Minimization solution of three equations in two variables

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The discussion revolves around finding a minimization solution for three equations in two variables, suggesting the use of the distance from a point to a line formula to determine distances from an arbitrary point within the triangle formed by the equations. The idea is to square and sum these distances to minimize the function D(X0,Y0), which could potentially extend to fitting multiple lines or planes. The conversation also touches on the concept of optimization and regression solutions, highlighting the least squares method for solving the equation AX=Y when A^TA is nonsingular. This method, expressed as X=(A^TA)^{-1}A^TY, is noted for its simplicity and effectiveness in finding the closest solution. Overall, the discussion emphasizes the relationship between geometric interpretation and mathematical optimization techniques.
barryj
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Homework Statement
given these three equations: I know I have more equations than variables. However, isn't there a way to find the closest solution, some sort of regression solution?
2x - y = -3
-2x - y = -4
-2.1x - y = 4
Relevant Equations
2x - y = -3
-2x - y = -4
-2.1x - y = 4
I do not know the solution.
 
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I am thinking about how regression is performed. Let's assume I plot the three equations and they form a triange where they intersect. I can use the "distance from a point to a line" formula to get the distance from an arbitrary point , (X0,Y0) within the triangle to each of the lines. I think i could then square and add the distances and try to minimize the resulting function D(X0,Y0) . It seems that this could be extended to find the best fit of multiple lines or even planes. I guess this is the topic of optimization. I do not know if this is a good way or not.
 
barryj said:
isn't there a way to find the closest solution, some sort of regression solution?
Given ##AX=Y##, ##A^TAX=A^TY##.
IF ##A^TA## is nonsingular you have ##X=(A^TA)^{-1}A^TY##.
This can be shown to be the least sum squares solution.
 
Amazing! Hard to imagine it is this simple
Thanks.
 

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