Is the axiom for addition in the set of polynomials of degree >=3 true?

In summary, the set V of polynomials of degree >= 3, together with 0, is not a vector space as the axiom "For u,v in V, u+v in V" does not hold. This is shown by taking two polynomials u(x) and v(x) with degrees n and k, where n and k are both >= 3. When n < k, the sum u + v is still a polynomial of degree >= 3. However, when n = k and an = -bn, the sum is no longer a polynomial of degree >= 3. This contradicts the textbook, which states that all scalar multiplication axioms hold.
  • #1
stunner5000pt
1,461
2
Determine if this is a vector space with the indicated operations

the set of V of all polynominals of degree >=3, togehter iwth 0, operations of P (P the set of polynomials)

now all the scalar multiplication axioms hold.
the text however says that the axion
[tex] \mbox{For u,v} \in V, \mbox{then} \ u+v \in V [/tex] does not hold

well ok take two polynomials
[tex] u(x) = a_{3} x^3 + ... + a_{n} x^n [/tex]
[tex] v(x) = b_{3} x^3 + ... + b_{k} x^k [/tex]
where both n,k>= 3, then suppose k< n
[tex] u(x) + v(x) = (a_{3} + b_{3}) x^3 + ... + (a_{k} + b_{k}) x^k + ... + a_{n} x^n [/tex]
which is certainly a polynomial or degree >= 3 isn't it?
It also applies for n<k and n = k
is the textbook wrong?
 
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  • #2
What is the degree of the following poly

x^4+1

now, do you see your error?
 
  • #3
ok let me correct that then
n,k >= 3
[tex] u(x) = a_{0} + a_{1} x + ... + a_{n} x^n [/tex]
[tex] v(x) = b_{0} + b_{1} x + ... + b_{k} x^k [/tex]
then for n< k
[tex] u + v = (a_{0} + b_{0}) + ... + (a_{k} + b_{k}) x^k + ... + a_{n} x^n [/tex]

stil lseems to be of degree three to me
however if k=n and an= -bn then the polynomial is no more degree 3
is this corret?
 
  • #4
Why don't you just find a counter example? two polys of degree 3 or greaterwhose sum isn't? A single counter examplem suffices.
 

Related to Is the axiom for addition in the set of polynomials of degree >=3 true?

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of vectors and operations that can be performed on those vectors. It is a fundamental concept in linear algebra and has many applications in physics, engineering, and other fields.

2. What are the properties of a vector space?

A vector space must satisfy several properties, including closure under vector addition and scalar multiplication, the existence of a zero vector, and the existence of additive and multiplicative inverses for each vector. It must also follow the associative, commutative, and distributive properties.

3. How do you determine if a set is a vector space?

In order for a set to be considered a vector space, it must follow all of the properties listed above. This can be checked by verifying that the set satisfies the vector space axioms, which are a set of rules that define how vectors and operations must behave in a vector space.

4. What is the difference between a subspace and a vector space?

A subspace is a subset of a vector space that also satisfies all of the properties of a vector space. In other words, it is a smaller vector space contained within a larger one. A vector space, on the other hand, is a self-contained mathematical structure with its own set of vectors and operations.

5. What are some examples of vector spaces?

Vector spaces can be found in many different areas of mathematics and science. Some common examples include the set of n-dimensional column vectors, the set of polynomials of degree n or less, and the set of continuous functions on a given interval. Other examples can be found in graph theory, quantum mechanics, and computer science.

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