember that i denotes a general element...then..to prove the coefficients as zero, you can't put x=a_(i) as i is now reserved for the general element..you have to use some other letter in that case...


Now, as for the question as to why we had to find P_(j)(a_i)...by putting x=a_(i), you are reducing all elements of the sum to zero except the term with index j=i. This term is
c_i * P_(i)(a_i)=c_i.

thank you.
but if i and j are just dummy variables and it doesn't matter which one we put.
how could we have [tex]P_i(a_i)=1 [/tex] and [tex]P_j(a_i)=0 [/tex] ??
?
 
  • #4
Welll...first thing you should realize is that even if you use i or j or k or a or b or c, the two expressions are fundamentally different...


[tex]P_{i}(a_{i})[/tex] has the the index for [tex]P[/tex] and [tex]a[/tex] as the same..

[tex]P_{j}(a_{i})[/tex] has the the index for [tex]P[/tex] and [tex]a[/tex] as different..of course, the assumption here being that, whatever i and j are, they are not equal..i.e if we put i=1, then j cannot be 1...


See the definition of [tex]P_{i}(X)[/tex]...In the numerator, there is a product...the product contains all terms of the form [tex]X-a_{j}[/tex] such that j is not equal to i...

Like, if i=3, then the product contains [tex](X-a_{1}),(X-a_{2}),(X-a_{4}),(X-a_{5}) etc[/tex] but not [tex](X-a_{3})[/tex]...


Then, you can see why putting [tex]X=a_{3}[/tex] won't make the expression to zero..but [tex]X=a_{j}[/tex]... such that j is not three...will reduce the whole term to zero...
 
  • #5
krishna mohan said:
Welll...first thing you should realize is that even if you use i or j or k or a or b or c, the two expressions are fundamentally different...


[tex]P_{i}(a_{i})[/tex] has the the index for [tex]P[/tex] and [tex]a[/tex] as the same..

[tex]P_{j}(a_{i})[/tex] has the the index for [tex]P[/tex] and [tex]a[/tex] as different..of course, the assumption here being that, whatever i and j are, they are not equal..i.e if we put i=1, then j cannot be 1...


See the definition of [tex]P_{i}(X)[/tex]...In the numerator, there is a product...the product contains all terms of the form [tex]X-a_{j}[/tex] such that j is not equal to i...

Like, if i=3, then the product contains [tex](X-a_{1}),(X-a_{2}),(X-a_{4}),(X-a_{5}) etc[/tex] but not [tex](X-a_{3})[/tex]...


Then, you can see why putting [tex]X=a_{3}[/tex] won't make the expression to zero..but [tex]X=a_{j}[/tex]... such that j is not three...will reduce the whole term to zero...

i think I'm getting somewhere,thank u so mush for your help.
:smile:
 

FAQ: Prove Lagrange Polynomials Basis of $\mathbb{R}_{n}[X]

What are Lagrange polynomials?

Lagrange polynomials are a set of polynomials that are used to approximate a function within a given interval. They are commonly used in numerical analysis and interpolation.

What is the basis of $\mathbb{R}_{n}[X]$?

The basis of $\mathbb{R}_{n}[X]$ is a set of polynomials of degree $n$ or less that can be used to represent any polynomial of degree $n$ or less. These polynomials form a basis for the vector space of polynomials of degree $n$ or less.

How do Lagrange polynomials prove to be a basis of $\mathbb{R}_{n}[X]$?

Lagrange polynomials can be proven to be a basis of $\mathbb{R}_{n}[X]$ by showing that they are linearly independent and span the vector space of polynomials of degree $n$ or less. This means that any polynomial of degree $n$ or less can be uniquely represented as a linear combination of the Lagrange polynomials.

What is the significance of proving Lagrange polynomials as a basis of $\mathbb{R}_{n}[X]$?

Proving Lagrange polynomials as a basis of $\mathbb{R}_{n}[X]$ is significant because it allows us to approximate a function using a set of known polynomials. This can be useful in solving problems in numerical analysis and interpolation, where it may be difficult to find the exact solution.

Can Lagrange polynomials be used for functions of any degree?

Yes, Lagrange polynomials can be used for functions of any degree. However, the accuracy of the approximation may vary depending on the degree of the function and the number of Lagrange polynomials used.

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