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Solutions to three projection and least square problems. The first problem involves finding the projection and residual of a vector on a subspace. The second problem deals with finding the least square linear fit to a given dataset. The third problem focuses on finding the least square solution to a system of linear equations. Each problem is solved using matrix algebra and the normal equations.
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Examples of projection/least square problems
Problem 1. Let S = R(A), where
(^) and b =
Find the projection p and residual r of b on S.
Solution. We have that
and AT^ b =
Solving the linear system AT^ Ac = AT^ b in c we obtain c = [110/ 203 , − 51 /203]T^. Hence
p = Ac = [94/ 203 , 43 / 203 , 373 /203]T
and r = b − p = [− 297 / 203 , 363 / 203 , 33 /203]T^.
Problem 2. Find the least square linear fit to the data
x − 1 0 1 2 y − 2 − 1 0 3.
Solution. Let
and b =
We have that
AT^ A =
and AT^ b =
Solving the linear system AT^ Ac = AT^ b in c we obtain c = [− 4 / 5 , 8 /5]T^. Hence
y = (− 4 /5)x + 8/ 5
is the least square linear fit to the data.
Problem 3. Let
A =
(^) and b =
Find the least square solution to the system Ax = b.
Solution. We have that
and AT^ b =
Solving the linear system AT^ Ax = AT^ b in x we obtain that
x = [1/ 2 , 1 /2]T
is the least square solution to Ax = b.