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{{task|Matrices}}[[Category:Mathematics]] Any rectangular matrix can be decomposed to a product of an orthogonal matrix and an upper (right) triangular matrix , as described in [[wp:QR decomposition|QR decomposition]].
'''Task'''
Demonstrate the QR decomposition on the example matrix from the [[wp:QR_decomposition#Example_2|Wikipedia article]]:
::
and the usage for linear least squares problems on the example from [[Polynomial_regression]]. The method of [[wp: Householder transformation|Householder reflections]] should be used:
'''Method'''
Multiplying a given vector , for example the first column of matrix , with the Householder matrix , which is given as
::
reflects about a plane given by its normal vector . When the normal vector of the plane is given as
::
then the transformation reflects onto the first standard basis vector
::
which means that all entries but the first become zero. To avoid numerical cancellation errors, we should take the opposite sign of :
::
and normalize with respect to the first element:
::
The equation for thus becomes:
::
or, in another form
::
with ::
Applying on then gives
::
and applying on the matrix zeroes all subdiagonal elements of the first column:
::
In the second step, the second column of , we want to zero all elements but the first two, which means that we have to calculate with the first column of the ''submatrix'' (denoted *), not on the whole second column of .
To get , we then embed the new into an identity:
::
This is how we can, column by column, remove all subdiagonal elements of and thus transform it into .
::
The product of all the Householder matrices , for every column, in reverse order, will then yield the orthogonal matrix .
::
The QR decomposition should then be used to solve linear least squares ([[Multiple regression]]) problems by solving
::
When is not square, i.e. we have to cut off the zero padded bottom rows.
::
and the same for the RHS:
::
Finally, solve the square upper triangular system by back substitution:
::
Ada
Output matches that of Matlab solution, not tested with other matrices.
with Ada.Text_IO; use Ada.Text_IO;
with Ada.Numerics.Real_Arrays; use Ada.Numerics.Real_Arrays;
with Ada.Numerics.Generic_Elementary_Functions;
procedure QR is
procedure Show (mat : Real_Matrix) is
package FIO is new Ada.Text_IO.Float_IO (Float);
begin
for row in mat'Range (1) loop
for col in mat'Range (2) loop
FIO.Put (mat (row, col), Exp => 0, Aft => 4, Fore => 5);
end loop;
New_Line;
end loop;
end Show;
function GetCol (mat : Real_Matrix; n : Integer) return Real_Matrix is
column : Real_Matrix (mat'Range (1), 1 .. 1);
begin
for row in mat'Range (1) loop
column (row, 1) := mat (row, n);
end loop;
return column;
end GetCol;
function Mag (mat : Real_Matrix) return Float is
sum : Real_Matrix := Transpose (mat) * mat;
package Math is new Ada.Numerics.Generic_Elementary_Functions
(Float);
begin
return Math.Sqrt (sum (1, 1));
end Mag;
function eVect (col : Real_Matrix; n : Integer) return Real_Matrix is
vect : Real_Matrix (col'Range (1), 1 .. 1);
begin
for row in col'Range (1) loop
if row /= n then vect (row, 1) := 0.0;
else vect (row, 1) := 1.0; end if;
end loop;
return vect;
end eVect;
function Identity (n : Integer) return Real_Matrix is
mat : Real_Matrix (1 .. n, 1 .. n) := (1 .. n => (others => 0.0));
begin
for i in Integer range 1 .. n loop mat (i, i) := 1.0; end loop;
return mat;
end Identity;
function Chop (mat : Real_Matrix; n : Integer) return Real_Matrix is
small : Real_Matrix (n .. mat'Length (1), n .. mat'Length (2));
begin
for row in small'Range (1) loop
for col in small'Range (2) loop
small (row, col) := mat (row, col);
end loop;
end loop;
return small;
end Chop;
function H_n (inmat : Real_Matrix; n : Integer)
return Real_Matrix is
mat : Real_Matrix := Chop (inmat, n);
col : Real_Matrix := GetCol (mat, n);
colT : Real_Matrix (1 .. 1, mat'Range (1));
H : Real_Matrix := Identity (mat'Length (1));
Hall : Real_Matrix := Identity (inmat'Length (1));
begin
col := col - Mag (col) * eVect (col, n);
col := col / Mag (col);
colT := Transpose (col);
H := H - 2.0 * (col * colT);
for row in H'Range (1) loop
for col in H'Range (2) loop
Hall (n - 1 + row, n - 1 + col) := H (row, col);
end loop;
end loop;
return Hall;
end H_n;
A : constant Real_Matrix (1 .. 3, 1 .. 3) := (
(12.0, -51.0, 4.0),
(6.0, 167.0, -68.0),
(-4.0, 24.0, -41.0));
Q1, Q2, Q3, Q, R: Real_Matrix (1 .. 3, 1 .. 3);
begin
Q1 := H_n (A, 1);
Q2 := H_n (Q1 * A, 2);
Q3 := H_n (Q2 * Q1* A, 3);
Q := Transpose (Q1) * Transpose (Q2) * TransPose(Q3);
R := Q3 * Q2 * Q1 * A;
Put_Line ("Q:"); Show (Q);
Put_Line ("R:"); Show (R);
end QR;
{{out}}
Q:
0.8571 -0.3943 -0.3314
0.4286 0.9029 0.0343
-0.2857 0.1714 -0.9429
R:
14.0000 21.0000 -14.0000
-0.0000 175.0000 -70.0000
-0.0000 0.0000 35.0000
Axiom
The following provides a generic QR decomposition for arbitrary precision floats, double floats and exact calculations:
)abbrev package TESTP TestPackage
TestPackage(R:Join(Field,RadicalCategory)): with
unitVector: NonNegativeInteger -> Vector(R)
"/": (Vector(R),R) -> Vector(R)
"^": (Vector(R),NonNegativeInteger) -> Vector(R)
solveUpperTriangular: (Matrix(R),Vector(R)) -> Vector(R)
signValue: R -> R
householder: Vector(R) -> Matrix(R)
qr: Matrix(R) -> Record(q:Matrix(R),r:Matrix(R))
lsqr: (Matrix(R),Vector(R)) -> Vector(R)
polyfit: (Vector(R),Vector(R),NonNegativeInteger) -> Vector(R)
== add
unitVector(dim) ==
out := new(dim,0@R)$Vector(R)
out(1) := 1@R
out
v:Vector(R) / a:R == map((vi:R):R +-> vi/a, v)$Vector(R)
v:Vector(R) ^ n:NonNegativeInteger == map((vi:R):R +-> vi^n, v)$Vector(R)
solveUpperTriangular(r,b) ==
n := ncols r
x := new(n,0@R)$Vector(R)
for k in n..1 by -1 repeat
index := min(n,k+1)
x(k) := (b(k)-reduce("+",subMatrix(r,k,k,index,n)*x.(index..n)))/r(k,k)
x
signValue(r) ==
R has (sign: R -> Integer) => coerce(sign(r)$R)$R
zero? r => r
if sqrt(r*r) = r then 1 else -1
householder(a) ==
m := #a
u := a + length(a)*signValue(a(1))*unitVector(m)
v := u/u(1)
beta := (1+1)/dot(v,v)
scalarMatrix(m,1) - beta*transpose(outerProduct(v,v))
qr(a) ==
(m,n) := (nrows a, ncols a)
qm := scalarMatrix(m,1)
rm := copy a
for i in 1..(if m=n then n-1 else n) repeat
x := column(subMatrix(rm,i,m,i,i),1)
h := scalarMatrix(m,1)
setsubMatrix!(h,i,i,householder x)
qm := qm*h
rm := h*rm
[qm,rm]
lsqr(a,b) ==
dc := qr a
n := ncols(dc.r)
solveUpperTriangular(subMatrix(dc.r,1,n,1,n),transpose(dc.q)*b)
polyfit(x,y,n) ==
a := new(#x,n+1,0@R)$Matrix(R)
for j in 0..n repeat
setColumn!(a,j+1,x^j)
lsqr(a,y)
This can be called using:
m := matrix [[12, -51, 4], [6, 167, -68], [-4, 24, -41]];
qr m
x := vector [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
y := vector [1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321];
polyfit(x, y, 2)
With output in exact form:
qr m
+ 6 69 58 +
|- - --- --- |
| 7 175 175 |
| | +- 14 - 21 14 +
| 3 158 6 | | |
[q= |- - - --- - ---|,r= | 0 - 175 70 |]
| 7 175 175| | |
| | + 0 0 - 35+
| 2 6 33 |
| - - -- -- |
+ 7 35 35 +
Type: Record(q: Matrix(AlgebraicNumber),r: Matrix(AlgebraicNumber))
polyfit(x, y, 2)
[1,2,3]
Type: Vector(AlgebraicNumber)
The calculations are comparable to those from the default QR decomposition in R.
BBC BASIC
{{works with|BBC BASIC for Windows}} Makes heavy use of BBC BASIC's matrix arithmetic.
*FLOAT 64
@% = &2040A
INSTALL @lib$+"ARRAYLIB"
REM Test matrix for QR decomposition:
DIM A(2,2)
A() = 12, -51, 4, \
\ 6, 167, -68, \
\ -4, 24, -41
REM Do the QR decomposition:
DIM Q(2,2), R(2,2)
PROCqrdecompose(A(), Q(), R())
PRINT "Q:"
PRINT Q(0,0), Q(0,1), Q(0,2)
PRINT Q(1,0), Q(1,1), Q(1,2)
PRINT Q(2,0), Q(2,1), Q(2,2)
PRINT "R:"
PRINT R(0,0), R(0,1), R(0,2)
PRINT R(1,0), R(1,1), R(1,2)
PRINT R(2,0), R(2,1), R(2,2)
REM Test data for least-squares solution:
DIM x(10) : x() = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
DIM y(10) : y() = 1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321
REM Do the least-squares solution:
DIM a(10,2), q(10,10), r(10,2), t(10,10), b(10), z(2)
FOR i% = 0 TO 10
FOR j% = 0 TO 2
a(i%,j%) = x(i%) ^ j%
NEXT
NEXT
PROCqrdecompose(a(), q(), r())
PROC_transpose(q(),t())
b() = t() . y()
FOR k% = 2 TO 0 STEP -1
s = 0
IF k% < 2 THEN
FOR j% = k%+1 TO 2
s += r(k%,j%) * z(j%)
NEXT
ENDIF
z(k%) = (b(k%) - s) / r(k%,k%)
NEXT k%
PRINT '"Least-squares solution:"
PRINT z(0), z(1), z(2)
END
DEF PROCqrdecompose(A(), Q(), R())
LOCAL i%, k%, m%, n%, H()
m% = DIM(A(),1) : n% = DIM(A(),2)
DIM H(m%,m%)
FOR i% = 0 TO m% : Q(i%,i%) = 1 : NEXT
WHILE n%
PROCqrstep(n%, k%, A(), H())
A() = H() . A()
Q() = Q() . H()
k% += 1
m% -= 1
n% -= 1
ENDWHILE
R() = A()
ENDPROC
DEF PROCqrstep(n%, k%, A(), H())
LOCAL a(), h(), i%, j%
DIM a(n%,0), h(n%,n%)
FOR i% = 0 TO n% : a(i%,0) = A(i%+k%,k%) : NEXT
PROChouseholder(h(), a())
H() = 0 : H(0,0) = 1
FOR i% = 0 TO n%
FOR j% = 0 TO n%
H(i%+k%,j%+k%) = h(i%,j%)
NEXT
NEXT
ENDPROC
REM Create the Householder matrix for the supplied column vector:
DEF PROChouseholder(H(), a())
LOCAL e(), u(), v(), vt(), vvt(), I(), d()
LOCAL i%, n% : n% = DIM(a(),1)
REM Create the scaled standard basis vector e():
DIM e(n%,0) : e(0,0) = SGN(a(0,0)) * MOD(a())
REM Create the normal vector u():
DIM u(n%,0) : u() = a() + e()
REM Normalise with respect to the first element:
DIM v(n%,0) : v() = u() / u(0,0)
REM Get the transpose of v() and its dot product with v():
DIM vt(0,n%), d(0) : PROC_transpose(v(), vt()) : d() = vt() . v()
REM Get the product of v() and vt():
DIM vvt(n%,n%) : vvt() = v() . vt()
REM Create an identity matrix I():
DIM I(n%,n%) : FOR i% = 0 TO n% : I(i%,i%) = 1 : NEXT
REM Create the Householder matrix H() = I - 2/vt()v() v()vt():
vvt() *= 2 / d(0) : H() = I() - vvt()
ENDPROC
'''Output:'''
Q:
-0.8571 0.3943 0.3314
-0.4286 -0.9029 -0.0343
0.2857 -0.1714 0.9429
R:
-14.0000 -21.0000 14.0000
0.0000 -175.0000 70.0000
0.0000 0.0000 -35.0000
Least-squares solution:
1.0000 2.0000 3.0000
C
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
typedef struct {
int m, n;
double ** v;
} mat_t, *mat;
mat matrix_new(int m, int n)
{
mat x = malloc(sizeof(mat_t));
x->v = malloc(sizeof(double*) * m);
x->v[0] = calloc(sizeof(double), m * n);
for (int i = 0; i < m; i++)
x->v[i] = x->v[0] + n * i;
x->m = m;
x->n = n;
return x;
}
void matrix_delete(mat m)
{
free(m->v[0]);
free(m->v);
free(m);
}
void matrix_transpose(mat m)
{
for (int i = 0; i < m->m; i++) {
for (int j = 0; j < i; j++) {
double t = m->v[i][j];
m->v[i][j] = m->v[j][i];
m->v[j][i] = t;
}
}
}
mat matrix_copy(int n, double a[][n], int m)
{
mat x = matrix_new(m, n);
for (int i = 0; i < m; i++)
for (int j = 0; j < n; j++)
x->v[i][j] = a[i][j];
return x;
}
mat matrix_mul(mat x, mat y)
{
if (x->n != y->m) return 0;
mat r = matrix_new(x->m, y->n);
for (int i = 0; i < x->m; i++)
for (int j = 0; j < y->n; j++)
for (int k = 0; k < x->n; k++)
r->v[i][j] += x->v[i][k] * y->v[k][j];
return r;
}
mat matrix_minor(mat x, int d)
{
mat m = matrix_new(x->m, x->n);
for (int i = 0; i < d; i++)
m->v[i][i] = 1;
for (int i = d; i < x->m; i++)
for (int j = d; j < x->n; j++)
m->v[i][j] = x->v[i][j];
return m;
}
/* c = a + b * s */
double *vmadd(double a[], double b[], double s, double c[], int n)
{
for (int i = 0; i < n; i++)
c[i] = a[i] + s * b[i];
return c;
}
/* m = I - v v^T */
mat vmul(double v[], int n)
{
mat x = matrix_new(n, n);
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++)
x->v[i][j] = -2 * v[i] * v[j];
for (int i = 0; i < n; i++)
x->v[i][i] += 1;
return x;
}
/* ||x|| */
double vnorm(double x[], int n)
{
double sum = 0;
for (int i = 0; i < n; i++) sum += x[i] * x[i];
return sqrt(sum);
}
/* y = x / d */
double* vdiv(double x[], double d, double y[], int n)
{
for (int i = 0; i < n; i++) y[i] = x[i] / d;
return y;
}
/* take c-th column of m, put in v */
double* mcol(mat m, double *v, int c)
{
for (int i = 0; i < m->m; i++)
v[i] = m->v[i][c];
return v;
}
void matrix_show(mat m)
{
for(int i = 0; i < m->m; i++) {
for (int j = 0; j < m->n; j++) {
printf(" %8.3f", m->v[i][j]);
}
printf("\n");
}
printf("\n");
}
void householder(mat m, mat *R, mat *Q)
{
mat q[m->m];
mat z = m, z1;
for (int k = 0; k < m->n && k < m->m - 1; k++) {
double e[m->m], x[m->m], a;
z1 = matrix_minor(z, k);
if (z != m) matrix_delete(z);
z = z1;
mcol(z, x, k);
a = vnorm(x, m->m);
if (m->v[k][k] > 0) a = -a;
for (int i = 0; i < m->m; i++)
e[i] = (i == k) ? 1 : 0;
vmadd(x, e, a, e, m->m);
vdiv(e, vnorm(e, m->m), e, m->m);
q[k] = vmul(e, m->m);
z1 = matrix_mul(q[k], z);
if (z != m) matrix_delete(z);
z = z1;
}
matrix_delete(z);
*Q = q[0];
*R = matrix_mul(q[0], m);
for (int i = 1; i < m->n && i < m->m - 1; i++) {
z1 = matrix_mul(q[i], *Q);
if (i > 1) matrix_delete(*Q);
*Q = z1;
matrix_delete(q[i]);
}
matrix_delete(q[0]);
z = matrix_mul(*Q, m);
matrix_delete(*R);
*R = z;
matrix_transpose(*Q);
}
double in[][3] = {
{ 12, -51, 4},
{ 6, 167, -68},
{ -4, 24, -41},
{ -1, 1, 0},
{ 2, 0, 3},
};
int main()
{
mat R, Q;
mat x = matrix_copy(3, in, 5);
householder(x, &R, &Q);
puts("Q"); matrix_show(Q);
puts("R"); matrix_show(R);
// to show their product is the input matrix
mat m = matrix_mul(Q, R);
puts("Q * R"); matrix_show(m);
matrix_delete(x);
matrix_delete(R);
matrix_delete(Q);
matrix_delete(m);
return 0;
}
{{out}}
Q
0.846 -0.391 0.343 0.082 0.078
0.423 0.904 -0.029 0.026 0.045
-0.282 0.170 0.933 -0.047 -0.137
-0.071 0.014 -0.001 0.980 -0.184
0.141 -0.017 -0.106 -0.171 -0.969
R
14.177 20.667 -13.402
-0.000 175.043 -70.080
0.000 0.000 -35.202
-0.000 -0.000 -0.000
0.000 0.000 -0.000
Q * R
12.000 -51.000 4.000
6.000 167.000 -68.000
-4.000 24.000 -41.000
-1.000 1.000 -0.000
2.000 -0.000 3.000
C++
/*
* g++ -O3 -Wall --std=c++11 qr_standalone.cpp -o qr_standalone
*/
#include <cstdio>
#include <cstdlib>
#include <cstring> // for memset
#include <limits>
#include <iostream>
#include <vector>
#include <math.h>
class Vector;
class Matrix {
public:
// default constructor (don't allocate)
Matrix() : m(0), n(0), data(nullptr) {}
// constructor with memory allocation, initialized to zero
Matrix(int m_, int n_) : Matrix() {
m = m_;
n = n_;
allocate(m_,n_);
}
// copy constructor
Matrix(const Matrix& mat) : Matrix(mat.m,mat.n) {
for (int i = 0; i < m; i++)
for (int j = 0; j < n; j++)
(*this)(i,j) = mat(i,j);
}
// constructor from array
template<int rows, int cols>
Matrix(double (&a)[rows][cols]) : Matrix(rows,cols) {
for (int i = 0; i < m; i++)
for (int j = 0; j < n; j++)
(*this)(i,j) = a[i][j];
}
// destructor
~Matrix() {
deallocate();
}
// access data operators
double& operator() (int i, int j) {
return data[i+m*j]; }
double operator() (int i, int j) const {
return data[i+m*j]; }
// operator assignment
Matrix& operator=(const Matrix& source) {
// self-assignment check
if (this != &source) {
if ( (m*n) != (source.m * source.n) ) { // storage cannot be reused
allocate(source.m,source.n); // re-allocate storage
}
// storage can be used, copy data
std::copy(source.data, source.data + source.m*source.n, data);
}
return *this;
}
// compute minor
void compute_minor(const Matrix& mat, int d) {
allocate(mat.m, mat.n);
for (int i = 0; i < d; i++)
(*this)(i,i) = 1.0;
for (int i = d; i < mat.m; i++)
for (int j = d; j < mat.n; j++)
(*this)(i,j) = mat(i,j);
}
// Matrix multiplication
// c = a * b
// c will be re-allocated here
void mult(const Matrix& a, const Matrix& b) {
if (a.n != b.m) {
std::cerr << "Matrix multiplication not possible, sizes don't match !\n";
return;
}
// reallocate ourself if necessary i.e. current Matrix has not valid sizes
if (a.m != m or b.n != n)
allocate(a.m, b.n);
memset(data,0,m*n*sizeof(double));
for (int i = 0; i < a.m; i++)
for (int j = 0; j < b.n; j++)
for (int k = 0; k < a.n; k++)
(*this)(i,j) += a(i,k) * b(k,j);
}
void transpose() {
for (int i = 0; i < m; i++) {
for (int j = 0; j < i; j++) {
double t = (*this)(i,j);
(*this)(i,j) = (*this)(j,i);
(*this)(j,i) = t;
}
}
}
// take c-th column of m, put in v
void extract_column(Vector& v, int c);
// memory allocation
void allocate(int m_, int n_) {
// if already allocated, memory is freed
deallocate();
// new sizes
m = m_;
n = n_;
data = new double[m_*n_];
memset(data,0,m_*n_*sizeof(double));
} // allocate
// memory free
void deallocate() {
if (data)
delete[] data;
data = nullptr;
}
int m, n;
private:
double* data;
}; // struct Matrix
// column vector
class Vector {
public:
// default constructor (don't allocate)
Vector() : size(0), data(nullptr) {}
// constructor with memory allocation, initialized to zero
Vector(int size_) : Vector() {
size = size_;
allocate(size_);
}
// destructor
~Vector() {
deallocate();
}
// access data operators
double& operator() (int i) {
return data[i]; }
double operator() (int i) const {
return data[i]; }
// operator assignment
Vector& operator=(const Vector& source) {
// self-assignment check
if (this != &source) {
if ( size != (source.size) ) { // storage cannot be reused
allocate(source.size); // re-allocate storage
}
// storage can be used, copy data
std::copy(source.data, source.data + source.size, data);
}
return *this;
}
// memory allocation
void allocate(int size_) {
deallocate();
// new sizes
size = size_;
data = new double[size_];
memset(data,0,size_*sizeof(double));
} // allocate
// memory free
void deallocate() {
if (data)
delete[] data;
data = nullptr;
}
// ||x||
double norm() {
double sum = 0;
for (int i = 0; i < size; i++) sum += (*this)(i) * (*this)(i);
return sqrt(sum);
}
// divide data by factor
void rescale(double factor) {
for (int i = 0; i < size; i++) (*this)(i) /= factor;
}
void rescale_unit() {
double factor = norm();
rescale(factor);
}
int size;
private:
double* data;
}; // class Vector
// c = a + b * s
void vmadd(const Vector& a, const Vector& b, double s, Vector& c)
{
if (c.size != a.size or c.size != b.size) {
std::cerr << "[vmadd]: vector sizes don't match\n";
return;
}
for (int i = 0; i < c.size; i++)
c(i) = a(i) + s * b(i);
}
// mat = I - 2*v*v^T
// !!! m is allocated here !!!
void compute_householder_factor(Matrix& mat, const Vector& v)
{
int n = v.size;
mat.allocate(n,n);
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++)
mat(i,j) = -2 * v(i) * v(j);
for (int i = 0; i < n; i++)
mat(i,i) += 1;
}
// take c-th column of a matrix, put results in Vector v
void Matrix::extract_column(Vector& v, int c) {
if (m != v.size) {
std::cerr << "[Matrix::extract_column]: Matrix and Vector sizes don't match\n";
return;
}
for (int i = 0; i < m; i++)
v(i) = (*this)(i,c);
}
void matrix_show(const Matrix& m, const std::string& str="")
{
std::cout << str << "\n";
for(int i = 0; i < m.m; i++) {
for (int j = 0; j < m.n; j++) {
printf(" %8.3f", m(i,j));
}
printf("\n");
}
printf("\n");
}
// L2-norm ||A-B||^2
double matrix_compare(const Matrix& A, const Matrix& B) {
// matrices must have same size
if (A.m != B.m or A.n != B.n)
return std::numeric_limits<double>::max();
double res=0;
for(int i = 0; i < A.m; i++) {
for (int j = 0; j < A.n; j++) {
res += (A(i,j)-B(i,j)) * (A(i,j)-B(i,j));
}
}
res /= A.m*A.n;
return res;
}
void householder(Matrix& mat,
Matrix& R,
Matrix& Q)
{
int m = mat.m;
int n = mat.n;
// array of factor Q1, Q2, ... Qm
std::vector<Matrix> qv(m);
// temp array
Matrix z(mat);
Matrix z1;
for (int k = 0; k < n && k < m - 1; k++) {
Vector e(m), x(m);
double a;
// compute minor
z1.compute_minor(z, k);
// extract k-th column into x
z1.extract_column(x, k);
a = x.norm();
if (mat(k,k) > 0) a = -a;
for (int i = 0; i < e.size; i++)
e(i) = (i == k) ? 1 : 0;
// e = x + a*e
vmadd(x, e, a, e);
// e = e / ||e||
e.rescale_unit();
// qv[k] = I - 2 *e*e^T
compute_householder_factor(qv[k], e);
// z = qv[k] * z1
z.mult(qv[k], z1);
}
Q = qv[0];
// after this loop, we will obtain Q (up to a transpose operation)
for (int i = 1; i < n && i < m - 1; i++) {
z1.mult(qv[i], Q);
Q = z1;
}
R.mult(Q, mat);
Q.transpose();
}
double in[][3] = {
{ 12, -51, 4},
{ 6, 167, -68},
{ -4, 24, -41},
{ -1, 1, 0},
{ 2, 0, 3},
};
int main()
{
Matrix A(in);
Matrix Q, R;
matrix_show(A,"A");
// compute QR decompostion
householder(A, R, Q);
matrix_show(Q,"Q");
matrix_show(R,"R");
// compare Q*R to the original matrix A
Matrix A_check;
A_check.mult(Q, R);
// compute L2 norm ||A-A_check||^2
double l2 = matrix_compare(A,A_check);
// display Q*R
matrix_show(A_check, l2 < 1e-12 ? "A == Q * R ? yes" : "A == Q * R ? no");
return EXIT_SUCCESS;
}
{{out}}
A
12.000 -51.000 4.000
6.000 167.000 -68.000
-4.000 24.000 -41.000
-1.000 1.000 0.000
2.000 0.000 3.000
Q
0.846 -0.391 0.343 0.082 0.078
0.423 0.904 -0.029 0.026 0.045
-0.282 0.170 0.933 -0.047 -0.137
-0.071 0.014 -0.001 0.980 -0.184
0.141 -0.017 -0.106 -0.171 -0.969
R
14.177 20.667 -13.402
-0.000 175.043 -70.080
0.000 0.000 -35.202
-0.000 -0.000 -0.000
0.000 0.000 -0.000
A == Q * R ? yes
12.000 -51.000 4.000
6.000 167.000 -68.000
-4.000 24.000 -41.000
-1.000 1.000 -0.000
2.000 -0.000 3.000
C#
{{libheader|Math.Net}}
using System;
using MathNet.Numerics.LinearAlgebra;
using MathNet.Numerics.LinearAlgebra.Double;
class Program
{
static void Main(string[] args)
{
Matrix<double> A = DenseMatrix.OfArray(new double[,]
{
{ 12, -51, 4 },
{ 6, 167, -68 },
{ -4, 24, -41 }
});
Console.WriteLine("A:");
Console.WriteLine(A);
var qr = A.QR();
Console.WriteLine();
Console.WriteLine("Q:");
Console.WriteLine(qr.Q);
Console.WriteLine();
Console.WriteLine("R:");
Console.WriteLine(qr.R);
}
}
{{out}}
A:
DenseMatrix 3x3-Double
12 -51 4
6 167 -68
-4 24 -41
Q:
DenseMatrix 3x3-Double
-0.857143 0.394286 -0.331429
-0.428571 -0.902857 0.0342857
0.285714 -0.171429 -0.942857
R:
DenseMatrix 3x3-Double
-14 -21 14
0 -175 70
0 0 35
Common Lisp
Uses the routines m+, m-, .*, ./ from [[Element-wise_operations]], mmul from [[Matrix multiplication]], mtp from [[Matrix transposition]].
Helper functions:
(defun sign (x)
(if (zerop x)
x
(/ x (abs x))))
(defun norm (x)
(let ((len (car (array-dimensions x))))
(sqrt (loop for i from 0 to (1- len) sum (expt (aref x i 0) 2)))))
(defun make-unit-vector (dim)
(let ((vec (make-array `(,dim ,1) :initial-element 0.0d0)))
(setf (aref vec 0 0) 1.0d0)
vec))
;; Return a nxn identity matrix.
(defun eye (n)
(let ((I (make-array `(,n ,n) :initial-element 0)))
(loop for j from 0 to (- n 1) do
(setf (aref I j j) 1))
I))
(defun array-range (A ma mb na nb)
(let* ((mm (1+ (- mb ma)))
(nn (1+ (- nb na)))
(B (make-array `(,mm ,nn) :initial-element 0.0d0)))
(loop for i from 0 to (1- mm) do
(loop for j from 0 to (1- nn) do
(setf (aref B i j)
(aref A (+ ma i) (+ na j)))))
B))
(defun rows (A) (car (array-dimensions A)))
(defun cols (A) (cadr (array-dimensions A)))
(defun mcol (A n) (array-range A 0 (1- (rows A)) n n))
(defun mrow (A n) (array-range A n n 0 (1- (cols A))))
(defun array-embed (A B row col)
(let* ((ma (rows A))
(na (cols A))
(mb (rows B))
(nb (cols B))
(C (make-array `(,ma ,na) :initial-element 0.0d0)))
(loop for i from 0 to (1- ma) do
(loop for j from 0 to (1- na) do
(setf (aref C i j) (aref A i j))))
(loop for i from 0 to (1- mb) do
(loop for j from 0 to (1- nb) do
(setf (aref C (+ row i) (+ col j))
(aref B i j))))
C))
Main routines:
(defun make-householder (a)
(let* ((m (car (array-dimensions a)))
(s (sign (aref a 0 0)))
(e (make-unit-vector m))
(u (m+ a (.* (* (norm a) s) e)))
(v (./ u (aref u 0 0)))
(beta (/ 2 (aref (mmul (mtp v) v) 0 0))))
(m- (eye m)
(.* beta (mmul v (mtp v))))))
(defun qr (A)
(let* ((m (car (array-dimensions A)))
(n (cadr (array-dimensions A)))
(Q (eye m)))
;; Work on n columns of A.
(loop for i from 0 to (if (= m n) (- n 2) (- n 1)) do
;; Select the i-th submatrix. For i=0 this means the original matrix A.
(let* ((B (array-range A i (1- m) i (1- n)))
;; Take the first column of the current submatrix B.
(x (mcol B 0))
;; Create the Householder matrix for the column and embed it into an mxm identity.
(H (array-embed (eye m) (make-householder x) i i)))
;; The product of all H matrices from the right hand side is the orthogonal matrix Q.
(setf Q (mmul Q H))
;; The product of all H matrices with A from the LHS is the upper triangular matrix R.
(setf A (mmul H A))))
;; Return Q and R.
(values Q A)))
Example 1:
(qr #2A((12 -51 4) (6 167 -68) (-4 24 -41)))
#2A((-0.85 0.39 0.33)
(-0.42 -0.90 -0.03)
( 0.28 -0.17 0.94))
#2A((-14.0 -21.0 14.0)
( 0.0 -175.0 70.0)
( 0.0 0.0 -35.0))
Example 2, [[Polynomial regression]]:
(defun polyfit (x y n)
(let* ((m (cadr (array-dimensions x)))
(A (make-array `(,m ,(+ n 1)) :initial-element 0)))
(loop for i from 0 to (- m 1) do
(loop for j from 0 to n do
(setf (aref A i j)
(expt (aref x 0 i) j))))
(lsqr A (mtp y))))
;; Solve a linear least squares problem by QR decomposition.
(defun lsqr (A b)
(multiple-value-bind (Q R) (qr A)
(let* ((n (cadr (array-dimensions R))))
(solve-upper-triangular (array-range R 0 (- n 1) 0 (- n 1))
(array-range (mmul (mtp Q) b) 0 (- n 1) 0 0)))))
;; Solve an upper triangular system by back substitution.
(defun solve-upper-triangular (R b)
(let* ((n (cadr (array-dimensions R)))
(x (make-array `(,n 1) :initial-element 0.0d0)))
(loop for k from (- n 1) downto 0
do (setf (aref x k 0)
(/ (- (aref b k 0)
(loop for j from (+ k 1) to (- n 1)
sum (* (aref R k j)
(aref x j 0))))
(aref R k k))))
x))
;; Finally use the data:
(let ((x #2A((0 1 2 3 4 5 6 7 8 9 10)))
(y #2A((1 6 17 34 57 86 121 162 209 262 321))))
(polyfit x y 2))
#2A((0.999999966345088) (2.000000015144699) (2.99999999879804))
D
{{trans|Common Lisp}} Uses the functions copied from [[Element-wise_operations]], [[Matrix multiplication]], and [[Matrix transposition]].
import std.stdio, std.math, std.algorithm, std.traits,
std.typecons, std.numeric, std.range, std.conv;
template elementwiseMat(string op) {
T[][] elementwiseMat(T)(in T[][] A, in T B) pure nothrow {
if (A.empty)
return null;
auto R = new typeof(return)(A.length, A[0].length);
foreach (immutable r, const row; A)
R[r][] = mixin("row[] " ~ op ~ "B");
return R;
}
T[][] elementwiseMat(T, U)(in T[][] A, in U[][] B)
pure nothrow if (is(Unqual!T == Unqual!U)) {
assert(A.length == B.length);
if (A.empty)
return null;
auto R = new typeof(return)(A.length, A[0].length);
foreach (immutable r, const row; A) {
assert(row.length == B[r].length);
R[r][] = mixin("row[] " ~ op ~ "B[r][]");
}
return R;
}
}
alias mSum = elementwiseMat!q{ + },
mSub = elementwiseMat!q{ - },
pMul = elementwiseMat!q{ * },
pDiv = elementwiseMat!q{ / };
bool isRectangular(T)(in T[][] mat) pure nothrow {
return mat.all!(r => r.length == mat[0].length);
}
T[][] matMul(T)(in T[][] a, in T[][] b) pure nothrow
in {
assert(a.isRectangular && b.isRectangular &&
a[0].length == b.length);
} body {
auto result = new T[][](a.length, b[0].length);
auto aux = new T[b.length];
foreach (immutable j; 0 .. b[0].length) {
foreach (immutable k; 0 .. b.length)
aux[k] = b[k][j];
foreach (immutable i; 0 .. a.length)
result[i][j] = a[i].dotProduct(aux);
}
return result;
}
Unqual!T[][] transpose(T)(in T[][] m) pure nothrow {
auto r = new Unqual!T[][](m[0].length, m.length);
foreach (immutable nr, row; m)
foreach (immutable nc, immutable c; row)
r[nc][nr] = c;
return r;
}
T norm(T)(in T[][] m) pure nothrow {
return transversal(m, 0).map!q{ a ^^ 2 }.sum.sqrt;
}
Unqual!T[][] makeUnitVector(T)(in size_t dim) pure nothrow {
auto result = new Unqual!T[][](dim, 1);
foreach (row; result)
row[] = 0;
result[0][0] = 1;
return result;
}
/// Return a nxn identity matrix.
Unqual!T[][] matId(T)(in size_t n) pure nothrow {
auto Id = new Unqual!T[][](n, n);
foreach (immutable r, row; Id) {
row[] = 0;
row[r] = 1;
}
return Id;
}
T[][] slice2D(T)(in T[][] A,
in size_t ma, in size_t mb,
in size_t na, in size_t nb) pure nothrow {
auto B = new T[][](mb - ma + 1, nb - na + 1);
foreach (immutable i, brow; B)
brow[] = A[ma + i][na .. na + brow.length];
return B;
}
size_t rows(T)(in T[][] A) pure nothrow { return A.length; }
size_t cols(T)(in T[][] A) pure nothrow {
return A.length ? A[0].length : 0;
}
T[][] mcol(T)(in T[][] A, in size_t n) pure nothrow {
return slice2D(A, 0, A.rows - 1, n, n);
}
T[][] matEmbed(T)(in T[][] A, in T[][] B,
in size_t row, in size_t col) pure nothrow {
auto C = new T[][](rows(A), cols(A));
foreach (immutable i, const arow; A)
C[i][] = arow[]; // Some wasted copies.
foreach (immutable i, const brow; B)
C[row + i][col .. col + brow.length] = brow[];
return C;
}
// Main routines ---------------
T[][] makeHouseholder(T)(in T[][] a) {
immutable m = a.rows;
immutable T s = a[0][0].sgn;
immutable e = makeUnitVector!T(m);
immutable u = mSum(a, pMul(e, a.norm * s));
immutable v = pDiv(u, u[0][0]);
immutable beta = 2.0 / v.transpose.matMul(v)[0][0];
return mSub(matId!T(m), pMul(v.matMul(v.transpose), beta));
}
Tuple!(T[][],"Q", T[][],"R") QRdecomposition(T)(T[][] A) {
immutable m = A.rows;
immutable n = A.cols;
auto Q = matId!T(m);
// Work on n columns of A.
foreach (immutable i; 0 .. (m == n ? n - 1 : n)) {
// Select the i-th submatrix. For i=0 this means the original
// matrix A.
immutable B = slice2D(A, i, m - 1, i, n - 1);
// Take the first column of the current submatrix B.
immutable x = mcol(B, 0);
// Create the Householder matrix for the column and embed it
// into an mxm identity.
immutable H = matEmbed(matId!T(m), x.makeHouseholder, i, i);
// The product of all H matrices from the right hand side is
// the orthogonal matrix Q.
Q = Q.matMul(H);
// The product of all H matrices with A from the LHS is the
// upper triangular matrix R.
A = H.matMul(A);
}
// Return Q and R.
return typeof(return)(Q, A);
}
// Polynomial regression ---------------
/// Solve an upper triangular system by back substitution.
T[][] solveUpperTriangular(T)(in T[][] R, in T[][] b) pure nothrow {
immutable n = R.cols;
auto x = new T[][](n, 1);
foreach_reverse (immutable k; 0 .. n) {
T tot = 0;
foreach (immutable j; k + 1 .. n)
tot += R[k][j] * x[j][0];
x[k][0] = (b[k][0] - tot) / R[k][k];
}
return x;
}
/// Solve a linear least squares problem by QR decomposition.
T[][] lsqr(T)(T[][] A, in T[][] b) pure nothrow {
const qr = A.QRdecomposition;
immutable n = qr.R.cols;
return solveUpperTriangular(
slice2D(qr.R, 0, n - 1, 0, n - 1),
slice2D(qr.Q.transpose.matMul(b), 0, n - 1, 0, 0));
}
T[][] polyFit(T)(in T[][] x, in T[][] y, in size_t n) pure nothrow {
immutable size_t m = x.cols;
auto A = new T[][](m, n + 1);
foreach (immutable i, row; A)
foreach (immutable j, ref item; row)
item = x[0][i] ^^ j;
return lsqr(A, y.transpose);
}
void main() {
// immutable (Q, R) = QRdecomposition([[12.0, -51, 4],
immutable qr = QRdecomposition([[12.0, -51, 4],
[ 6.0, 167, -68],
[-4.0, 24, -41]]);
immutable form = "[%([%(%2.3f, %)]%|,\n %)]\n";
writefln(form, qr.Q);
writefln(form, qr.R);
immutable x = [[0.0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]];
immutable y = [[1.0, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321]];
polyFit(x, y, 2).writeln;
}
{{out}}
[[-0.857, 0.394, 0.331],
[-0.429, -0.903, -0.034],
[0.286, -0.171, 0.943]]
[[-14.000, -21.000, 14.000],
[0.000, -175.000, 70.000],
[0.000, -0.000, -35.000]]
[[1], [2], [3]]
Futhark
import "lib/github.com/diku-dk/linalg/linalg"
module linalg_f64 = mk_linalg f64
let eye (n: i32): [n][n]f64 =
let arr = map (\ind -> let (i,j) = (ind/n,ind%n) in if (i==j) then 1.0 else 0.0) (iota (n*n))
in unflatten n n arr
let norm v = linalg_f64.dotprod v v |> f64.sqrt
let qr [n] [m] (a: [m][n]f64): ([m][m]f64, [m][n]f64) =
let make_householder [d] (x: [d]f64): [d][d]f64 =
let div = if x[0] > 0 then x[0] + norm x else x[0] - norm x
let v = map (/div) x
let v[0] = 1
let fac = 2.0 / linalg_f64.dotprod v v
in map2 (map2 (-)) (eye d) (map (map (*fac)) (linalg_f64.outer v v))
let step ((x,y):([m][m]f64,[m][n]f64)) (i:i32): ([m][m]f64,[m][n]f64) =
let h = eye m
let h[i:m,i:m] = make_householder y[i:m,i]
let q': [m][m]f64 = linalg_f64.matmul x h
let a': [m][n]f64 = linalg_f64.matmul h y
in (q',a')
let q = eye m
in foldl step (q,a) (iota n)
entry main = qr [[12.0, -51.0, 4.0],[6.0, 167.0, -68.0],[-4.0, 24.0, -41.0]]
{{out}}
$ ./qr
[[-0.857143f64, 0.394286f64, -0.331429f64], [-0.428571f64, -0.902857f64, 0.034286f64], [0.285714f64, -0.171429f64, -0.942857f64]]
[[-14.000000f64, -21.000000f64, 14.000000f64], [0.000000f64, -175.000000f64, 70.000000f64], [-0.000000f64, 0.000000f64, 35.000000f64]]
Go
===Method of task description, library go.matrix=== {{trans|Common Lisp}} A fairly close port of the Common Lisp solution, this solution uses the [http://github.com/skelterjohn/go.matrix go.matrix library] for supporting functions. Note though, that go.matrix has QR decomposition, as shown in the [[Polynomial_regression#Go|Go solution]] to Polynomial regression. The solution there is coded more directly than by following the CL example here. Similarly, examination of the go.matrix QR source shows that it computes the decomposition more directly.
package main
import (
"fmt"
"math"
"github.com/skelterjohn/go.matrix"
)
func sign(s float64) float64 {
if s > 0 {
return 1
} else if s < 0 {
return -1
}
return 0
}
func unitVector(n int) *matrix.DenseMatrix {
vec := matrix.Zeros(n, 1)
vec.Set(0, 0, 1)
return vec
}
func householder(a *matrix.DenseMatrix) *matrix.DenseMatrix {
m := a.Rows()
s := sign(a.Get(0, 0))
e := unitVector(m)
u := matrix.Sum(a, matrix.Scaled(e, a.TwoNorm()*s))
v := matrix.Scaled(u, 1/u.Get(0, 0))
// (error checking skipped in this solution)
prod, _ := v.Transpose().TimesDense(v)
β := 2 / prod.Get(0, 0)
prod, _ = v.TimesDense(v.Transpose())
return matrix.Difference(matrix.Eye(m), matrix.Scaled(prod, β))
}
func qr(a *matrix.DenseMatrix) (q, r *matrix.DenseMatrix) {
m := a.Rows()
n := a.Cols()
q = matrix.Eye(m)
last := n - 1
if m == n {
last--
}
for i := 0; i <= last; i++ {
// (copy is only for compatibility with an older version of gomatrix)
b := a.GetMatrix(i, i, m-i, n-i).Copy()
x := b.GetColVector(0)
h := matrix.Eye(m)
h.SetMatrix(i, i, householder(x))
q, _ = q.TimesDense(h)
a, _ = h.TimesDense(a)
}
return q, a
}
func main() {
// task 1: show qr decomp of wp example
a := matrix.MakeDenseMatrixStacked([][]float64{
{12, -51, 4},
{6, 167, -68},
{-4, 24, -41}})
q, r := qr(a)
fmt.Println("q:\n", q)
fmt.Println("r:\n", r)
// task 2: use qr decomp for polynomial regression example
x := matrix.MakeDenseMatrixStacked([][]float64{
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}})
y := matrix.MakeDenseMatrixStacked([][]float64{
{1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321}})
fmt.Println("\npolyfit:\n", polyfit(x, y, 2))
}
func polyfit(x, y *matrix.DenseMatrix, n int) *matrix.DenseMatrix {
m := x.Cols()
a := matrix.Zeros(m, n+1)
for i := 0; i < m; i++ {
for j := 0; j <= n; j++ {
a.Set(i, j, math.Pow(x.Get(0, i), float64(j)))
}
}
return lsqr(a, y.Transpose())
}
func lsqr(a, b *matrix.DenseMatrix) *matrix.DenseMatrix {
q, r := qr(a)
n := r.Cols()
prod, _ := q.Transpose().TimesDense(b)
return solveUT(r.GetMatrix(0, 0, n, n), prod.GetMatrix(0, 0, n, 1))
}
func solveUT(r, b *matrix.DenseMatrix) *matrix.DenseMatrix {
n := r.Cols()
x := matrix.Zeros(n, 1)
for k := n - 1; k >= 0; k-- {
sum := 0.
for j := k + 1; j < n; j++ {
sum += r.Get(k, j) * x.Get(j, 0)
}
x.Set(k, 0, (b.Get(k, 0)-sum)/r.Get(k, k))
}
return x
}
Output:
q:
{-0.857143, 0.394286, 0.331429,
-0.428571, -0.902857, -0.034286,
0.285714, -0.171429, 0.942857}
r:
{ -14, -21, 14,
0, -175, 70,
0, 0, -35}
polyfit:
{1,
2,
3}
===Library QR, gonum/matrix===
package main
import (
"fmt"
"github.com/gonum/matrix/mat64"
)
func main() {
// task 1: show qr decomp of wp example
a := mat64.NewDense(3, 3, []float64{
12, -51, 4,
6, 167, -68,
-4, 24, -41,
})
var qr mat64.QR
qr.Factorize(a)
var q, r mat64.Dense
q.QFromQR(&qr)
r.RFromQR(&qr)
fmt.Printf("q: %.3f\n\n", mat64.Formatted(&q, mat64.Prefix(" ")))
fmt.Printf("r: %.3f\n\n", mat64.Formatted(&r, mat64.Prefix(" ")))
// task 2: use qr decomp for polynomial regression example
x := []float64{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
y := []float64{1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321}
a = Vandermonde(x, 2)
b := mat64.NewDense(11, 1, y)
qr.Factorize(a)
var f mat64.Dense
f.SolveQR(&qr, false, b)
fmt.Printf("polyfit: %.3f\n",
mat64.Formatted(&f, mat64.Prefix(" ")))
}
func Vandermonde(a []float64, degree int) *mat64.Dense {
x := mat64.NewDense(len(a), degree+1, nil)
for i := range a {
for j, p := 0, 1.; j <= degree; j, p = j+1, p*a[i] {
x.Set(i, j, p)
}
}
return x
}
{{out}}
q: ⎡-0.857 0.394 0.331⎤
⎢-0.429 -0.903 -0.034⎥
⎣ 0.286 -0.171 0.943⎦
r: ⎡ -14.000 -21.000 14.000⎤
⎢ 0.000 -175.000 70.000⎥
⎣ 0.000 0.000 -35.000⎦
polyfit: ⎡1.000⎤
⎢2.000⎥
⎣3.000⎦
Haskell
Square matrices only; decompose A and solve Rx = q by back substitution
import Data.List
import Text.Printf (printf)
eps = 1e-6 :: Double
-- a matrix is represented as a list of columns
mmult :: Num a => [[a]] -> [[a]] -> [[a]]
nth :: Num a => [[a]] -> Int -> Int -> a
mmult_num :: Num a => [[a]] -> a -> [[a]]
madd :: Num a => [[a]] -> [[a]] -> [[a]]
idMatrix :: Num a => Int -> Int -> [[a]]
adjustWithE :: [[Double]] -> Int -> [[Double]]
mmult a b = [ [ sum $ zipWith (*) ak bj | ak <- (transpose a) ] | bj <- b ]
nth mA i j = (mA !! j) !! i
mmult_num mA n = map (\c -> map (*n) c) mA
madd mA mB = zipWith (\c1 c2 -> zipWith (+) c1 c2) mA mB
idMatrix n m = [ [if (i==j) then 1 else 0 | i <- [1..n]] | j <- [1..m]]
adjustWithE mA n = let lA = length mA in
(idMatrix n (n - lA)) ++ (map (\c -> (take (n - lA) (repeat 0.0)) ++ c ) mA)
-- auxiliary functions
sqsum :: Floating a => [a] -> a
norm :: Floating a => [a] -> a
epsilonize :: [[Double]] -> [[Double]]
sqsum a = foldl (\x y -> x + y*y) 0 a
norm a = sqrt $! sqsum a
epsilonize mA = map (\c -> map (\x -> if abs x <= eps then 0 else x) c) mA
-- Householder transformation; householder A = (Q, R)
uTransform :: [Double] -> [Double]
hMatrix :: [Double] -> Int -> Int -> [[Double]]
householder :: [[Double]] -> ([[Double]], [[Double]])
-- householder_rec Q R A
householder_rec :: [[Double]] -> [[Double]] -> Int -> ([[Double]], [[Double]])
uTransform a = let t = (head a) + (signum (head a))*(norm a) in
1 : map (\x -> x/t) (tail a)
hMatrix a n i = let u = uTransform (drop i a) in
madd
(idMatrix (n-i) (n-i))
(mmult_num
(mmult [u] (transpose [u]))
((/) (-2) (sqsum u)))
householder_rec mQ mR 0 = (mQ, mR)
householder_rec mQ mR n = let mSize = length mR in
let mH = adjustWithE (hMatrix (mR!!(mSize - n)) mSize (mSize - n)) mSize in
householder_rec (mmult mQ mH) (mmult mH mR) (n - 1)
householder mA = let mSize = length mA in
let (mQ, mR) = householder_rec (idMatrix mSize mSize) mA mSize in
(epsilonize mQ, epsilonize mR)
backSubstitution :: [[Double]] -> [Double] -> [Double] -> [Double]
backSubstitution mR [] res = res
backSubstitution mR@(hR:tR) q@(h:t) res =
let x = (h / (head hR)) in
backSubstitution
(map tail tR)
(tail (zipWith (-) q (map (*x) hR)))
(x : res)
showMatrix :: [[Double]] -> String
showMatrix mA =
concat $ intersperse "\n"
(map (\x -> unwords $ printf "%10.4f" <$> (x::[Double])) (transpose mA))
mY = [[12, 6, -4], [-51, 167, 24], [4, -68, -41]] :: [[Double]]
q = [21, 245, 35] :: [Double]
main = let (mQ, mR) = householder mY in
putStrLn ("Q: \n" ++ showMatrix mQ) >>
putStrLn ("R: \n" ++ showMatrix mR) >>
putStrLn ("q: \n" ++ show q) >>
putStrLn ("x: \n" ++ show (backSubstitution (reverse (map reverse mR)) (reverse q) []))
{{out}}
Q:
-0.8571 0.3943 -0.3314
-0.4286 -0.9029 0.0343
0.2857 -0.1714 -0.9429
R:
-14.0000 -21.0000 14.0000
0.0000 -175.0000 70.0000
0.0000 0.0000 35.0000
q:
[21.0,245.0,35.0]
x:
[1.0000000000000004,-0.9999999999999999,1.0]
J
'''Solution''' (built-in):
QR =: 128!:0
'''Solution''' (custom implementation):
mp=: +/ . * NB. matrix product
h =: +@|: NB. conjugate transpose
QR=: 3 : 0
n=.{:$A=.y
if. 1>:n do.
A ((% {.@,) ; ]) %:(h A) mp A
else.
m =.>.n%2
A0=.m{."1 A
A1=.m}."1 A
'Q0 R0'=.QR A0
'Q1 R1'=.QR A1 - Q0 mp T=.(h Q0) mp A1
(Q0,.Q1);(R0,.T),(-n){."1 R1
end.
)
'''Example''':
QR 12 _51 4,6 167 _68,:_4 24 _41
+-----------------------------+----------+
| 0.857143 _0.394286 _0.331429|14 21 _14|
| 0.428571 0.902857 0.0342857| 0 175 _70|
|_0.285714 0.171429 _0.942857| 0 0 35|
+-----------------------------+----------+
'''Example''' (polynomial fitting using QR reduction):
X=:i.# Y=:1 6 17 34 57 86 121 162 209 262 321
'Q R'=: QR X ^/ i.3
R %.~(|:Q)+/ .* Y
1 2 3
'''Notes''':J offers a built-in QR decomposition function, 128!:0. If J did not offer this function as a built-in, it could be written in J along the lines of the second version, which is covered in [[j:Essays/QR Decomposition|an essay on the J wiki]].
Java
Note: uses the [https://math.nist.gov/javanumerics/jama/ JAMA Java Matrix Package].
Compile with: '''javac -cp Jama-1.0.3.jar Decompose.java'''.
import Jama.Matrix;
import Jama.QRDecomposition;
import java.io.StringWriter;
import java.io.PrintWriter;
public class Decompose {
public static void main(String[] args) {
Matrix matrix = new Matrix(new double[][] {
{ 12, -51, 4 },
{ 6, 167, -68 },
{ -4, 24, -41 },
});
QRDecomposition d = new QRDecomposition(matrix);
System.out.print(toString(d.getQ()));
System.out.print(toString(d.getR()));
}
public static String toString(Matrix m) {
StringWriter sw = new StringWriter();
m.print(new PrintWriter(sw, true), 8, 6);
return sw.toString();
}
}
{{out}}
-0.857143 0.394286 -0.331429
-0.428571 -0.902857 0.034286
0.285714 -0.171429 -0.942857
-14.000000 -21.000000 14.000000
0.000000 -175.000000 70.000000
0.000000 0.000000 35.000000
Julia
Built-in function
Q, R = qr([12 -51 4; 6 167 -68; -4 24 -41])
{{out}}
(
3x3 Array{Float64,2}:
-0.857143 0.394286 0.331429
-0.428571 -0.902857 -0.0342857
0.285714 -0.171429 0.942857 ,
3x3 Array{Float64,2}:
-14.0 -21.0 14.0
0.0 -175.0 70.0
0.0 0.0 -35.0)
Maple
with(LinearAlgebra):
Q,R := QRDecomposition( evalf( <<12|-51|4>,<6|167|-68>,<-4|24|-41>>) ):
Q;
R;
Output:
[-0.857142857142857 0.394285714285714 0.331428571428571]
[ ]
[-0.428571428571429 -0.902857142857143 -0.0342857142857143]
[ ]
[ 0.285714285714286 -0.171428571428571 0.942857142857143]
[-14. -21. 14.0000000000000]
[ ]
[ 0. -175.000000000000 70.0000000000000]
[ ]
[ 0. 0. -35.]
Mathematica
{q,r}=QRDecomposition[{{12, -51, 4}, {6, 167, -68}, {-4, 24, -41}}];
q//MatrixForm
-> 6/7 3/7 -(2/7)
-69/175 158/175 6/35
-58/175 6/175 -33/35
r//MatrixForm
-> 14 21 -14
0 175 -70
0 0 35
=={{header|MATLAB}} / {{header|Octave}}==
A = [12 -51 4
6 167 -68
-4 24 -41];
[Q,R]=qr(A)
Output:
Q =
0.857143 -0.394286 -0.331429
0.428571 0.902857 0.034286
-0.285714 0.171429 -0.942857
R =
14 21 -14
0 175 -70
0 0 35
Maxima
load(lapack)$ /* This may hang up in wxMaxima, if this happens, use xMaxima or plain MAxima in a terminal */
a: matrix([12, -51, 4],
[ 6, 167, -68],
[-4, 24, -41])$
[q, r]: dgeqrf(a)$
mat_norm(q . r - a, 1);
4.2632564145606011E-14
/* Note: the lapack package is a lisp translation of the fortran lapack library */
For an exact or arbitrary precision solution:
load("linearalgebra")$
load("eigen")$
unitVector(n) := ematrix(n,1,1,1,1);
signValue(r) := block([s:sign(r)],
if s='pos then 1 else if s='zero then 0 else -1);
householder(a) := block([m : length(a),u,v,beta],
u : a + sqrt(a . a)*signValue(a[1,1])*unitVector(m),
v : u / u[1,1],
beta : 2/(v . v),
diagmatrix(m,1) - beta*transpose(v . transpose(v)));
getSubmatrix(obj,i1,j1,i2,j2) :=
genmatrix(lambda([i,j], obj[i+i1-1,j+j1-1]),i2-i1+1,j2-j1+1);
setSubmatrix(obj,i1,j1,subobj) := block([m,n],
[m,n] : matrix_size(subobj),
for i: 0 thru m-1 do
(for j: 0 thru n-1 do
obj[i1+i,j1+j] : subobj[i+1,j+1]));
qr(obj) := block([m,n,qm,rm,i],
[m,n] : matrix_size(obj),
qm : diagmatrix(m,1),
rm : copymatrix(obj),
for i: 1 thru (if m=n then n-1 else n) do
block([x,h],
x : getSubmatrix(rm,i,i,m,i),
h : diagmatrix(m,1),
setSubmatrix(h,i,i,householder(x)),
qm : qm . h,
rm : h . rm),
[qm,rm]);
solveUpperTriangular(r,b) := block([n,x,index,k],
n : second(matrix_size(r)),
x : genmatrix(lambda([a, b], 0), n, 1),
for k: n thru 1 step -1 do
(index : min(n,k+1),
x[k,1] : (b[k,1] - (getSubmatrix(r,k,index,k,n) . getSubmatrix(x,index,1,n,1)))/r[k,k]),
x);
lsqr(a,b) := block([q,r,n],
[q,r] : qr(a),
n : second(matrix_size(r)),
solveUpperTriangular(getSubmatrix(r,1,1,n,n), transpose(q) . b));
polyfit(x,y,n) := block([a,j],
a : genmatrix(lambda([i,j], if j=1 then 1.0b0 else bfloat(x[i,1]^(j-1))),
length(x),n+1),
lsqr(a,y));
Then we have the examples:
(%i) [q,r] : qr(a);
[ 6 69 58 ]
[ - - --- --- ]
[ 7 175 175 ]
[ ] [ - 14 - 21 14 ]
[ 3 158 6 ] [ ]
(%o) [[ - - - --- - --- ], [ 0 - 175 70 ]]
[ 7 175 175 ] [ ]
[ ] [ 0 0 - 35 ]
[ 2 6 33 ]
[ - - -- -- ]
[ 7 35 35 ]
(%i) mat_norm(q . r - a, 1);
(%o) 0
(%i) x : transpose(matrix([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))$
(%i) y : transpose(matrix([1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321]))$
(%i) fpprec : 30$
(%i) polyfit(x, y, 2);
[ 9.99999999999999999999999999996b-1 ]
[ ]
(%o) [ 2.00000000000000000000000000002b0 ]
[ ]
[ 3.0b0 ]
PARI/GP
{{works with|PARI/GP|2.6.0 and above}}
matqr(M)
Perl
Letting the PDL module do all the work.
use strict;
use warnings;
use PDL;
use PDL::LinearAlgebra qw(mqr);
my $a = pdl(
[12, -51, 4],
[ 6, 167, -68],
[-4, 24, -41],
[-1, 1, 0],
[ 2, 0, 3]
);
my ($q, $r) = mqr($a);
print $q, $r, $q x $r;
{{out}}
[
[ -0.84641474 0.39129081 -0.34312406]
[ -0.42320737 -0.90408727 0.029270162]
[ 0.28213825 -0.17042055 -0.93285599]
[ 0.070534562 -0.014040652 0.001099372]
[ -0.14106912 0.016655511 0.10577161]
]
[
[-14.177447 -20.666627 13.401567]
[ 0 -175.04254 70.080307]
[ 0 0 35.201543]
]
[
[ 12 -51 4]
[ 6 167 -68]
[ -4 24 -41]
[ -1 1 0]
[ 2 0 3]
]
Perl 6
{{Works with|rakudo|2018.06}}
# sub householder translated from https://codereview.stackexchange.com/questions/120978/householder-transformation
use v6;
sub identity(Int:D $m --> Array of Array) {
my Array @M;
for 0 ..^ $m -> $i {
@M.push: [0 xx $m];
@M[$i; $i] = 1;
}
@M;
}
multi multiply(Array:D @A, @b where Array:D --> Array) {
my @c;
for ^@A X ^@b -> ($i, $j) {
@c[$i] += @A[$i; $j] * @b[$j];
}
@c;
}
multi multiply(Array:D @A, Array:D @B --> Array of Array) {
my Array @C;
for ^@A X ^@B[0] -> ($i, $j) {
@C[$i; $j] += @A[$i; $_] * @B[$_; $j] for ^@B;
}
@C;
}
sub transpose(Array:D @M --> Array of Array) {
my ($rows, $cols) = (@M.elems, @M[0].elems);
my Array @T;
for ^$cols X ^$rows -> ($j, $i) {
@T[$j; $i] = @M[$i; $j];
}
@T;
}
####################################################
# NOTE: @A gets overwritten and becomes @R, only need
# to return @Q.
####################################################
sub householder(Array:D @A --> Array) {
my Int ($m, $n) = (@A.elems, @A[0].elems);
my @v = 0 xx $m;
my Array @Q = identity($m);
for 0 ..^ $n -> $k {
my Real $sum = 0;
my Real $A0 = @A[$k; $k];
my Int $sign = $A0 < 0 ?? -1 !! 1;
for $k ..^ $m -> $i {
$sum += @A[$i; $k] * @A[$i; $k];
}
my Real $sqr_sum = $sign * sqrt($sum);
my Real $tmp = sqrt(2 * ($sum + $A0 * $sqr_sum));
@v[$k] = ($sqr_sum + $A0) / $tmp;
for ($k + 1) ..^ $m -> $i {
@v[$i] = @A[$i; $k] / $tmp;
}
for 0 ..^ $n -> $j {
$sum = 0;
for $k ..^ $m -> $i {
$sum += @v[$i] * @A[$i; $j];
}
for $k ..^ $m -> $i {
@A[$i; $j] -= 2 * @v[$i] * $sum;
}
}
for 0 ..^ $m -> $j {
$sum = 0;
for $k ..^ $m -> $i {
$sum += @v[$i] * @Q[$i; $j];
}
for $k ..^ $m -> $i {
@Q[$i; $j] -= 2 * @v[$i] * $sum;
}
}
}
@Q
}
sub dotp(@a where Array:D, @b where Array:D --> Real) {
[+] @a >>*<< @b;
}
sub upper-solve(Array:D @U, @b where Array:D, Int:D $n --> Array) {
my @y = 0 xx $n;
@y[$n - 1] = @b[$n - 1] / @U[$n - 1; $n - 1];
for reverse ^($n - 1) -> $i {
@y[$i] = (@b[$i] - (dotp(@U[$i], @y))) / @U[$i; $i];
}
@y;
}
sub polyfit(@x where Array:D, @y where Array:D, Int:D $n) {
my Int $m = @x.elems;
my Array @V;
# Vandermonde matrix
for ^$m X (0 .. $n) -> ($i, $j) {
@V[$i; $j] = @x[$i] ** $j
}
# least squares
my $Q = householder(@V);
my @b = multiply($Q, @y);
return upper-solve(@V, @b, $n + 1);
}
sub print-mat(Array:D @M, Str:D $name) {
my Int ($m, $n) = (@M.elems, @M[0].elems);
print "\n$name:\n";
for 0 ..^ $m -> $i {
for 0 ..^ $n -> $j {
print @M[$i; $j].fmt("%12.6f ");
}
print "\n";
}
}
sub MAIN() {
############
# 1st part #
############
my Array @A = (
[12, -51, 4],
[ 6, 167, -68],
[-4, 24, -41],
[-1, 1, 0],
[ 2, 0, 3]
);
print-mat(@A, 'A');
my $Q = householder(@A);
$Q = transpose($Q);
print-mat($Q, 'Q');
# after householder, @A is now @R
print-mat(@A, 'R');
print-mat(multiply($Q, @A), 'check Q x R = A');
############
# 2nd part #
############
my @x = [^11];
my @y = [1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321];
my @coef = polyfit(@x, @y, 2);
say
"\npolyfit:\n",
<constant X X^2>.fmt("%12s"),
"\n",
@coef.fmt("%12.6f");
}
output:
A:
12.000000 -51.000000 4.000000
6.000000 167.000000 -68.000000
-4.000000 24.000000 -41.000000
-1.000000 1.000000 0.000000
2.000000 0.000000 3.000000
Q:
-0.846415 0.391291 -0.343124 0.066137 -0.091462
-0.423207 -0.904087 0.029270 0.017379 -0.048610
0.282138 -0.170421 -0.932856 -0.021942 0.143712
0.070535 -0.014041 0.001099 0.997401 0.004295
-0.141069 0.016656 0.105772 0.005856 0.984175
R:
-14.177447 -20.666627 13.401567
-0.000000 -175.042539 70.080307
0.000000 0.000000 35.201543
-0.000000 0.000000 0.000000
0.000000 -0.000000 0.000000
check Q x R = A:
12.000000 -51.000000 4.000000
6.000000 167.000000 -68.000000
-4.000000 24.000000 -41.000000
-1.000000 1.000000 -0.000000
2.000000 -0.000000 3.000000
polyfit:
constant X X^2
1.000000 2.000000 3.000000
Phix
using matrix_mul from [[Matrix_multiplication#Phix]]
-- demo/rosettacode/QRdecomposition.exw
function vtranspose(sequence v)
-- transpose a vector of length m into an mx1 matrix,
-- eg {1,2,3} -> {{1},{2},{3}}
for i=1 to length(v) do v[i] = {v[i]} end for
return v
end function
function mat_col(sequence a, integer col)
sequence res = repeat(0,length(a))
for i=col to length(a) do
res[i] = a[i,col]
end for
return res
end function
function mat_norm(sequence a)
atom res = 0
for i=1 to length(a) do
res += a[i]*a[i]
end for
res = sqrt(res)
return res
end function
function mat_ident(integer n)
sequence res = repeat(repeat(0,n),n)
for i=1 to n do
res[i,i] = 1
end for
return res
end function
function QRHouseholder(sequence a)
integer columns = length(a[1]),
rows = length(a),
m = max(columns,rows),
n = min(rows,columns)
sequence q, I = mat_ident(m), Q = I, u, v
--
-- Programming note: The code of this main loop was not as easily
-- written as the first glance might suggest. Explicitly setting
-- to 0 any a[i,j] [etc] that should be 0 but have inadvertently
-- gotten set to +/-1e-15 or thereabouts may be advisable. The
-- commented-out code was retrieved from a backup and should be
-- treated as an example and not be trusted (iirc, it made no
-- difference to the test cases used, so I deleted it, and then
-- had second thoughts a few days later).
--
for j=1 to min(m-1,n) do
u = mat_col(a,j)
u[j] -= mat_norm(u)
v = sq_div(u,mat_norm(u))
q = sq_sub(I,sq_mul(2,matrix_mul(vtranspose(v),{v})))
a = matrix_mul(q,a)
-- for row=j+1 to length(a) do
-- a[row][j] = 0
-- end for
Q = matrix_mul(Q,q)
end for
-- Get the upper triangular matrix R.
sequence R = repeat(repeat(0,n),m)
for i=1 to n do -- (logically 1 to m(>=n), but no need)
for j=i to n do
R[i,j] = a[i,j]
end for
end for
return {Q,R}
end function
sequence a = {{12, -51, 4},
{ 6, 167, -68},
{-4, 24, -41}},
{q,r} = QRHouseholder(a)
?"A" pp(a,{pp_Nest,1})
?"Q" pp(q,{pp_Nest,1})
?"R" pp(r,{pp_Nest,1})
?"Q * R" pp(matrix_mul(q,r),{pp_Nest,1})
{{out}}
"A"
{{12,-51,4},
{6,167,-68},
{-4,24,-41}}
"Q"
{{0.8571428571,-0.3942857143,0.3314285714},
{0.4285714286,0.9028571429,-0.03428571429},
{-0.2857142857,0.1714285714,0.9428571429}}
"R"
{{14,21,-14},
{0,175,-70},
{0,0,-35}}
"Q * R"
{{12,-51,4},
{6,167,-68},
{-4,24,-41}}
using matrix_transpose from [[Matrix_transposition#Phix]]
procedure least_squares()
sequence x = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
y = {1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321},
a = repeat(repeat(0,3),length(x))
for i=1 to length(x) do
for j=1 to 3 do
a[i,j] = power(x[i],j-1)
end for
end for
{q,r} = QRHouseholder(a)
sequence t = matrix_transpose(q),
b = matrix_mul(t,vtranspose(y)),
z = repeat(0,3)
for k=3 to 1 by -1 do
atom s = 0
if k<3 then
for j = k+1 to 3 do
s += r[k,j]*z[j]
end for
end if
z[k] = (b[k][1]-s)/r[k,k]
end for
?{"Least-squares solution:",z}
end procedure
least_squares()
{{out}}
{"Least-squares solution:",{1.0,2.0,3.0}}
PowerShell
function qr([double[][]]$A) {
$m,$n = $A.count, $A[0].count
$pm,$pn = ($m-1), ($n-1)
[double[][]]$Q = 0..($m-1) | foreach{$row = @(0) * $m; $row[$_] = 1; ,$row}
[double[][]]$R = $A | foreach{$row = $_; ,@(0..$pn | foreach{$row[$_]})}
foreach ($h in 0..$pn) {
[double[]]$u = $R[$h..$pm] | foreach{$_[$h]}
[double]$nu = $u | foreach {[double]$sq = 0} {$sq += $_*$_} {[Math]::Sqrt($sq)}
$u[0] -= if ($u[0] -lt 1) {$nu} else {-$nu}
[double]$nu = $u | foreach {$sq = 0} {$sq += $_*$_} {[Math]::Sqrt($sq)}
[double[]]$u = $u | foreach { $_/$nu}
[double[][]]$v = 0..($u.Count - 1) | foreach{$i = $_; ,($u | foreach{2*$u[$i]*$_})}
[double[][]]$CR = $R | foreach{$row = $_; ,@(0..$pn | foreach{$row[$_]})}
[double[][]]$CQ = $Q | foreach{$row = $_; ,@(0..$pm | foreach{$row[$_]})}
foreach ($i in $h..$pm) {
foreach ($j in $h..$pn) {
$R[$i][$j] -= $h..$pm | foreach {[double]$sum = 0} {$sum += $v[$i-$h][$_-$h]*$CR[$_][$j]} {$sum}
}
}
if (0 -eq $h) {
foreach ($i in $h..$pm) {
foreach ($j in $h..$pm) {
$Q[$i][$j] -= $h..$pm | foreach {$sum = 0} {$sum += $v[$i][$_]*$CQ[$_][$j]} {$sum}
}
}
} else {
$p = $h-1
foreach ($i in $h..$pm) {
foreach ($j in 0..$p) {
$Q[$i][$j] -= $h..$pm | foreach {$sum = 0} {$sum += $v[$i-$h][$_-$h]*$CQ[$_][$j]} {$sum}
}
foreach ($j in $h..$pm) {
$Q[$i][$j] -= $h..$pm | foreach {$sum = 0} {$sum += $v[$i-$h][$_-$h]*$CQ[$_][$j]} {$sum}
}
}
}
}
foreach ($i in 0..$pm) {
foreach ($j in $i..$pm) {$Q[$i][$j],$Q[$j][$i] = $Q[$j][$i],$Q[$i][$j]}
}
[PSCustomObject]@{"Q" = $Q; "R" = $R}
}
function leastsquares([Double[][]]$A,[Double[]]$y) {
$QR = qr $A
[Double[][]]$Q = $QR.Q
[Double[][]]$R = $QR.R
$m,$n = $A.count, $A[0].count
[Double[]]$z = foreach ($j in 0..($m-1)) {
0..($m-1) | foreach {$sum = 0} {$sum += $Q[$_][$j]*$y[$_]} {$sum}
}
[Double[]]$x = @(0)*$n
for ($i = $n-1; $i -ge 0; $i--) {
for ($j = $i+1; $j -lt $n; $j++) {
$z[$i] -= $x[$j]*$R[$i][$j]
}
$x[$i] = $z[$i]/$R[$i][$i]
}
$x
}
function polyfit([Double[]]$x,[Double[]]$y,$n) {
$m = $x.Count
[Double[][]]$A = 0..($m-1) | foreach{$row = @(1) * ($n+1); ,$row}
for ($i = 0; $i -lt $m; $i++) {
for ($j = $n-1; 0 -le $j; $j--) {
$A[$i][$j] = $A[$i][$j+1]*$x[$i]
}
}
leastsquares $A $y
}
function show($m) {$m | foreach {write-host "$_"}}
$A = @(@(12,-51,4), @(6,167,-68), @(-4,24,-41))
$x = @(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
$y = @(1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321)
$QR = qr $A
$ps = (polyfit $x $y 2)
"Q = "
show $QR.Q
"R = "
show $QR.R
"polyfit "
"X^2 X constant"
"$(polyfit $x $y 2)"
{{out}}
Q =
-0.857142857142857 0.394285714285714 -0.331428571428571
-0.428571428571429 -0.902857142857143 0.0342857142857143
0.285714285714286 -0.171428571428571 -0.942857142857143
R =
-14 -21 14
8.88178419700125E-16 -175 70
-4.44089209850063E-16 0 35
polyfit
X^2 X constant
3 1.99999999999998 1.00000000000005
Python
{{libheader|NumPy}} Numpy has a qr function but here is a reimplementation to show construction and use of the Householder reflections.
#!/usr/bin/env python3
import numpy as np
def qr(A):
m, n = A.shape
Q = np.eye(m)
for i in range(n - (m == n)):
H = np.eye(m)
H[i:, i:] = make_householder(A[i:, i])
Q = np.dot(Q, H)
A = np.dot(H, A)
return Q, A
def make_householder(a):
v = a / (a[0] + np.copysign(np.linalg.norm(a), a[0]))
v[0] = 1
H = np.eye(a.shape[0])
H -= (2 / np.dot(v, v)) * np.dot(v[:, None], v[None, :])
return H
# task 1: show qr decomp of wp example
a = np.array(((
(12, -51, 4),
( 6, 167, -68),
(-4, 24, -41),
)))
q, r = qr(a)
print('q:\n', q.round(6))
print('r:\n', r.round(6))
# task 2: use qr decomp for polynomial regression example
def polyfit(x, y, n):
return lsqr(x[:, None]**np.arange(n + 1), y.T)
def lsqr(a, b):
q, r = qr(a)
_, n = r.shape
return np.linalg.solve(r[:n, :], np.dot(q.T, b)[:n])
x = np.array((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10))
y = np.array((1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321))
print('\npolyfit:\n', polyfit(x, y, 2))
{{out}}
q:
[[-0.857143 0.394286 0.331429]
[-0.428571 -0.902857 -0.034286]
[ 0.285714 -0.171429 0.942857]]
r:
[[ -14. -21. 14.]
[ 0. -175. 70.]
[ 0. 0. -35.]]
polyfit:
[ 1. 2. 3.]
R
# R has QR decomposition built-in (using LAPACK or LINPACK)
a <- matrix(c(12, -51, 4, 6, 167, -68, -4, 24, -41), nrow=3, ncol=3, byrow=T)
d <- qr(a)
qr.Q(d)
qr.R(d)
# now fitting a polynomial
x <- 0:10
y <- 3*x^2 + 2*x + 1
# using QR decomposition directly
a <- cbind(1, x, x^2)
qr.coef(qr(a), y)
# using least squares
a <- cbind(x, x^2)
lsfit(a, y)$coefficients
# using a linear model
xx <- x*x
m <- lm(y ~ x + xx)
coef(m)
Racket
Racket has QR-decomposition builtin:
> (require math)
> (matrix-qr (matrix [[12 -51 4]
[ 6 167 -68]
[-4 24 -41]]))
(array #[#[6/7 -69/175 -58/175] #[3/7 158/175 6/175] #[-2/7 6/35 -33/35]])
(array #[#[14 21 -14] #[0 175 -70] #[0 0 35]])
The builtin QR-decomposition uses the Gram-Schmidt algorithm.
Here is an implementation of the Householder method:
#lang racket
(require math/matrix math/array)
(define-values (T I col size)
(values ; short names
matrix-transpose identity-matrix matrix-col matrix-num-rows))
(define (scale c A) (matrix-scale A c))
(define (unit n i) (build-matrix n 1 (λ (j _) (if (= j i) 1 0))))
(define (H u)
(matrix- (I (size u))
(scale (/ 2 (matrix-dot u u))
(matrix* u (T u)))))
(define (normal a)
(define a0 (matrix-ref a 0 0))
(matrix- a (scale (* (sgn a0) (matrix-2norm a))
(unit (size a) 0))))
(define (QR A)
(define n (size A))
(for/fold ([Q (I n)] [R A]) ([i (- n 1)])
(define Hi (H (normal (submatrix R (:: i n) (:: i (+ i 1))))))
(define Hi* (if (= i 0) Hi (block-diagonal-matrix (list (I i) Hi))))
(values (matrix* Q Hi*) (matrix* Hi* R))))
(QR (matrix [[12 -51 4]
[ 6 167 -68]
[-4 24 -41]]))
Output:
(array #[#[6/7 69/175 -58/175]
#[3/7 -158/175 6/175]
#[-2/7 -6/35 -33/35]])
(array #[#[14 21 -14]
#[0 -175 70]
#[0 0 35]])
Rascal
[[File:Qrresult.jpeg||200px|thumb|right]] This function applies the Gram Schmidt algorithm. Q is printed in the console, R can be printed or visualized.
import util::Math;
import Prelude;
import vis::Figure;
import vis::Render;
public rel[real,real,real] QRdecomposition(rel[real x, real y, real v] matrix){
//orthogonalcolumns
oc = domainR(matrix, {0.0});
for (x <- sort(toList(domain(matrix)-{0.0}))){
c = domainR(matrix, {x});
o = domainR(oc, {x-1});
for (n <- [1.0 .. x]){
o = domainR(oc, {n-1});
c = matrixSubtract(c, matrixMultiplybyN(o, matrixDotproduct(o, c)/matrixDotproduct(o, o)));
}
oc += c;
}
Q = {};
//from orthogonal to orthonormal columns
for (el <- oc){
c = domainR(oc, {el[0]});
Q += matrixNormalize({el}, c);
}
//from Q to R
R= matrixMultiplication(matrixTranspose(Q), matrix);
R= {<x,y,toReal(round(v))> | <x,y,v> <- R};
println("Q:");
iprintlnExp(Q);
println();
println("R:");
return R;
}
//a function that takes the transpose of a matrix, see also Rosetta Code problem "Matrix transposition"
public rel[real, real, real] matrixTranspose(rel[real x, real y, real v] matrix){
return {<y, x, v> | <x, y, v> <- matrix};
}
//a function to normalize an element of a matrix by the normalization of a column
public rel[real,real,real] matrixNormalize(rel[real x, real y, real v] element, rel[real x, real y, real v] column){
normalized = 1.0/nroot((0.0 | it + v*v | <x,y,v> <- column), 2);
return matrixMultiplybyN(element, normalized);
}
//a function that takes the dot product, see also Rosetta Code problem "Dot product"
public real matrixDotproduct(rel[real x, real y, real v] column1, rel[real x, real y, real v] column2){
return (0.0 | it + v1*v2 | <x1,y1,v1> <- column1, <x2,y2,v2> <- column2, y1==y2);
}
//a function to subtract two columns
public rel[real,real,real] matrixSubtract(rel[real x, real y, real v] column1, rel[real x, real y, real v] column2){
return {<x1,y1,v1-v2> | <x1,y1,v1> <- column1, <x2,y2,v2> <- column2, y1==y2};
}
//a function to multiply a column by a number
public rel[real,real,real] matrixMultiplybyN(rel[real x, real y, real v] column, real n){
return {<x,y,v*n> | <x,y,v> <- column};
}
//a function to perform matrix multiplication, see also Rosetta Code problem "Matrix multiplication".
public rel[real, real, real] matrixMultiplication(rel[real x, real y, real v] matrix1, rel[real x, real y, real v] matrix2){
if (max(matrix1.x) == max(matrix2.y)){
p = {<x1,y1,x2,y2, v1*v2> | <x1,y1,v1> <- matrix1, <x2,y2,v2> <- matrix2};
result = {};
for (y <- matrix1.y){
for (x <- matrix2.x){
v = (0.0 | it + v | <x1, y1, x2, y2, v> <- p, x==x2 && y==y1, x1==y2 && y2==x1);
result += <x,y,v>;
}
}
return result;
}
else throw "Matrix sizes do not match.";
}
// a function to visualize the result
public void displayMatrix(rel[real x, real y, real v] matrix){
points = [box(text("<v>"), align(0.3333*(x+1),0.3333*(y+1)),shrink(0.25)) | <x,y,v> <- matrix];
render(overlay([*points], aspectRatio(1.0)));
}
//a matrix, given by a relation of <x-coordinate, y-coordinate, value>.
public rel[real x, real y, real v] matrixA = {
<0.0,0.0,12.0>, <0.0,1.0, 6.0>, <0.0,2.0,-4.0>,
<1.0,0.0,-51.0>, <1.0,1.0,167.0>, <1.0,2.0,24.0>,
<2.0,0.0,4.0>, <2.0,1.0,-68.0>, <2.0,2.0,-41.0>
};
Example using visualization
rascal>displayMatrix(QRdecomposition(matrixA))
Q:
{
<1.0,0.0,-0.394285714285714285714285714285714285714285714285714285714285714285713300>,
<0.0,0.0,0.857142857142857142857142857142857142857142857142857142857142857142840>,
<0.0,1.0,0.428571428571428571428571428571428571428571428571428571428571428571420>,
<0.0,2.0,-0.285714285714285714285714285714285714285714285714285714285714285714280>,
<2.0,0.0,-0.33142857142857142857142857142857142857142857142857142857142857142858800>,
<1.0,2.0,0.171428571428571428571428571428571428571428571428571428571428571428571000>,
<2.0,2.0,-0.94285714285714285714285714285714285714285714285714285714285714285719000>,
<1.0,1.0,0.902857142857142857142857142857142857142857142857142857142857142857140600>,
<2.0,1.0,0.03428571428571428571428571428571428571428571428571428571428571428571600>
}
See R in picture
SAS
/* See http://support.sas.com/documentation/cdl/en/imlug/63541/HTML/default/viewer.htm#imlug_langref_sect229.htm */
proc iml;
a={12 -51 4,6 167 -68,-4 24 -41};
print(a);
call qr(q,r,p,d,a);
print(q);
print(r);
quit;
/*
a
12 -51 4
6 167 -68
-4 24 -41
q
-0.857143 0.3942857 -0.331429
-0.428571 -0.902857 0.0342857
0.2857143 -0.171429 -0.942857
r
-14 -21 14
0 -175 70
0 0 35
*/
Scala
{{Out}}Best seen running in your browser [https://scastie.scala-lang.org/NMueO16uQl6oivliBKZHew Scastie (remote JVM)].
import java.io.{PrintWriter, StringWriter}
import Jama.{Matrix, QRDecomposition}
object QRDecomposition extends App {
val matrix =
new Matrix(
Array[Array[Double]](Array(12, -51, 4),
Array(6, 167, -68),
Array(-4, 24, -41)))
val d = new QRDecomposition(matrix)
def toString(m: Matrix): String = {
val sw = new StringWriter
m.print(new PrintWriter(sw, true), 8, 6)
sw.toString
}
print(toString(d.getQ))
print(toString(d.getR))
}
SequenceL
{{trans|Go}}
;
import <Utilities/Sequence.sl>;
import <Utilities/Conversion.sl>;
main :=
let
qrTest := [[12.0, -51.0, 4.0],
[ 6.0, 167.0, -68.0],
[-4.0, 24.0, -41.0]];
qrResult := qr(qrTest);
x := 1.0*(0 ... 10);
y := 1.0*[1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321];
regResult := polyfit(x, y, 2);
in
"q:\n" ++ delimit(delimit(floatToString(qrResult[1], 6), ','), '\n') ++ "\n\n" ++
"r:\n" ++ delimit(delimit(floatToString(qrResult[2], 1), ','), '\n') ++ "\n\n" ++
"polyfit:\n" ++ "[" ++ delimit(floatToString(regResult, 1), ',') ++ "]";
//---Polynomial Regression---
polyfit(x(1), y(1), n) :=
let
a[j] := x ^ j foreach j within 0 ... n;
in
lsqr(transpose(a), transpose([y]));
lsqr(a(2), b(2)) :=
let
qrDecomp := qr(a);
prod := mm(transpose(qrDecomp[1]), b);
in
solveUT(qrDecomp[2], prod);
solveUT(r(2), b(2)) :=
let
n := size(r[1]);
in
solveUTHelper(r, b, n, duplicate(0.0, n));
solveUTHelper(r(2), b(2), k, x(1)) :=
let
n := size(r[1]);
newX := setElementAt(x, k, (b[k][1] - sum(r[k][(k+1) ... n] * x[(k+1) ... n])) / r[k][k]);
in
x when k <= 0
else
solveUTHelper(r, b, k - 1, newX);
//---QR Decomposition---
qr(A(2)) := qrHelper(A, id(size(A)), 1);
qrHelper(A(2), Q(2), i) :=
let
m := size(A);
n := size(A[1]);
householder := makeHouseholder(A[i ... m, i]);
H[j,k] :=
householder[j - i + 1][k - i + 1] when j >= i and k >= i
else
1.0 when j = k else 0.0
foreach j within 1 ... m,
k within 1 ... m;
in
[Q,A] when i > (n - 1 when m = n else n)
else
qrHelper(mm(H, A), mm(Q, H), i + 1);
makeHouseholder(a(1)) :=
let
v := [1.0] ++ tail(a / (a[1] + sqrt(sum(a ^ 2)) * sign(a[1])));
H := id(size(a)) - (2.0 / mm([v], transpose([v])))[1,1] * mm(transpose([v]), [v]);
in
H;
//---Utilities---
id(n)[i,j] := 1.0 when i = j else 0.0
foreach i within 1 ... n,
j within 1 ... n;
mm(A(2), B(2))[i,j] := sum( A[i] * transpose(B)[j] );
{{out}}
"q:
-0.857143,0.394286,0.331429
-0.428571,-0.902857,-0.034286
0.285714,-0.171429,0.942857
r:
-14.0,-21.0,14.0
-0.0,-175.0,70.0
0.0,0.0,-35.0
polyfit:
[1.0,2.0,3.0]"
SPAD
See [[QR_decomposition#Axiom]] in Axiom.
Standard ML
{{trans|Axiom}} We first define a signature for a radical category joined with a field. We then define a functor with (a) structures to define operators and functions for Array and Array2, and (b) functions for the QR decomposition:
signature RADCATFIELD = sig
type real
val zero : real
val one : real
val + : real * real -> real
val - : real * real -> real
val * : real * real -> real
val / : real * real -> real
val sign : real -> real
val sqrt : real -> real
end
functor QR(F: RADCATFIELD) = struct
structure A = struct
local
open Array
in
fun unitVector n = tabulate (n, fn i => if i=0 then F.one else F.zero)
fun map f x = tabulate(length x, fn i => f(sub(x,i)))
fun map2 f (x, y) = tabulate(length x, fn i => f(sub(x,i),sub(y,i)))
val op + = map2 F.+
val op - = map2 F.-
val op * = map2 F.*
fun multc(c,x) = array(length x,c)*x
fun dot (x,y) = foldl F.+ F.zero (x*y)
fun outer f (x,y) =
Array2.tabulate Array2.RowMajor (length x, length y,
fn (i,j) => f(sub(x,i),sub(y,j)))
fun copy x = map (fn x => x) x
fun fromVector v = tabulate(Vector.length v, fn i => Vector.sub(v,i))
fun slice(x,i,sz) =
let open ArraySlice
val s = slice(x,i,sz)
in Array.tabulate(length s, fn i => sub(s,i)) end
end
end
structure M = struct
local
open Array2
in
fun map f x = tabulate RowMajor (nRows x, nCols x, fn (i,j) => f(sub(x,i,j)))
fun map2 f (x, y) =
tabulate RowMajor (nRows x, nCols x, fn (i,j) => f(sub(x,i,j),sub(y,i,j)))
fun scalarMatrix(m, x) = tabulate RowMajor (m,m,fn (i,j) => if i=j then x else F.zero)
fun multc(c, x) = map (fn xij => F.*(c,xij)) x
val op + = map2 F.+
val op - = map2 F.-
fun column(x,i) = A.fromVector(Array2.column(x,i))
fun row(x,i) = A.fromVector(Array2.row(x,i))
fun x*y = tabulate RowMajor (nRows x, nCols y,
fn (i,j) => A.dot(row(x,i), column(y,j)))
fun multa(x,a) = Array.tabulate (nRows x, fn i => A.dot(row(x,i), a))
fun copy x = map (fn x => x) x
fun subMatrix(h, i1, i2, j1, j2) =
tabulate RowMajor (Int.+(Int.-(i2,i1),1),
Int.+(Int.-(j2,j1),1),
fn (a,b) => sub(h,Int.+(i1,a),Int.+(j1,b)))
fun transpose m = tabulate RowMajor (nCols m,
nRows m,
fn (i,j) => sub(m,j,i))
fun updateSubMatrix(h,i,j,s) =
tabulate RowMajor (nRows s, nCols s, fn (a,b) => update(h,Int.+(i,a),Int.+(j,b),sub(s,a,b)))
end
end
fun toList a =
List.tabulate(Array2.nRows a, fn i => List.tabulate(Array2.nCols a, fn j => Array2.sub(a,i,j)))
fun householder a =
let open Array
val m = length a
val len = F.sqrt(A.dot(a,a))
val u = A.+(a, A.multc(F.*(len,F.sign(sub(a,0))), A.unitVector m))
val v = A.multc(F./(F.one,sub(u,0)), u)
val beta = F./(F.+(F.one,F.one),A.dot(v,v))
in
M.-(M.scalarMatrix(m,F.one), M.multc(beta,A.outer F.* (v,v)))
end
fun qr mat =
let open Array2
val (m,n) = dimensions mat
val upperIndex = if m=n then Int.-(n,1) else n
fun loop(i,qm,rm) = if i=upperIndex then {q=qm,r=rm} else
let val x = A.slice(A.fromVector(column(rm,i)),i,NONE)
val h = M.scalarMatrix(m,F.one)
val _ = M.updateSubMatrix(h,i,i,householder x)
in
loop(Int.+(i,1), M.*(qm,h), M.*(h,rm))
end
in
loop(0, M.scalarMatrix(m,F.one), mat)
end
fun solveUpperTriangular(r,b) =
let open Array
val n = Array2.nCols r
val x = array(n, F.zero)
fun loop k =
let val index = Int.min(Int.-(n,1),Int.+(k,1))
val _ = update(x,k,
F./(F.-(sub(b,k),
A.dot(A.slice(x,index,NONE),
A.slice(M.row(r,k),index,NONE))),
Array2.sub(r,k,k)))
in
if k=0 then x else loop(Int.-(k,1))
end
in
loop (Int.-(n,1))
end
fun lsqr(a,b) =
let val {q,r} = qr a
val n = Array2.nCols r
in
solveUpperTriangular(M.subMatrix(r, 0, Int.-(n,1), 0, Int.-(n,1)),
M.multa(M.transpose(q), b))
end
fun pow(x,1) = x
| pow(x,n) = F.*(x,pow(x,Int.-(n,1)))
fun polyfit(x,y,n) =
let open Array2
val a = tabulate RowMajor (Array.length x,
Int.+(n,1),
fn (i,j) => if j=0 then F.one else
pow(Array.sub(x,i),j))
in
lsqr(a,y)
end
end
We can then show the examples:
structure RealRadicalCategoryField : RADCATFIELD = struct
open Real
val one = 1.0
val zero = 0.0
val sign = real o Real.sign
val sqrt = Real.Math.sqrt
end
structure Q = QR(RealRadicalCategoryField);
let
val mat = Array2.fromList [[12.0, ~51.0, 4.0], [6.0, 167.0, ~68.0], [~4.0, 24.0, ~41.0]]
val {q,r} = Q.qr(mat)
in
{q=Q.toList q; r=Q.toList r}
end;
(* output *)
val it =
{q=[[~0.857142857143,0.394285714286,0.331428571429],
[~0.428571428571,~0.902857142857,~0.0342857142857],
[0.285714285714,~0.171428571429,0.942857142857]],
r=[[~14.0,~21.0,14.0],[5.97812397875E~18,~175.0,70.0],
[4.47505280695E~16,0.0,~35.0]]} : {q:real list list, r:real list list}
let open Array
val x = fromList [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
val y = fromList [1.0, 6.0, 17.0, 34.0, 57.0, 86.0, 121.0, 162.0, 209.0, 262.0, 321.0]
in
Q.polyfit(x, y, 2)
end;
(* output *)
val it = [|1.0,2.0,3.0|] : real array
Stata
See [http://www.stata.com/help.cgi?mf_qrd QR decomposition] in Stata help.
mata
: qrd(a=(12,-51,4\6,167,-68\-4,24,-41),q=.,r=.)
: a
1 2 3
+-------------------+
1 | 12 -51 4 |
2 | 6 167 -68 |
3 | -4 24 -41 |
+-------------------+
: q
1 2 3
+----------------------------------------------+
1 | -.8571428571 .3942857143 .3314285714 |
2 | -.4285714286 -.9028571429 -.0342857143 |
3 | .2857142857 -.1714285714 .9428571429 |
+----------------------------------------------+
: r
1 2 3
+----------------------+
1 | -14 -21 14 |
2 | 0 -175 70 |
3 | 0 0 -35 |
+----------------------+
Tcl
Assuming the presence of the Tcl solutions to these tasks: [[Element-wise operations]], [[Matrix multiplication]], [[Matrix transposition]] {{trans|Common Lisp}}
package require Tcl 8.5
namespace path {::tcl::mathfunc ::tcl::mathop}
proc sign x {expr {$x == 0 ? 0 : $x < 0 ? -1 : 1}}
proc norm vec {
set s 0
foreach x $vec {set s [expr {$s + $x**2}]}
return [sqrt $s]
}
proc unitvec n {
set v [lrepeat $n 0.0]
lset v 0 1.0
return $v
}
proc I n {
set m [lrepeat $n [lrepeat $n 0.0]]
for {set i 0} {$i < $n} {incr i} {lset m $i $i 1.0}
return $m
}
proc arrayEmbed {A B row col} {
# $A will be copied automatically; Tcl values are copy-on-write
lassign [size $B] mb nb
for {set i 0} {$i < $mb} {incr i} {
for {set j 0} {$j < $nb} {incr j} {
lset A [expr {$row + $i}] [expr {$col + $j}] [lindex $B $i $j]
}
}
return $A
}
# Unlike the Common Lisp version, here we use a specialist subcolumn
# extraction function: like that, there's a lot less intermediate memory allocation
# and the code is actually clearer.
proc subcolumn {A size column} {
for {set i $column} {$i < $size} {incr i} {lappend x [lindex $A $i $column]}
return $x
}
proc householder A {
lassign [size $A] m
set U [m+ $A [.* [unitvec $m] [expr {[norm $A] * [sign [lindex $A 0 0]]}]]]
set V [./ $U [lindex $U 0 0]]
set beta [expr {2.0 / [lindex [matrix_multiply [transpose $V] $V] 0 0]}]
return [m- [I $m] [.* [matrix_multiply $V [transpose $V]] $beta]]
}
proc qrDecompose A {
lassign [size $A] m n
set Q [I $m]
for {set i 0} {$i < ($m==$n ? $n-1 : $n)} {incr i} {
# Construct the Householder matrix
set H [arrayEmbed [I $m] [householder [subcolumn $A $n $i]] $i $i]
# Apply to build the decomposition
set Q [matrix_multiply $Q $H]
set A [matrix_multiply $H $A]
}
return [list $Q $A]
}
Demonstrating:
set demo [qrDecompose {{12 -51 4} {6 167 -68} {-4 24 -41}}]
puts "==Q=="
print_matrix [lindex $demo 0] "%f"
puts "==R=="
print_matrix [lindex $demo 1] "%.1f"
Output:
==Q==
-0.857143 0.394286 0.331429
-0.428571 -0.902857 -0.034286
0.285714 -0.171429 0.942857
==R==
-14.0 -21.0 14.0
0.0 -175.0 70.0
0.0 0.0 -35.0
VBA
{{trans|Phix}}
Option Base 1
Private Function vtranspose(v As Variant) As Variant
'-- transpose a vector of length m into an mx1 matrix,
'-- eg {1,2,3} -> {1;2;3}
vtranspose = WorksheetFunction.Transpose(v)
End Function
Private Function mat_col(a As Variant, col As Integer) As Variant
Dim res() As Double
ReDim res(UBound(a))
For i = col To UBound(a)
res(i) = a(i, col)
Next i
mat_col = res
End Function
Private Function mat_norm(a As Variant) As Double
mat_norm = Sqr(WorksheetFunction.SumProduct(a, a))
End Function
Private Function mat_ident(n As Integer) As Variant
mat_ident = WorksheetFunction.Munit(n)
End Function
Private Function sq_div(a As Variant, p As Double) As Variant
Dim res() As Variant
ReDim res(UBound(a))
For i = 1 To UBound(a)
res(i) = a(i) / p
Next i
sq_div = res
End Function
Private Function sq_mul(p As Double, a As Variant) As Variant
Dim res() As Variant
ReDim res(UBound(a), UBound(a, 2))
For i = 1 To UBound(a)
For j = 1 To UBound(a, 2)
res(i, j) = p * a(i, j)
Next j
Next i
sq_mul = res
End Function
Private Function sq_sub(x As Variant, y As Variant) As Variant
Dim res() As Variant
ReDim res(UBound(x), UBound(x, 2))
For i = 1 To UBound(x)
For j = 1 To UBound(x, 2)
res(i, j) = x(i, j) - y(i, j)
Next j
Next i
sq_sub = res
End Function
Private Function matrix_mul(x As Variant, y As Variant) As Variant
matrix_mul = WorksheetFunction.MMult(x, y)
End Function
Private Function QRHouseholder(ByVal a As Variant) As Variant
Dim columns As Integer: columns = UBound(a, 2)
Dim rows As Integer: rows = UBound(a)
Dim m As Integer: m = WorksheetFunction.Max(columns, rows)
Dim n As Integer: n = WorksheetFunction.Min(rows, columns)
I_ = mat_ident(m)
Q_ = I_
Dim q As Variant
Dim u As Variant, v As Variant, j As Integer
For j = 1 To WorksheetFunction.Min(m - 1, n)
u = mat_col(a, j)
u(j) = u(j) - mat_norm(u)
v = sq_div(u, mat_norm(u))
q = sq_sub(I_, sq_mul(2, matrix_mul(vtranspose(v), v)))
a = matrix_mul(q, a)
Q_ = matrix_mul(Q_, q)
Next j
'-- Get the upper triangular matrix R.
Dim R() As Variant
ReDim R(m, n)
For i = 1 To m 'in Phix this is n
For j = 1 To n 'in Phix this is i to n. starting at 1 to fill zeroes
R(i, j) = a(i, j)
Next j
Next i
Dim res(2) As Variant
res(1) = Q_
res(2) = R
QRHouseholder = res
End Function
Private Sub pp(m As Variant)
For i = 1 To UBound(m)
For j = 1 To UBound(m, 2)
Debug.Print Format(m(i, j), "0.#####"),
Next j
Debug.Print
Next i
End Sub
Public Sub main()
a = [{12, -51, 4; 6, 167, -68; -4, 24, -41;-1,1,0;2,0,3}]
result = QRHouseholder(a)
q = result(1)
r_ = result(2)
Debug.Print "A"
pp a
Debug.Print "Q"
pp q
Debug.Print "R"
pp r_
Debug.Print "Q * R"
pp matrix_mul(q, r_)
End Sub
{{out}}
A
12, -51, 4,
6, 167, -68,
-4, 24, -41,
-1, 1, 0,
2, 0, 3,
Q
0,84641 -0,39129 -0,34312 0,06641 -0,09126
0,42321 0,90409 0,02927 0,01752 -0,04856
-0,28214 0,17042 -0,93286 -0,02237 0,14365
-0,07053 0,01404 0,0011 0,99738 0,00728
0,14107 -0,01666 0,10577 0,00291 0,98419
R
14,17745 20,66663 -13,40157
0, 175,04254 -70,08031
0, 0, 35,20154
0, 0, 0,
0, 0, 0,
Q * R
12, -51, 4,
6, 167, -68,
-4, 24, -41,
-1, 1, 0,
2, 0, 3,
Least squares
Public Sub least_squares()
x = [{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}]
y = [{1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321}]
Dim a() As Double
ReDim a(UBound(x), 3)
For i = 1 To UBound(x)
For j = 1 To 3
a(i, j) = x(i) ^ (j - 1)
Next j
Next i
result = QRHouseholder(a)
q = result(1)
r_ = result(2)
t = WorksheetFunction.Transpose(q)
b = matrix_mul(t, vtranspose(y))
Dim z(3) As Double
For k = 3 To 1 Step -1
Dim s As Double: s = 0
If k < 3 Then
For j = k + 1 To 3
s = s + r_(k, j) * z(j)
Next j
End If
z(k) = (b(k, 1) - s) / r_(k, k)
Next k
Debug.Print "Least-squares solution:",
For i = 1 To 3
Debug.Print Format(z(i), "0.#####"),
Next i
End Sub
{{out}}
Least-squares solution: 1, 2, 3,
zkl
var [const] GSL=Import("zklGSL"); // libGSL (GNU Scientific Library)
A:=GSL.Matrix(3,3).set(12.0, -51.0, 4.0,
6.0, 167.0, -68.0,
4.0, 24.0, -41.0);
Q,R:=A.QRDecomp();
println("Q:\n",Q.format());
println("R:\n",R.format());
println("Q*R:\n",(Q*R).format());
{{out}}
Q:
-0.86, 0.47, -0.22
-0.43, -0.88, -0.20
-0.29, -0.08, 0.95
R:
-14.00, -34.71, 37.43
0.00, -172.80, 65.07
0.00, 0.00, -26.19
Q*R:
12.00, -51.00, 4.00
6.00, 167.00, -68.00
4.00, 24.00, -41.00