Minimization solution of three equations in two variables

In summary, the conversation discusses the idea of using a distance formula to find the best fit of multiple lines or planes in the topic of optimization. The speaker also mentions the concept of regression and how it can be simplified using the formula ##X=(A^TA)^{-1}A^TY## if ##A^TA## is nonsingular. This is known as the least sum squares solution. The conversation ends with the realization that this method is surprisingly simple.
  • #1
barryj
854
51
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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  • #2
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.
 
  • #3
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.
 
  • #4
Amazing! Hard to imagine it is this simple
Thanks.
 

Related to Minimization solution of three equations in two variables

What is the minimization solution of three equations in two variables?

The minimization solution of three equations in two variables refers to finding the values of the two variables that simultaneously satisfy all three equations while minimizing the overall error or difference between the equations.

Why is it important to find the minimization solution of three equations in two variables?

Finding the minimization solution allows us to accurately model and predict real-world phenomena by reducing the error between our equations and the actual data. It is also a fundamental problem in optimization and has numerous applications in fields such as economics, engineering, and physics.

What methods are commonly used to find the minimization solution of three equations in two variables?

The most commonly used methods include the substitution method, elimination method, and graphing method. Other more advanced techniques such as matrix operations, gradient descent, and calculus-based optimization can also be used.

What are the challenges of finding the minimization solution of three equations in two variables?

One of the main challenges is that there is no guaranteed method to find the exact solution for all cases. Depending on the equations and initial values, some methods may not converge or may give inaccurate results. It also requires a good understanding of algebra and problem-solving skills.

How can the minimization solution of three equations in two variables be applied in real-world scenarios?

The minimization solution can be used in various real-world scenarios such as finding the optimal production levels in a manufacturing process, determining the most profitable investment portfolio, or predicting the trajectory of a projectile. It can also be used in machine learning algorithms to minimize the error between predicted and actual values.

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