Linear least-squares method and row multiplication of matrix

In summary, the least-squares method can be used to obtain an approximative solution for an overdetermined equation system in matrix form. This solution, called "c", can be affected by multiplying a row of the system with a constant. However, if the system is consistent, the same solution will be obtained for both the original and modified systems. This is because the least-squares method minimizes the sum of deviations, and multiplying one equation by a constant changes the weight of that equation in the sum.
  • #1
Mesud1
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Suppose that I have an overdetermined equation system in matrix form:

Ax = b

Where x and b are column vectors, and A has the same number of rows as b, and x has less rows than both.

The least-squares method could be used here to obtain the best possible approximative solution. Let's call this solution "c".

Now, suppose I multiply some row of the equation system with a constant k. Let's say this row is the second row. In that case, I must multiply the 2nd row of A with k, as well as the 2nd row of b. This yields a new equation system, let's write it as:

Bx = d

If I use the method of least squares on the second system, I get a new solution that is different from c. Why is the solution different? Since I performed an elementary row operation on the first system to obtain the second system, shouldn't the two systems be equivalent, and therefore have the same least-squares solution?

When I did the same thing with a consistent system, I got the same solution for both systems.
 
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  • #2
The least squares method minimizes the sum of the deviations of the left hand from the right hand side. If you multiply one equation by c, this equation gets more weight in the sum and the optimal solution will be different. This doesn't happen if all equations can be fulfilled identically.
 
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  • #3
DrDu said:
The least squares method minimizes the sum of the deviations of the left hand from the right hand side. If you multiply one equation by c, this equation gets more weight in the sum and the optimal solution will be different. This doesn't happen if all equations can be fulfilled identically.

Makes perfect sense, thank you.
 

FAQ: Linear least-squares method and row multiplication of matrix

1. What is the linear least-squares method?

The linear least-squares method is a statistical technique used to find the best fitting line or curve for a set of data points. It minimizes the sum of the squared differences between the observed data points and the predicted values from the line or curve.

2. How is the linear least-squares method calculated?

The linear least-squares method is calculated by finding the slope and y-intercept of the line or curve that minimizes the sum of the squared differences between the observed data points and the predicted values. This is typically done using a formula or by using a computer program.

3. What is row multiplication of a matrix?

Row multiplication of a matrix is a mathematical operation where each element in a row of a matrix is multiplied by a constant. This is typically done to transform a matrix and can be used in conjunction with other operations to solve systems of equations.

4. How is row multiplication used in the linear least-squares method?

In the linear least-squares method, row multiplication is used to transform the original data points into a form that can be used to calculate the slope and y-intercept of the best fitting line or curve. This transformation is necessary to make the calculations more efficient and accurate.

5. What is the relationship between the linear least-squares method and matrix operations?

The linear least-squares method is closely related to matrix operations, specifically row multiplication, because it involves transforming the original data points into a matrix form in order to find the best fitting line or curve. This method also utilizes matrix operations to calculate the slope and y-intercept of the line or curve.

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