Is This a Proof of Consistency for Ax=b and b in Span(S)?

In summary, for all \vec{b}\in R^n, Ax = b is consistent if and only if \vec{b}\in Span\{\vec{w_1}, \cdots, \vec{w_n}\}.
  • #1
hkus10
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Let S = {w1, w2, ..., wn} be a set of n vectors in R^n and let A be nxn matix whoise columns are the elements of S. Prove that for all b belong in R^n, Ax = b is consistent if and only if b belongs span(S).

My approach is:
I use contrapositive method to prove both sides
First, I prove that if Ax = b is consistent, the b belongs span(s).

Assume that Ax = b is inconsistent. Let x be [x1 x2 ... xn] ***This is a vectical vector which means x1, x2, and xn lines up vectically since I cannot express it in this way.
This means the last row of all vectors in S are zeros and the last row of b has nonzero integer. Then, b cannot be written as a linear combination of vectors in S since 0x1 + 0x2 + ... + 0xn = 0.
Therefore, b does not belong span(S).

For the other side:
Assume b does not belong span(S).
Then, b cannot be written as a linear combination of vectors in S.
If the last row of b is an nonzero integer, then the last row of all vectors in S must be zeros so that b cannot be written as a linear combination of vectors in S.
By the Matrix-Vector Product written in terms of columns, [v1 v2 ... vn][x] not equal to .
Thus, Ax = b is inconsistent.

My question is that this proof seems quite reasonably for me. However, am I really proving this question. If not, how to approach it instead?

Thanks
 
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  • #2


Greetings! I don't see any errors in your logic, but it does seem that using contradiction adds a layer of complexity. Here's another way to go about it:

Let [tex]\vec{x} = [ x_1 \cdots x_n ]^T [/tex] and [tex] A = [ \vec{w_1} \cdots \vec{w_n} ][/tex]. If [tex]A \vec{x} = \vec{b}[/tex] is consistent, then there exists a [tex]\vec{b}[/tex] such that [tex]A \vec{x} = \vec{b}[/tex]. Since [tex] A\vec{x} = x_1\vec{w_1} + \cdots + x_n\vec{w_n} = \vec{b}[/tex], and since [tex]Span\{\vec{w_1}, \cdots, \vec{w_n}\} = c_1\vec{w_1} + \cdots + c_n\vec{w_n}[/tex] for some [tex]c_1, \ldots, c_n\in R^n[/tex], clearly [tex]\vec{b}\in Span\{\vec{w_1}, \cdots, \vec{w_n}\}[/tex].

To prove the other direction, let [tex]\vec{b}\in Span\{\vec{w_1}, \cdots, \vec{w_n}\}[/tex]. Then [tex] \vec{b} = c_1\vec{w_1} + \cdots + c_n\vec{w_n} = A \vec{c}[/tex], where [tex]\vec{c} = [ c_1 \cdots c_n ]^T [/tex]. By calling [tex]\vec{x} = \vec{c}[/tex], then there exists an [tex]\vec{x}[/tex] such that [tex]A \vec{x} = \vec{b}[/tex] is consistent, as desired.
 

What is a set of n vectors in R^n?

A set of n vectors in R^n refers to a collection of n vectors that belong to the n-dimensional vector space, R^n. These vectors are represented by n coordinates and can be added, subtracted, and multiplied by a scalar value.

How do you represent a set of n vectors in R^n?

A set of n vectors in R^n can be represented in various ways, such as using column or row matrices, or writing the coordinates of each vector in a list. For example, a set of 3 vectors in R^3 can be represented as {(1,2,3), (4,5,6), (7,8,9)}.

What is the significance of n in a set of n vectors in R^n?

The value of n represents the dimension of the vector space. In R^n, n refers to the number of coordinates or dimensions needed to represent a vector. Therefore, a set of n vectors in R^n means that each vector has n coordinates and belongs to an n-dimensional vector space.

Can a set of n vectors in R^n be linearly dependent?

Yes, a set of n vectors in R^n can be linearly dependent, meaning that one or more vectors in the set can be expressed as a linear combination of the other vectors. In this case, the set is said to be linearly dependent, and the vectors are not considered to be linearly independent.

What is the difference between a set of n vectors in R^n and a basis of R^n?

A set of n vectors in R^n is a collection of n vectors, while a basis of R^n is a set of linearly independent vectors that span the entire vector space. Therefore, a basis of R^n is a special type of set of n vectors in R^n that is used to represent all possible vectors in the n-dimensional vector space.

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