Linear Algebra Polynomial Vector Space

In summary, the subspace theorem is used to determine which of the given sets are real vector spaces with the usual operations. The set of real polynomials of degree \leq n (b) is a real vector space since it is closed under addition and scalar multiplication. However, the set of all real polynomials (a) and the set of real polynomials of degree exactly n (c) are not real vector spaces because they do not satisfy all of the necessary axioms, such as having an identity element and being closed under addition and scalar multiplication. The difference between these sets lies in the size of their basis set, with a) containing polynomials of any degree, b) containing polynomials up to degree n, and c
  • #1
Snippy
5
0

Homework Statement


Use the subspace theorem to decide which of the following are real vector spaces with the usual operations.

a) The set of all real polynomials of any degree.
b) The set of real polynomials of degree [tex]\leq n[/tex]
c) The set of real polynomails of degree exactly n.


Homework Equations





The Attempt at a Solution



I know how to do b) since the equation for the set of real polynomials of degree [tex]\leq n[/tex] is:
Pn = {a0 + a1x + a2x2 + ... + anx2 | a0, a1, ... , an [tex]\in[/tex] R }

And I can prove that it is closed under addition and scalar multiplication.

But I am not sure what the difference between the equation for b) (at most n) and a) (any n) and c) (= n) is.

Also I know b) is a real vector space but I would've thought that meant c) was too since b) includes degree = n. But the answers say a) is b) is but c isn't.
 
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  • #2
you need to check through all the axioms systematically... for c) is there an identity element? and is it closed?
 
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  • #3
to see the difference between a) & b) consider the size of a basis set
 
  • #5
My problem is I don't know what the difference between the equations for the 3 different problems is in order to check the axioms.
 
  • #6
ok so i would read it a polynomial of degree n, is any polynomial given by [itex] P_n = {a_0 + a_1x + a_2x^2 + ... + a_nx^2 | a_0, a_1, ... , a_n \in R} [/itex], when a_n is non-zero.

so
a) contains every Pm, for m = 0 to infinity
b) contains every Pm, for m = 0 to n
c) contains every Pm with m = n

though you also need to assume they contain 0... ie. in the n = 0 case, a_0 can be zero
 
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  • #7
as some examples
[itex] x^3 + 1[/itex] is a polynomial of degree 3
[itex] 2[/itex] is a polynomial of degree 0
and so on
 
  • #8
You have to consider n to be fixed. Say n=3 for example. Then x4 is an element of a) but is not an element of b) or c); x3 is an all of them; and x2 is an element of a) and b) but not of c). Do you see why?
 

FAQ: Linear Algebra Polynomial Vector Space

What is Linear Algebra Polynomial Vector Space?

Linear Algebra Polynomial Vector Space is a mathematical concept that involves the study of vector spaces and the operations that can be performed on them. It also deals with the representation of these operations using polynomials.

What are the basic properties of Linear Algebra Polynomial Vector Space?

The basic properties of Linear Algebra Polynomial Vector Space include closure, associativity, distributivity, and commutativity. Closure means that the result of any operation on vectors will always be a vector within the same vector space. Associativity means that the order in which operations are performed does not affect the final result. Distributivity refers to the ability to distribute operations over different vectors. Commutativity means that the order in which vectors are added or multiplied does not affect the final result.

What is the importance of Linear Algebra Polynomial Vector Space in science?

Linear Algebra Polynomial Vector Space is an important mathematical tool used in various scientific fields such as physics, engineering, and computer science. It helps in solving problems related to systems of linear equations, optimization, and data analysis. It also provides a framework for understanding and solving complex problems in a more efficient manner.

What are the main applications of Linear Algebra Polynomial Vector Space?

Linear Algebra Polynomial Vector Space has numerous applications in different fields. Some of the main applications include computer graphics, image processing, machine learning, and cryptography. It is also used in physics for modeling physical systems and in engineering for designing and optimizing structures and systems.

What are some common misconceptions about Linear Algebra Polynomial Vector Space?

There are a few common misconceptions about Linear Algebra Polynomial Vector Space. One of them is that it is only used in advanced mathematics. In reality, it has various real-world applications and is used in many scientific and technological fields. Another misconception is that it is just a more complex version of basic algebra. However, Linear Algebra Polynomial Vector Space involves the study of vector spaces and operations that cannot be performed in basic algebra.

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