Proof: Basis Representation Theorem

In summary, the Basis Representation Theorem states that for any integer k larger than 1, there exists a unique representation of any positive integer n as a sum of terms of the form a_k * k^s, where a_k is nonnegative and less than k. The proof uses the concept of b_k representations and shows that for each b_k representation, there is at least one associated b_k-1 representation, leading to the conclusion that b_k is less than or equal to b_k-1. The reason for using the "less than or equal to" sign is because there may be unknown b_k-1 representations, making the two values not necessarily equal.
  • #1
buffordboy23
548
2
I had a question about the following theorem.

Basis Representation Theorem: Let [tex] k [/tex] be any integer larger than 1. Then, for each positive integer [tex] n [/tex], there exists a representation

[tex] n = a_{0}k^{s} + a_{1}k^{s-1} + ... + a_{s} [/tex]

where [tex] a_{0} \neq 0 [/tex], and where each [tex] a_{i} [/tex] is nonnegative and less than [tex] k [/tex]. Furthermore, this representation of [tex] n [/tex] is unique; it is called the representation of [tex] n [/tex] to base [tex] k [/tex].


Proof: Let [tex] b_{k}\left(n\right) [/tex] denote the number of representations of [tex] n [/tex] to the base [tex] k [/tex]. We must show that [tex] b_{k}\left(n\right) [/tex] always equals 1.

Suppose that

[tex] n = a_{0}k^{s} + a_{1}k^{s-1} + ... + a_{s-t}k^{t} [/tex]

where neither [tex] a_{0} [/tex] nor [tex] a_{s-t} [/tex] equals zero. Then

[tex] n - 1 = a_{0}k^{s} + a_{1}k^{s-1} + ... + a_{s-t}k^{t} - 1 = a_{0}k^{s} + a_{1}k^{s-1} + ... + \left(a_{s-t} - 1\right)k^{t} + k^{t} - 1 = a_{0}k^{s} + a_{1}k^{s-1} + ... + \left(a_{s-t} - 1\right)k^{t} + \sum_{j=0}^{t-1} \left(k - 1\right)k^{t}[/tex]

Thus we see that for each representation of [tex] n [/tex] to the base [tex] k [/tex], we can find a representation of [tex] n-1 [/tex]. Consequently,

[tex] b_{k}\left(n\right) \leq b_{k}\left(n-1\right) [/tex]


Question: In the previous line, why is there a "less than or equal to" sign rather than an "equal" or "greater than or equal to" sign?
 
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  • #2
buffordboy23 said:
Thus we see that for each representation of [tex] n [/tex] to the base [tex] k [/tex], we can find a representation of [tex] n-1 [/tex]. Consequently,

[tex] b_{k}\left(n\right) \leq b_{k}\left(n-1\right) [/tex]


Question: In the previous line, why is there a "less than or equal to" sign rather than an "equal" or "greater than or equal to" sign?

Each b_k representation has at least one associated b_k-1 representation, but there may be b_k-1 representations we don't know about. If there were U >= 0 such unknown representations (in fact U = 0, but we don't know that), then b_k + U = b_k-1, so b_k <= b_k-1.
 
  • #3
Thanks CRGreathouse.

This is clear now. When first reading the proof, it seemed obvious that U = 0, but this was the flaw in my thinking. So we could use the "equals" sign but only if we first show that U = 0, which is more work than is necessary.
 
  • #4
Thanks so much! This was really puzzling me too.
 

What is the Basis Representation Theorem?

The Basis Representation Theorem is a mathematical principle that states that any vector in a finite-dimensional vector space can be represented as a linear combination of a set of basis vectors.

Why is the Basis Representation Theorem important?

The Basis Representation Theorem is important because it allows us to simplify and generalize calculations in vector spaces. By using a set of basis vectors, we can represent any vector in the space without needing to consider the specific coordinates of that vector.

How does the Basis Representation Theorem relate to linear independence?

The Basis Representation Theorem is closely related to linear independence. In order for a set of vectors to serve as a basis for a vector space, those vectors must be linearly independent, meaning that no vector in the set can be written as a linear combination of the other vectors.

Can the Basis Representation Theorem be applied to infinite-dimensional vector spaces?

No, the Basis Representation Theorem only applies to finite-dimensional vector spaces. In infinite-dimensional spaces, it is possible for a basis to contain an infinite number of vectors, making it impossible to represent any vector as a finite linear combination of those basis vectors.

How is the Basis Representation Theorem used in practical applications?

The Basis Representation Theorem has many practical applications in fields such as physics, engineering, and computer science. It is often used in the analysis and manipulation of signals, images, and other types of data represented as vectors. It also plays a key role in the understanding of linear transformations and their properties.

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