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QRDecomposition—Wolfram Language Documentation

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• See Also
  • SchurDecomposition
  • LUDecomposition
  • SingularValueDecomposition
  • JordanDecomposition
  • CholeskyDecomposition
  • HessenbergDecomposition
  • Orthogonalize
• Related Guides
  • Matrix Decompositions
  • Matrices and Linear Algebra
  • Matrix-Based Minimization
• Tech Notes
  • Advanced Matrix Operations
  • Implementation Notes: Numerical and Related Functions

QRDecomposition[m]

yields the QR decomposition for a numerical matrix m. The result is a list {q,r}, where q is a unitary matrix and r is an upper‐triangular matrix.

The decomposition of a 2×2 matrix into a unitary (orthogonal) matrix and upper triangular matrix :

Verify that :

Compute the QR decomposition for a 3×2 matrix with exact values:

is the original matrix:

Compute the QR decomposition for a 2×3 matrix with approximate numerical values:

is the original matrix:

Find the QR decomposition of a machine-precision matrix:

Format the result:

QR decomposition for a complex matrix:

Use QRDecomposition for an exact matrix:

QR decomposition for an arbitrary-precision matrix:

Use QRDecomposition with a symbolic matrix:

The QR decomposition for a large numerical matrix is computed efficiently:

QR decomposition of a non-square matrix:

Find the QR decomposition for a sparse matrix:

QR decompositions of structured matrices:

Use with a QuantityArray structured matrix that has consistent units:

The matrix is dimensionless; the matrix gets the units:

QR decomposition of an IdentityMatrix consists of two identity matrices:

QR decomposition of HilbertMatrix:

Compute the QR decomposition using machine arithmetic with pivoting:

The elements along the diagonal of r are in order of decreasing magnitude:

The matrix p is a permutation matrix:

QRDecomposition satisfies m.p==ConjugateTranspose[q].r:

Find an orthonormal basis for the column space of the following matrix , and then use that basis to find a QR factorization of :

Compute the dimensions of :

Define as the column of and as the element of the corresponding Gram–Schmidt basis:

Let be the matrix whose rows are the :

Let be the matrix whose elements are the components of along the basis vector:

Confirm that :

This is the same result as given by QRDecomposition:

Compare QR decompositions found using Orthogonalize and QRDecomposition for the following matrix :

Let be the result of applying Orthogonalize to the columns of :

Let equal :

Confirm that :

This is the same result as given by QRDecomposition:

Compare QR decompositions found using Orthogonalize and QRDecomposition for the following matrix :

Let be the result of applying Orthogonalize to the complex-conjugated columns of :

Let equal :

Confirm that :

Up to sign, this is the same result as given by QRDecomposition:

For some applications, it use useful to compute a so-called full QR decomposition, in which the is square (and thus unitary) and has the same dimensions as the input matrix. Compute the full QR decomposition for the following matrix :

There are only two linearly independent columns, so and each have only two rows:

Use NullSpace to find vectors outside the span of the rows of , then orthogonalize the complete set:

This matrix is unitary:

Simply pad the matrix with zeros to make it the same shape as :

Verify that this is also a valid QR decomposition:

Use the QR decomposition to find the that minimizes for the following matrix and vector :

Compute decomposition of :

Since , , and the normal equations can be recast as :

As is invertible (because the columns of are linearly independent), the solution is :

Confirm the result using LeastSquares:

Use the QR decomposition to solve for the following matrix and vector :

Compute the QR decomposition of , which gives an invertible , as has linearly independent rows:

Let as if solving the least-squares problem:

As the columns of span , must be a solution of the equation:

QRDecomposition can be used to find a best-fit curve to data. Consider the following data:

Extract the and coordinates from the data:

Let have the columns and , so that minimizing will be fitting to a line :

As the columns of are linearly independent, the coefficients for a linear least‐squares fit are :

Verify the coefficients using Fit:

Plot the best-fit curve along with the data:

Find the best-fit parabola to the following data:

Extract the and coordinates from the data:

Let have the columns , and , so that minimizing will be fitting to :

As the columns of are linearly independent, the coefficients for a least‐squares fit are :

Verify the coefficients using Fit:

Plot the best-fit curve along with the data:

m is a 3×4 matrix:

Compute the QR decomposition:

The rows of q are orthonormal:

r is upper triangular:

m is equal to ConjugateTranspose[q].r:

If is an matrix, the matrix will have columns and the matrix columns:

QRDecomposition computes the "thin" decomposition, where and have MatrixRank[m] rows:

If m is real-valued and invertible, the matrix of its QR decomposition is orthogonal:

If m is invertible, the matrix of its QR decomposition is unitary:

If a is an matrix and MatrixRank[a]==n, the matrix of its QR decomposition is unitary:

If a is an matrix and MatrixRank[a]==m, the matrix of its QR decomposition is invertible:

Moreover, PseudoInverse[a]==Inverse[r].q:

Orthogonalize can be used to compute a QR decomposition:

For an approximate matrix, it is typically different from the one found by QRDecomposition:

LeastSquares and QRDecomposition can both be used to solve the least-squares problem:

The Cholesky decomposition of coincides with 's QR decomposition up to phase:

Compute CholeskyDecomposition[ConjugateTranspose[m].]m:

Find the QR decomposition of :

is the same as except for the choice of phase for each row:

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