Prove Least Squares Equation Has Solution

In summary, the conversation discusses the least squares method and the Least squares equation, which is used to find the best approximation for a vector x* that satisfies A^T A x^*= A^T b. The question is posed on how to prove that there is always a solution to this equation, even in the case where A^T A is not invertible. Possible methods for finding a solution in this case are using criteria such as minimum norm or showing that the columns of A^T form the same column space as the columns of A^TA. However, it is unclear how to prove that A^T b is in the column space of A^TA.
  • #1
dirk_mec1
761
13

Homework Statement



In the least squares method the vector x* that is the best approximation to b statisfies the Least squares equation:

[tex]A^T A x^*= A^T b [/tex]

Prove that there's always a solution to this equation.

Homework Equations


-

The Attempt at a Solution


I distinct 2 situations [tex]A^T A [/tex] is invertible and it isn't invertible. If it's invertible then there's no problem [tex]x^*= (A^T A)^{-1} A^T b [/tex]

But how I prove that it works in the non-invertible case?
 
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  • #2
dirk_mec1 said:

Homework Statement



In the least squares method the vector x* that is the best approximation to b statisfies the Least squares equation:

[tex]A^T A x^*= A^T b [/tex]

Prove that there's always a solution to this equation.

Homework Equations

The Attempt at a Solution


I distinct 2 situations [tex]A^T A [/tex] is invertible and it isn't invertible. If it's invertible then there's no problem [tex]x^*= (A^T A)^{-1} A^T b [/tex]

But how I prove that it works in the non-invertible case?

If it is not invertible you need another criteria to get a unique solution. For instance you could require that x has minimum norm. In which case use could use the pseudo inverse which is based on singular value decomposition. However, you do not required a unique solution in the above question. So perhaps you could try showing that the columns of A^T form the same column space as the columns of A^TA.
 
  • #3
John Creighto said:
If it is not invertible you need another criteria to get a unique solution. For instance you could require that x has minimum norm. In which case use could use the pseudo inverse which is based on singular value decomposition. However, you do not required a unique solution in the above question. So perhaps you could try showing that the columns of A^T form the same column space as the columns of A^TA.

Don't you mean the columns of [tex]A^T b[/tex] are in the span of the columns of [tex]A^T A[/tex]? If so I don't understand how to prove such a thing.
 
  • #4
I've thought about it and I seriously don't know how to prove that the A^Tb is in the column space of A^TA. Can someone help me?
 

What is the Least Squares Equation?

The Least Squares Equation is a mathematical formula used to determine the best fit line for a set of data points. It is commonly used in regression analysis to find the line that minimizes the sum of the squared distance between each data point and the line.

Why is it important to prove that the Least Squares Equation has a solution?

Proving that the Least Squares Equation has a solution is important because it ensures that the calculated best fit line is a valid and accurate representation of the data. It also allows for further analysis and interpretation of the data using statistical methods.

How is the Least Squares Equation solved?

The Least Squares Equation can be solved using various methods, such as the normal equations or the gradient descent algorithm. These methods involve finding the values of the coefficients that minimize the sum of squared errors between the data points and the line.

What are the assumptions made in using the Least Squares Equation?

The Least Squares Equation assumes that the relationship between the variables being analyzed is linear and that the data points are independent and normally distributed. It also assumes that the errors in the data are random and have equal variances.

Can the Least Squares Equation be used for non-linear relationships?

No, the Least Squares Equation can only be used for linear relationships. If the relationship between the variables is non-linear, other regression methods such as polynomial regression or exponential regression should be used instead.

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