9.2 QR Decomposition

QR decomposition is a powerful tool that breaks down matrices into simpler parts. It's like taking apart a complex machine to understand how it works. This technique is closely tied to the Gram-Schmidt process, which helps create a set of perpendicular vectors.

By using QR decomposition, we can solve tricky math problems more easily. It's especially handy for dealing with systems of equations and finding the best-fit solutions. Think of it as a Swiss Army knife for tackling various linear algebra challenges.

QR Decomposition

Concept and Relation to Gram-Schmidt Process

Top images from around the web for Concept and Relation to Gram-Schmidt Process

• 

  Linear Algebra/Gram-Schmidt Orthogonalization - Wikibooks, open books for an open world View original

  Is this image relevant?

• 

  vector spaces - Orthonormal Sets and the Gram-Schmidt Procedure - Mathematics Stack Exchange View original

  Is this image relevant?

• 

  Linear Algebra/Gram-Schmidt Orthogonalization - Wikibooks, open books for an open world View original

  Is this image relevant?

• 

  vector spaces - Orthonormal Sets and the Gram-Schmidt Procedure - Mathematics Stack Exchange View original

  Is this image relevant?

1 of 2

Top images from around the web for Concept and Relation to Gram-Schmidt Process

• 

  Linear Algebra/Gram-Schmidt Orthogonalization - Wikibooks, open books for an open world View original

  Is this image relevant?

• 

  vector spaces - Orthonormal Sets and the Gram-Schmidt Procedure - Mathematics Stack Exchange View original

  Is this image relevant?

• 

  Linear Algebra/Gram-Schmidt Orthogonalization - Wikibooks, open books for an open world View original

  Is this image relevant?

• 

  vector spaces - Orthonormal Sets and the Gram-Schmidt Procedure - Mathematics Stack Exchange View original

  Is this image relevant?

1 of 2

• QR decomposition factorizes a matrix A into an orthogonal matrix Q and an upper triangular matrix R, such that A = QR
• The Gram-Schmidt process constructs an orthonormal basis from a set of linearly independent vectors
• QR decomposition can be computed using the Gram-Schmidt process by:
  1. Orthonormalizing the columns of the matrix A to form the matrix Q
  2. Calculating the upper triangular matrix R using the orthonormal basis
• The columns of Q form an orthonormal basis for the column space of A
• The matrix R represents the coordinates of the columns of A with respect to the orthonormal basis formed by the columns of Q

Applications

• QR decomposition is useful in various applications:
  • Solving linear systems
  • Least-squares problems
  • Eigenvalue computations
• QR decomposition provides a numerically stable method for solving these problems
• It avoids potential issues such as ill-conditioning that may arise in other methods (normal equations)

QR Decomposition with Gram-Schmidt

Performing QR Decomposition

• To perform QR decomposition using the Gram-Schmidt process:
  1. Initialize the first column of Q as the normalized first column of A
  2. For each subsequent column of A:
     • Subtract its projection onto the previous orthonormal vectors from itself
     • Normalize the resulting vector to obtain the corresponding column of Q
  3. Calculate the entries of the upper triangular matrix R as the dot products of the original columns of A with the corresponding orthonormal vectors in Q
• The Gram-Schmidt process ensures that the columns of Q are orthonormal (orthogonal to each other and have unit length)
• The resulting matrices Q and R satisfy the equation A = QR, where Q is an orthogonal matrix and R is an upper triangular matrix

Example

• Consider the matrix A = $\begin{bmatrix} 1 & 1 & 1 \\ 0 & 1 & 1 \\ 0 & 0 & 1 \end{bmatrix}$
• Applying the Gram-Schmidt process:
  1. The first column of Q is $\begin{bmatrix} 1 \\ 0 \\ 0 \end{bmatrix}$
  2. The second column of Q is $\begin{bmatrix} 0 \\ 1 \\ 0 \end{bmatrix}$
  3. The third column of Q is $\begin{bmatrix} 0 \\ 0 \\ 1 \end{bmatrix}$
• The resulting orthogonal matrix Q is $\begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix}$
• The upper triangular matrix R is $\begin{bmatrix} 1 & 1 & 1 \\ 0 & 1 & 1 \\ 0 & 0 & 1 \end{bmatrix}$
• A = QR holds true for this decomposition

Properties of QR Decomposition

Uniqueness and Existence

• QR decomposition is unique for a given matrix A if the diagonal entries of R are chosen to be positive
• The orthogonal matrix Q in the QR decomposition is not unique, as it can be multiplied by an orthogonal matrix from the right without changing the decomposition
• If A has full column rank, then the QR decomposition exists and is unique (up to the sign of the diagonal entries of R)

Orthonormality and Rank

• The columns of Q form an orthonormal basis for the column space of A
• The rows of Q^T form an orthonormal basis for the row space of A
• The rank of A is equal to the number of non-zero diagonal entries in R
  • This property can be used to determine the dimension of the column space and the null space of A
• The orthonormality of Q and the upper triangular structure of R provide useful properties for various applications

Applications of QR Decomposition

Solving Linear Systems

• QR decomposition can be used to solve linear systems of the form Ax = b:
  1. Compute the QR decomposition of A
  2. Solve the equivalent system Rx = Q^T b using back-substitution
• This approach is particularly useful for overdetermined systems (more equations than unknowns)
• QR decomposition provides a numerically stable method for solving linear systems

Least-Squares Problems

• For overdetermined systems, QR decomposition can be used to find the least-squares solution
  • The least-squares solution minimizes the Euclidean norm of the residual vector
• The least-squares solution can be obtained by solving the normal equations (A^T A)x = A^T b
  • QR decomposition can efficiently solve the normal equations
• QR decomposition avoids the potential ill-conditioning of the normal equations, making it a preferred method for solving least-squares problems
• When A has full column rank, the least-squares solution obtained using QR decomposition is unique and minimizes the Euclidean norm of the residual vector

Example

• Consider the overdetermined system Ax = b, where A = $\begin{bmatrix} 1 & 2 \\ 3 & 4 \\ 5 & 6 \end{bmatrix}$ and b = $\begin{bmatrix} 1 \\ 2 \\ 3 \end{bmatrix}$
• Computing the QR decomposition of A:
  • Q = $\begin{bmatrix} -0.1690 & -0.8452 & -0.5071 \\ -0.5071 & 0.5071 & -0.6977 \\ -0.8452 & 0.1690 & 0.5071 \end{bmatrix}$
  • R = $\begin{bmatrix} -5.9161 & -7.4374 \\ 0 & -0.8452 \end{bmatrix}$
• Solving Rx = Q^T b:
  • Q^T b = $\begin{bmatrix} -3.1623 \\ 0.5916 \end{bmatrix}$
  • Back-substitution yields x = $\begin{bmatrix} 0.5 \\ 0.2 \end{bmatrix}$
• The least-squares solution is x = $\begin{bmatrix} 0.5 \\ 0.2 \end{bmatrix}$, which minimizes the Euclidean norm of the residual vector