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

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SUMMARY

The discussion centers on solving the linear algebra problem defined by the equation AT𝑏 = 0, which indicates that vector 𝑏 is orthogonal to the column space of matrix A. Participants clarify that the best approximation of 𝑏 in the column space of A, denoted as 𝑏̂, is the zero vector, as 𝑏 is already orthogonal to all column vectors of A. The correct interpretation is that 𝑏 belongs to the null space of AT, expressed as 𝑏 ∈ Nul(AT), confirming that 𝑏 cannot be approximated effectively by any linear combination of the columns of A.

PREREQUISITES
  • Understanding of linear transformations and their properties
  • Familiarity with the concepts of orthogonality and null spaces
  • Knowledge of matrix operations, specifically transposition and multiplication
  • Basic proficiency in linear algebra, including the interpretation of column spaces
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  • Study the properties of orthogonal projections in linear algebra
  • Learn about the relationship between null spaces and column spaces in matrix theory
  • Explore the concept of least squares approximation in the context of linear regression
  • Investigate the implications of the Rank-Nullity Theorem in linear algebra
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Students studying linear algebra, educators teaching matrix theory, and anyone interested in understanding orthogonal projections and approximations in vector spaces.

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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