Least Square Solution(zeros in one row)

In summary, the problem is asking for a least squares solution to Ax=C and the residual error. The solution involves finding the RREF of the transpose of A times A augmented with the transpose of A times C. The bottom row of the RREF indicates an infinite amount of solutions, which suggests that A is not full rank. To proceed, the 2nd equation can be examined to determine the rank deficiency of A. The 1st equation may also provide helpful information.
  • #1
rey242
41
0

Homework Statement


Give a least squares solution to Ax=C and give the residual error

A=
-1, 1, 2;
1, -1, 0;
1, -1, 2;

C=
-1;
-1;
2;


Homework Equations



Residual Error= |Ax-C|

The Attempt at a Solution


I have done an RREF on the Transpose of A times A augmented with the Transpose of A time C and got this:
1, -1, 0, 2/3;
0, 0, 1, 1/4;
0, 0, 0, 0;

I'm not sure what I should do about the bottom row... I know that SHOULD be always an LSQ but it doesn't seem to come out. I know that bottom row indicates an infinite amount of solutions but not sure how to proceed next. Please help
 
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  • #2
Does anyone have any clue?

If it helps, I tried to set the third variable as a number and first and second as equations in terms of each other (i.e 1st in terms of 2nd).
 
  • #3
I presume this is a linear algebra class and you've learned about rank and rank deficiency. Is A full rank?
 
  • #4
not in precise terms...I know the columm space of A is 3x2. I looked up rank in wikipedia...
 
  • #5
I suggest you look at the 2nd equation. What does that tell you? Then, look at the 1st equation. It may help to write them out.
 

1. What is the concept of Least Square Solution?

The Least Square Solution is a mathematical method used to find the best fitting line or curve for a set of data points. It minimizes the sum of the squared distances between the data points and the line or curve.

2. How does Least Square Solution handle data with outliers?

Least Square Solution is sensitive to outliers, as it gives more weight to points that are further away from the line or curve. This can result in a skewed fit, so it may not be the best method to use if there are significant outliers in the data.

3. What is the difference between Least Square Solution and Ordinary Least Squares?

Ordinary Least Squares (OLS) is a specific type of Least Square Solution that is used for linear regression. OLS assumes that the errors in the data follow a normal distribution, while Least Square Solution can be applied to any type of data, regardless of the distribution of errors.

4. Can Least Square Solution be used for non-linear data?

Yes, Least Square Solution can be used for non-linear data. However, it may not provide the best fit for non-linear data, as it is primarily designed for linear regression. There are other methods, such as non-linear least squares, that are better suited for non-linear data.

5. How is the accuracy of Least Square Solution evaluated?

The accuracy of Least Square Solution is evaluated by calculating the sum of squared errors (SSE), which is the sum of the squared distances between the data points and the line or curve. A lower SSE indicates a better fit, but it is important to also consider other factors, such as the number of data points and the complexity of the model, when evaluating the accuracy of Least Square Solution.

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