Least Squares Solution - Or is there?

In summary, the conversation discusses finding the least squares solution to a problem involving a matrix K and a given vector. It is mentioned that since the determinate of K is zero, there may be infinite least squares solutions. However, when attempting to find a solution by multiplying each side by K^T, no solution is found. The conversation also mentions the need to express the solution in the form x = w + z, where w is in the corange and z is in the kernel. There is also a mention of a calculation error in the last matrix provided.
  • #1
Mindscrape
1,861
1
I have a problem that says to find the least squares solution to
[tex]\newcommand{\colv}[2] {\left(\begin{array}{c} #1 \\ #2 \end{array}\right)}
K x = \colv{2}{2}[/tex] for
[tex] K = \left(
\begin{array}{cc}
1 & 2\\
2 & 4
\end{array} \right)[/tex]. Then express the solution in the form x = w + z, where w is in the corange, and z is in the kernel.

Since the determinate of the matrix K will be zero, then there should be infinite least squares solutions since the kernel is non-zero, correct? When I try to find a solution by multiplying each side by K^T, I get no solution rather than infinite solutions. Specifically the sytem

[tex] \newcommand{\colv}[2] {\left(\begin{array}{c} #1 \\ #2
\left(
\begin{array}{cc}
5 & 10\\
0 & 0
\end{array} \right) x = \colv{0}{10}[/tex]

How can I find the least square of a matrix with determinate zero? Everything I know is based off the fact that the matrix will be invertible.

*My last matrix is messed up somehow, it should be the augmented matrix {{5,10 | 5},{0,0 | 10}}.
 
Last edited:
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  • #2
Nevermind, I made a calculation error.
 

Related to Least Squares Solution - Or is there?

1. What is the Least Squares Solution method?

The Least Squares Solution method is a mathematical technique used to find the best fit line or curve for a set of data points. It minimizes the sum of the squared differences between the actual data points and the predicted values on the line or curve. This method is commonly used in regression analysis and can be applied to linear, polynomial, and exponential relationships.

2. How does the Least Squares Solution method work?

The Least Squares Solution method works by finding the line or curve that minimizes the sum of the squared errors between the actual data points and the predicted values. This is done by calculating the slope and intercept of the line or curve that passes through the data points, and then adjusting these values iteratively until the sum of the squared errors is minimized.

3. When is the Least Squares Solution method used?

The Least Squares Solution method is commonly used in regression analysis to determine the relationship between two or more variables. It is also used in engineering, physics, and other scientific fields to analyze data and make predictions.

4. What are the advantages of using the Least Squares Solution method?

One of the main advantages of the Least Squares Solution method is that it provides a simple and efficient way to find the best fit line or curve for a set of data points. It also allows for the analysis of complex relationships between variables and can handle large datasets. Additionally, it provides a measure of the accuracy of the predicted values.

5. Are there any limitations to the Least Squares Solution method?

While the Least Squares Solution method is widely used and effective in many cases, it does have some limitations. It assumes that the relationship between variables is linear, which may not always be the case. It also assumes that the errors in the data are normally distributed. Additionally, outliers in the data can significantly affect the results.

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