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QR factorization

returns
the R part of the QR decomposition `R`

= qr(`A`

)```
A
= Q*R
```

. Here, `A`

is an *m*-by-*n* matrix, `R`

is
an *m*-by-*n* upper triangular matrix,
and `Q`

is an *m*-by-*m* unitary
matrix.

`[`

returns an upper triangular
matrix `Q`

,`R`

,`P`

]
= qr(`A`

)`R`

, a unitary matrix `Q`

,
and a permutation matrix `P`

, such that ```
A*P
= Q*R
```

. If all elements of `A`

can be
approximated by the floating-point numbers, then this syntax chooses
the column permutation `P`

so that `abs(diag(R))`

is
decreasing. Otherwise, it returns `P = eye(n)`

.

`[`

returns
an upper triangular matrix `C`

,`R`

,`P`

]
= qr(`A`

,`B`

)`R`

, a matrix `C`

,
such that `C = Q'*B`

, and a permutation matrix `P`

,
such that `A*P = Q*R`

. If all elements of `A`

can
be approximated by the floating-point numbers, then this syntax chooses
the permutation matrix `P`

so that `abs(diag(R))`

is
decreasing. Otherwise, it returns `P = eye(n)`

. Here, `A`

and `B`

must
have the same number of rows.

`C`

, `R`

, and `P`

represent
the solution of the matrix equation `A*X = B`

as ```
X
= P*(R\C)
```

.

`___ = qr(___,'econ')`

returns
the "economy size" decomposition. If `A`

is an `m`

-by-`n`

matrix
with `m > n`

, then `qr`

computes
only the first `n`

columns of `Q`

and
the first `n`

rows of `R`

. For ```
m
<= n
```

, the syntaxes with `'econ'`

are
equivalent to the corresponding syntaxes without `'econ'`

.

When you use `'econ'`

, `qr`

always
returns the permutation information as a vector `p`

.

You can use `0`

instead of `'econ'`

.
For example, `[Q,R] = qr(A,0)`

is equivalent to ```
[Q,R]
= qr(A,'econ')
```

.

`___ = qr(___,'real')`

assumes
that input arguments and intermediate results are real, and therefore,
suppresses calls to `abs`

and `conj`

.
When you use this flag, `qr`

assumes that all symbolic
variables represent real numbers. When using this flag, ensure that
all numeric arguments are real numbers.

Use `'real'`

to avoid complex conjugates in
the result.

Compute the R part of the QR decomposition
of the `4`

-by-`4`

Wilkinson's eigenvalue
test matrix.

Create the `4`

-by-`4`

Wilkinson's
eigenvalue test matrix:

A = sym(wilkinson(4))

A = [ 3/2, 1, 0, 0] [ 1, 1/2, 1, 0] [ 0, 1, 1/2, 1] [ 0, 0, 1, 3/2]

Use the syntax with one output argument to return the R part of the QR decomposition without returning the Q part:

R = qr(A)

R = [ 13^(1/2)/2, (4*13^(1/2))/13, (2*13^(1/2))/13, 0] [ 0, (13^(1/2)*53^(1/2))/26, (10*13^(1/2)*53^(1/2))/689, (2*13^(1/2)*53^(1/2))/53] [ 0, 0, (53^(1/2)*381^(1/2))/106, (172*53^(1/2)*381^(1/2))/20193] [ 0, 0, 0, (35*381^(1/2))/762]

Compute the QR decomposition of the `3`

-by-`3`

Pascal
matrix.

Create the `3`

-by-`3`

Pascal
matrix:

A = sym(pascal(3))

A = [ 1, 1, 1] [ 1, 2, 3] [ 1, 3, 6]

Find the `Q`

and `R`

matrices
representing the QR decomposition of `A`

:

[Q,R] = qr(A)

Q = [ 3^(1/2)/3, -2^(1/2)/2, 6^(1/2)/6] [ 3^(1/2)/3, 0, -6^(1/2)/3] [ 3^(1/2)/3, 2^(1/2)/2, 6^(1/2)/6] R = [ 3^(1/2), 2*3^(1/2), (10*3^(1/2))/3] [ 0, 2^(1/2), (5*2^(1/2))/2] [ 0, 0, 6^(1/2)/6]

Verify that `A = Q*R`

using `isAlways`

:

isAlways(A == Q*R)

ans = 3×3 logical array 1 1 1 1 1 1 1 1 1

Using permutations helps increase numerical
stability of the QR factorization for floating-point matrices. The `qr`

function
returns permutation information either as a matrix or as a vector.

Set the number of significant decimal digits, used for variable-precision
arithmetic, to 10. Approximate the `3`

-by-`3`

symbolic
Hilbert matrix by floating-point numbers:

previoussetting = digits(10); A = vpa(hilb(3))

A = [ 1.0, 0.5, 0.3333333333] [ 0.5, 0.3333333333, 0.25] [ 0.3333333333, 0.25, 0.2]

First, compute the QR decomposition of `A`

without
permutations:

[Q,R] = qr(A)

Q = [ 0.8571428571, -0.5016049166, 0.1170411472] [ 0.4285714286, 0.5684855721, -0.7022468832] [ 0.2857142857, 0.6520863915, 0.7022468832] R = [ 1.166666667, 0.6428571429, 0.45] [ 0, 0.1017143303, 0.1053370325] [ 0, 0, 0.003901371573]

Compute the difference between `A`

and `Q*R`

.
The computed `Q`

and `R`

matrices
do not strictly satisfy the equality `A*P = Q*R`

because
of the round-off errors.

A - Q*R

ans = [ -1.387778781e-16, -3.989863995e-16, -2.064320936e-16] [ -3.469446952e-18, -8.847089727e-17, -1.084202172e-16] [ -2.602085214e-18, -6.591949209e-17, -6.678685383e-17]

To increase numerical stability of the QR decomposition, use
permutations by specifying the syntax with three output arguments.
For matrices that do not contain symbolic variables, expressions,
or functions, this syntax triggers pivoting, so that `abs(diag(R))`

in
the returned matrix `R`

is decreasing.

[Q,R,P] = qr(A)

Q = [ 0.8571428571, -0.4969293466, -0.1355261854] [ 0.4285714286, 0.5421047417, 0.7228063223] [ 0.2857142857, 0.6776309272, -0.6776309272] R = [ 1.166666667, 0.45, 0.6428571429] [ 0, 0.1054092553, 0.1016446391] [ 0, 0, 0.003764616262] P = 1 0 0 0 0 1 0 1 0

Check the equality `A*P = Q*R`

again. QR factorization
with permutations results in smaller round-off errors.

A*P - Q*R

ans = [ -3.469446952e-18, -4.33680869e-18, -6.938893904e-18] [ 0, -8.67361738e-19, -1.734723476e-18] [ 0, -4.33680869e-19, -1.734723476e-18]

Now, return the permutation information as a vector by using
the `'vector'`

argument:

[Q,R,p] = qr(A,'vector')

Q = [ 0.8571428571, -0.4969293466, -0.1355261854] [ 0.4285714286, 0.5421047417, 0.7228063223] [ 0.2857142857, 0.6776309272, -0.6776309272] R = [ 1.166666667, 0.45, 0.6428571429] [ 0, 0.1054092553, 0.1016446391] [ 0, 0, 0.003764616262] p = 1 3 2

Verify that `A(:,p) = Q*R`

:

A(:,p) - Q*R

ans = [ -3.469446952e-18, -4.33680869e-18, -6.938893904e-18] [ 0, -8.67361738e-19, -1.734723476e-18] [ 0, -4.33680869e-19, -1.734723476e-18]

Exact symbolic computations let you avoid roundoff errors:

A = sym(hilb(3)); [Q,R] = qr(A); A - Q*R

ans = [ 0, 0, 0] [ 0, 0, 0] [ 0, 0, 0]

Restore the number of significant decimal digits to its default setting:

digits(previoussetting)

You can use `qr`

to solve
systems of equations in a matrix form.

Suppose you need to solve the system of equations ```
A*X
= b
```

, where `A`

and `b`

are
the following matrix and vector:

A = sym(invhilb(5)) b = sym([1:5]')

A = [ 25, -300, 1050, -1400, 630] [ -300, 4800, -18900, 26880, -12600] [ 1050, -18900, 79380, -117600, 56700] [ -1400, 26880, -117600, 179200, -88200] [ 630, -12600, 56700, -88200, 44100] b = 1 2 3 4 5

Use `qr`

to find matrices `C`

and `R`

,
such that `C = Q'*B`

and `A = Q*R`

:

[C,R] = qr(A,b);

Compute the solution `X`

:

X = R\C

X = 5 71/20 197/70 657/280 1271/630

Verify that `X`

is the solution of the system ```
A*X
= b
```

using `isAlways`

:

isAlways(A*X == b)

ans = 5×1 logical array 1 1 1 1 1

When solving systems of equations that contain floating-point numbers, use QR decomposition with the permutation matrix or vector.

Suppose you need to solve the system of equations ```
A*X
= b
```

, where `A`

and `b`

are
the following matrix and vector:

previoussetting = digits(10); A = vpa([2 -3 -1; 1 1 -1; 0 1 -1]); b = vpa([2; 0; -1]);

Use `qr`

to find matrices `C`

and `R`

,
such that `C = Q'*B`

and `A = Q*R`

:

[C,R,P] = qr(A,b)

C = -2.110579412 -0.2132007164 0.7071067812 R = [ 3.31662479, 0.3015113446, -1.507556723] [ 0, 1.705605731, -1.492405014] [ 0, 0, 0.7071067812] P = 0 0 1 1 0 0 0 1 0

Compute the solution `X`

:

X = P*(R\C)

X = 1.0 -0.25 0.75

Alternatively, return the permutation information as a vector:

[C,R,p] = qr(A,b,'vector')

C = -2.110579412 -0.2132007164 0.7071067812 R = [ 3.31662479, 0.3015113446, -1.507556723] [ 0, 1.705605731, -1.492405014] [ 0, 0, 0.7071067812] p = 2 3 1

In this case, compute the solution `X`

as follows:

X(p,:) = R\C

X = 1.0 -0.25 0.75

Restore the number of significant decimal digits to its default setting:

digits(previoussetting)

Use `'econ'`

to compute the
“economy size” QR decomposition.

Create a matrix that consists of the first two columns of the `4`

-by-`4`

Pascal
matrix:

A = sym(pascal(4)); A = A(:,1:2)

A = [ 1, 1] [ 1, 2] [ 1, 3] [ 1, 4]

Compute the QR decomposition for this matrix:

[Q,R] = qr(A)

Q = [ 1/2, -(3*5^(1/2))/10, (3^(1/2)*10^(1/2))/10, 0] [ 1/2, -5^(1/2)/10, -(2*3^(1/2)*10^(1/2))/15, 6^(1/2)/6] [ 1/2, 5^(1/2)/10, -(3^(1/2)*10^(1/2))/30, -6^(1/2)/3] [ 1/2, (3*5^(1/2))/10, (3^(1/2)*10^(1/2))/15, 6^(1/2)/6] R = [ 2, 5] [ 0, 5^(1/2)] [ 0, 0] [ 0, 0]

Now, compute the “economy size” QR decomposition
for this matrix. Because the number of rows exceeds the number of
columns, `qr`

computes only the first `2`

columns
of `Q`

and the first `2`

rows of `R`

.

[Q,R] = qr(A,'econ')

Q = [ 1/2, -(3*5^(1/2))/10] [ 1/2, -5^(1/2)/10] [ 1/2, 5^(1/2)/10] [ 1/2, (3*5^(1/2))/10] R = [ 2, 5] [ 0, 5^(1/2)]

Use the `'real'`

flag to avoid
complex conjugates in the result.

Create a matrix, one of the elements of which is a variable:

syms x A = [1 2; 3 x]

A = [ 1, 2] [ 3, x]

Compute the QR factorization of this matrix. By default, `qr`

assumes
that `x`

represents a complex number, and therefore,
the result contains expressions with the `abs`

function.

[Q,R] = qr(A)

Q = [ 10^(1/2)/10, -((3*x)/10 - 9/5)/(abs(x/10 - 3/5)^2... + abs((3*x)/10 - 9/5)^2)^(1/2)] [ (3*10^(1/2))/10, (x/10 - 3/5)/(abs(x/10 - 3/5)^2... + abs((3*x)/10 - 9/5)^2)^(1/2)] R = [ 10^(1/2), (10^(1/2)*(3*x + 2))/10] [ 0, (abs(x/10 - 3/5)^2 + abs((3*x)/10 - 9/5)^2)^(1/2)]

When you use `'real'`

, `qr`

assumes
that all symbolic variables represent real numbers, and can return
shorter results:

[Q,R] = qr(A,'real')

Q = [ 10^(1/2)/10, -((3*x)/10 - 9/5)/(x^2/10 - (6*x)/5... + 18/5)^(1/2)] [ (3*10^(1/2))/10, (x/10 - 3/5)/(x^2/10 - (6*x)/5... + 18/5)^(1/2)] R = [ 10^(1/2), (10^(1/2)*(3*x + 2))/10] [ 0, (x^2/10 - (6*x)/5 + 18/5)^(1/2)]

The upper triangular matrix

`R`

satisfies the following condition:`R = chol(A'*A)`

.The arguments

`'econ'`

and`0`

only affect the shape of the returned matrices.Calling

`qr`

for numeric matrices that are not symbolic objects (not created by`sym`

,`syms`

, or`vpa`

) invokes the MATLAB^{®}`qr`

function.If you use

`'matrix'`

instead of`'vector'`

, then`qr`

returns permutation matrices, as it does by default. If you use`'matrix'`

and`'econ'`

, then`qr`

throws an error.Matrix computations involving many symbolic variables can be slow. To increase the computational speed, reduce the number of symbolic variables by substituting the given values for some variables.