How can QR decomposition be used to solve least squares problems?

In summary, the problem asks for the least squares solution of a set of equations, with the added requirement of using QR decomposition. The solution involves finding the QR factorization of A, substituting it into the least squares equation, and solving for x using R^{-1}Q^Tb.
  • #1
epkid08
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Homework Statement


Okay so I'm supposed to find the least squares solution of a set of equations, which I can do, but it adds that I must use QR decomposition. I don't really know how to apply QR decomposition to this problem.

Problem: Find the least squares solution of
[tex]x_1 + x_2 = 4[/tex]
[tex]2x_1+x_2 = -2[/tex]
[tex]x_1 - x_2 = 1[/tex]

Use your answer to find the point on the plane spanned by (1,1,2) and (1,-3,1) that is closest to (1,4,3).



Homework Equations





The Attempt at a Solution

 
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  • #2
Forget the least squares stuff for a moment. Find the QR factorization of A, so we have A = QR. For least squares, you need to solve the equation [itex]A^TAx = A^Tb[/itex] right? So substitute in A = QR to get [itex](QR)^T(QR) = R^TQ^TQR[/itex]. The whole gimmick of orthonormal matrices such as Q is that [itex]Q^TQ = I[/itex], so we have [itex]A^TAx = R^TRx = (QR)^Tb[/itex]. So [itex]R^TRx= R^TQ^Tb[/itex]. sp the least squares equation comes down to [itex]Rx = Q^Tb[/itex]. But R is invertible, so the least squares solution is just

[tex]x = R^{-1}Q^Tb[/tex]
 

Related to How can QR decomposition be used to solve least squares problems?

What is QR Decomposition Application?

QR Decomposition Application is a mathematical algorithm used for decomposing a matrix into an orthogonal matrix and an upper triangular matrix. It is commonly used for solving systems of linear equations and for finding the eigenvalues and eigenvectors of a matrix.

How is QR Decomposition Application used in real-world applications?

QR Decomposition Application has a wide range of applications in fields such as engineering, physics, and data science. It is commonly used in signal processing, image processing, and machine learning algorithms such as principal component analysis.

What are the advantages of using QR Decomposition Application?

QR Decomposition Application is numerically stable and efficient, making it a popular choice for solving large and complex systems of equations. It also allows for easier computation of the inverse and determinant of a matrix.

What is the difference between QR Decomposition and LU Decomposition?

QR Decomposition and LU Decomposition are two different methods for decomposing a matrix. While QR Decomposition uses orthogonal matrices, LU Decomposition uses lower and upper triangular matrices. QR Decomposition is more stable for solving systems of equations, while LU Decomposition is more efficient for finding determinants and inverses of a matrix.

How does QR Decomposition Application handle matrices with complex numbers?

QR Decomposition Application can be extended to handle matrices with complex numbers by using the complex conjugate transpose of the original matrix. This allows for the decomposition of complex matrices into a unitary matrix and an upper triangular matrix, similar to the decomposition of real matrices.

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