Proving Linear Dependence in Pm(F) Using Polynomials with p_j(2)=0

In summary, if a set of polynomials in Pm(F) has the property that each polynomial in the set evaluates to 0 when x=2, then the set is not linearly independent. This can be proven by showing that the set must contain at least two polynomials of the same degree, which makes them linearly dependent.
  • #1
gravenewworld
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Suppose that p_0,p_1,p_2...,p_m are polynomials in Pm(F) such that p_j(2)=0 for each j. Prove that (p_0,...,p_m) is not linearly independent in Pm(F).

So far I have, suppose that there is a polynomial in the list that is of degree 0, then that polynomial must be 0, hence the list is linearly dependent. If there is no polynomial of degree zero, there are at least two polynomials in the list that have the same degree. This where I get stuck, am I going in the right direction?
 
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  • #2
What in the world is Pm(F)?
 
  • #3
Sorry maybe I should have explained that. Is P subscript m (F). Its all polynomial s over a field F with degree at most m.
 
  • #4
Can anyone help me at all? This problem is driving me crazy
 
  • #5
I think you may construct a weird induction argument on m.
here's my thoughts, anyway:
1. Any such set cannot contain a constant polynomial, sinze that would be the zero polynomial (and hence, the set would be linearly dependent)

2. Take the case of m=1:
We then have two linear polynomials,
[tex]p_{0}=a_{0}x+b_{0}[/tex]
[tex]p_{1}=a_{1}x+b_{1}[/tex]
(the a's distinct from zero)
But the condition [tex]p_{0}(2)=p_{1}(2)=0[/tex] implies that:
[tex]p_{0}=b_{0}(1-\frac{x}{2})[/tex]
[tex]p_{1}=b_{1}(1-\frac{x}{2})[/tex]
Hence, our polynomials are linearly dependent..
hope this helps a bit..
 
  • #6
Sorry to resurrect old posts but my linear alg. professor came up with a viable way:

we're trying to prove a_0*P_0 + a_1*P_1 + ... + a_m*P_m = 0 for where anyone a_0 through a_m is nonzero. (Equation 1)

P_0, P_1, ..., P_m are m+1 vectors and you already proved that there can't be a p_i of degree 0 for 0<=i<=m . We also know that each p_i(x)%(x-2) = 0 for 0<=i<=m so dividing Equation 1 by (x-2) we have m+1 polynomials of varying degree i where 0<=i<=m-1. These polynomials have a maximum dimension of m, therefore. m+1 polynomials cannot be a basis of F^m because they're not linearly independent therefore the polynomials are linearly dependent. I hope I translated his thoughts correctly.
 

1. What is linear dependence?

Linear dependence is a mathematical concept that describes the relationship between two or more vectors in a vector space. It means that one vector can be expressed as a linear combination of the other vectors, or in other words, one vector is a multiple of another vector.

2. How do you determine if a set of vectors is linearly dependent?

A set of vectors is linearly dependent if at least one vector in the set can be written as a linear combination of the other vectors. This can be determined by using row reduction techniques on a matrix representation of the vectors. If there is a row of zeros in the reduced matrix, then the set of vectors is linearly dependent.

3. Why is linear dependence important in mathematics?

Linear dependence is important in mathematics because it helps us understand the relationships between vectors in a vector space. It also allows us to solve systems of linear equations and find the span of a set of vectors. Additionally, linear dependence is a fundamental concept in linear algebra, which has many real-world applications in fields such as physics, engineering, and economics.

4. Can a set of three vectors be linearly dependent?

Yes, a set of three or more vectors can be linearly dependent. In fact, any set of vectors with more than two vectors can be linearly dependent, as long as at least one vector can be written as a linear combination of the other vectors.

5. How can linear dependence be used in real-life situations?

Linear dependence has many practical applications in real-life situations. For example, it can be used in data analysis to determine if there is a linear relationship between variables. It is also used in physics and engineering to model and solve systems involving forces and motion. Additionally, linear dependence is used in economics to analyze market trends and make predictions.

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