Linear Algebra: A^tb=0 - Solving for Best Approximation of b in Col A

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Homework Help Overview

The discussion revolves around a linear algebra problem concerning the relationship between a vector \(\vec{b}\) and the column space of a matrix \(A\). The original poster is tasked with understanding the implications of the equation \(A^T\vec{b} = \vec{0}\) and how it relates to finding the best approximation of \(\vec{b}\) in the column space of \(A\).

Discussion Character

  • Exploratory, Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • Participants explore the implications of the orthogonality condition given by \(A^T\vec{b} = \vec{0}\). There is discussion about the relationship between \(\vec{b}\) and the column space of \(A\), with attempts to clarify the meaning of the null space and its connection to the approximation of \(\vec{b}\).

Discussion Status

The conversation is ongoing, with some participants providing hints and nudges towards understanding the problem. There is recognition of the need to clarify how to measure the quality of the approximation, indicating a productive direction in the discussion.

Contextual Notes

Participants are navigating the constraints of the problem, including the definitions of orthogonality and the properties of the null space in relation to the column space of \(A\). There is an emphasis on ensuring accurate statements regarding the relationship between \(\vec{b}\) and \(Nul(A^T)\).

ji707
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hi all, i was given a take home exam for my linear algebra course and i can't seem to find the answer to this problem.

Homework Statement


if A^T\vec{b} = \vec{0}
what can you say about ##\hat{b}## the vector in Col A which is the best approximation of
##\vec{b}##.

Homework Equations


##A^T A \vec{x} = A^T\vec{b}##

The Attempt at a Solution


I don't even know where to start,
I have a feeling that there is a way to show that ##\hat{b}## is equal to ##\vec{b}##. but I don't know how to go about finding this.
I've been thinking about it for a day already. any hints or nudges in the right direction would be very helpful please.
 
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start by considering the columns of A. Each component of A^T.b is the same as the dot product of a column of A with b.
 
I tried this not sure if its correct. i used this theorem

##(col A)^\perp = Nul A^T##
since i was given ## A^T\vec{b} = \vec{0} ##
##Nul A^T = \vec{b}##
and this means that ##\vec{b}## is already orthogonal to ##col A##
and because ##\vec{b} - \hat{b} = \perp## to ## Col A##
##\hat{b} = \vec{0}##

right? or am I completely off?
 
I think you're heading in the right direction - b is orthogonal to every column vector in A, so they are going to do a pretty poor job when used to approximate b

ji707 said:
##Nul A^T = \vec{b}##
this is not quite true

## \vec{b} \in Nul(A^T) ##
is more accurate

i think you've pretty much got it, but you need to outline how you measure how "good" an approximation is to tie it together
 

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