Cholesky decomposition
You are encouraged to solve this task according to the task description, using any language you may know.
Every symmetric, positive definite matrix A can be decomposed into a product of a unique lower triangular matrix L and its transpose:
is called the Cholesky factor of , and can be interpreted as a generalized square root of , as described in Cholesky decomposition.
In a 3x3 example, we have to solve the following system of equations:
We can see that for the diagonal elements () of there is a calculation pattern:
or in general:
For the elements below the diagonal (, where ) there is also a calculation pattern:
which can also be expressed in a general formula:
Task description
The task is to implement a routine which will return a lower Cholesky factor for every given symmetric, positive definite nxn matrix . You should then test it on the following two examples and include your output.
Example 1:
25 15 -5 5 0 0 15 18 0 --> 3 3 0 -5 0 11 -1 1 3
Example 2:
18 22 54 42 4.24264 0.00000 0.00000 0.00000 22 70 86 62 --> 5.18545 6.56591 0.00000 0.00000 54 86 174 134 12.72792 3.04604 1.64974 0.00000 42 62 134 106 9.89949 1.62455 1.84971 1.39262
- Note
- The Cholesky decomposition of a Pascal upper-triangle matrix is the Identity matrix of the same size.
- The Cholesky decomposition of a Pascal symmetric matrix is the Pascal lower-triangle matrix of the same size.
Ada
decomposition.ads: <lang Ada>with Ada.Numerics.Generic_Real_Arrays; generic
with package Matrix is new Ada.Numerics.Generic_Real_Arrays (<>);
package Decomposition is
-- decompose a square matrix A by A = L * Transpose (L) procedure Decompose (A : Matrix.Real_Matrix; L : out Matrix.Real_Matrix);
end Decomposition;</lang>
decomposition.adb: <lang Ada>with Ada.Numerics.Generic_Elementary_Functions;
package body Decomposition is
package Math is new Ada.Numerics.Generic_Elementary_Functions
(Matrix.Real);
procedure Decompose (A : Matrix.Real_Matrix; L : out Matrix.Real_Matrix) is
use type Matrix.Real_Matrix, Matrix.Real;
Order : constant Positive := A'Length (1);
S : Matrix.Real;
begin
L := (others => (others => 0.0));
for I in 0 .. Order - 1 loop
for K in 0 .. I loop
S := 0.0;
for J in 0 .. K - 1 loop
S := S +
L (L'First (1) + I, L'First (2) + J) *
L (L'First (1) + K, L'First (2) + J);
end loop;
-- diagonals
if K = I then
L (L'First (1) + K, L'First (2) + K) :=
Math.Sqrt (A (A'First (1) + K, A'First (2) + K) - S);
else
L (L'First (1) + I, L'First (2) + K) :=
1.0 / L (L'First (1) + K, L'First (2) + K) *
(A (A'First (1) + I, A'First (2) + K) - S);
end if;
end loop;
end loop;
end Decompose;
end Decomposition;</lang>
Example usage: <lang Ada>with Ada.Numerics.Real_Arrays; with Ada.Text_IO; with Decomposition; procedure Decompose_Example is
package Real_Decomposition is new Decomposition
(Matrix => Ada.Numerics.Real_Arrays);
package Real_IO is new Ada.Text_IO.Float_IO (Float);
procedure Print (M : Ada.Numerics.Real_Arrays.Real_Matrix) is
begin
for Row in M'Range (1) loop
for Col in M'Range (2) loop
Real_IO.Put (M (Row, Col), 4, 3, 0);
end loop;
Ada.Text_IO.New_Line;
end loop;
end Print;
Example_1 : constant Ada.Numerics.Real_Arrays.Real_Matrix :=
((25.0, 15.0, -5.0),
(15.0, 18.0, 0.0),
(-5.0, 0.0, 11.0));
L_1 : Ada.Numerics.Real_Arrays.Real_Matrix (Example_1'Range (1),
Example_1'Range (2));
Example_2 : constant Ada.Numerics.Real_Arrays.Real_Matrix :=
((18.0, 22.0, 54.0, 42.0),
(22.0, 70.0, 86.0, 62.0),
(54.0, 86.0, 174.0, 134.0),
(42.0, 62.0, 134.0, 106.0));
L_2 : Ada.Numerics.Real_Arrays.Real_Matrix (Example_2'Range (1),
Example_2'Range (2));
begin
Real_Decomposition.Decompose (A => Example_1,
L => L_1);
Real_Decomposition.Decompose (A => Example_2,
L => L_2);
Ada.Text_IO.Put_Line ("Example 1:");
Ada.Text_IO.Put_Line ("A:"); Print (Example_1);
Ada.Text_IO.Put_Line ("L:"); Print (L_1);
Ada.Text_IO.New_Line;
Ada.Text_IO.Put_Line ("Example 2:");
Ada.Text_IO.Put_Line ("A:"); Print (Example_2);
Ada.Text_IO.Put_Line ("L:"); Print (L_2);
end Decompose_Example;</lang>
- Output:
Example 1: A: 25.000 15.000 -5.000 15.000 18.000 0.000 -5.000 0.000 11.000 L: 5.000 0.000 0.000 3.000 3.000 0.000 -1.000 1.000 3.000 Example 2: A: 18.000 22.000 54.000 42.000 22.000 70.000 86.000 62.000 54.000 86.000 174.000 134.000 42.000 62.000 134.000 106.000 L: 4.243 0.000 0.000 0.000 5.185 6.566 0.000 0.000 12.728 3.046 1.650 0.000 9.899 1.625 1.850 1.393
ALGOL 68
Note: This specimen retains the original C coding style. diff
<lang algol68>#!/usr/local/bin/a68g --script #
MODE FIELD=LONG REAL; PROC (FIELD)FIELD field sqrt = long sqrt; INT field prec = 5; FORMAT field fmt = $g(-(2+1+field prec),field prec)$;
MODE MAT = [0,0]FIELD;
PROC cholesky = (MAT a) MAT:(
[UPB a, 2 UPB a]FIELD l;
FOR i FROM LWB a TO UPB a DO
FOR j FROM 2 LWB a TO i DO
FIELD s := 0;
FOR k FROM 2 LWB a TO j-1 DO
s +:= l[i,k] * l[j,k]
OD;
l[i,j] := IF i = j
THEN field sqrt(a[i,i] - s)
ELSE 1.0 / l[j,j] * (a[i,j] - s) FI
OD;
FOR j FROM i+1 TO 2 UPB a DO
l[i,j]:=0 # Not required if matrix is declared as triangular #
OD
OD;
l
);
PROC print matrix v1 =(MAT a)VOID:(
FOR i FROM LWB a TO UPB a DO
FOR j FROM 2 LWB a TO 2 UPB a DO
printf(($g(-(2+1+field prec),field prec)$, a[i,j]))
OD;
printf($l$)
OD
);
PROC print matrix =(MAT a)VOID:(
FORMAT vector fmt = $"("f(field fmt)n(2 UPB a-2 LWB a)(", " f(field fmt))")"$;
FORMAT matrix fmt = $"("f(vector fmt)n( UPB a- LWB a)(","lxf(vector fmt))")"$;
printf((matrix fmt, a))
);
main: (
MAT m1 = ((25, 15, -5),
(15, 18, 0),
(-5, 0, 11));
MAT c1 = cholesky(m1);
print matrix(c1);
printf($l$);
MAT m2 = ((18, 22, 54, 42),
(22, 70, 86, 62),
(54, 86, 174, 134),
(42, 62, 134, 106));
MAT c2 = cholesky(m2);
print matrix(c2)
)</lang>
- Output:
(( 5.00000, 0.00000, 0.00000), ( 3.00000, 3.00000, 0.00000), (-1.00000, 1.00000, 3.00000)) (( 4.24264, 0.00000, 0.00000, 0.00000), ( 5.18545, 6.56591, 0.00000, 0.00000), (12.72792, 3.04604, 1.64974, 0.00000), ( 9.89949, 1.62455, 1.84971, 1.39262))
BBC BASIC
<lang bbcbasic> DIM m1(2,2)
m1() = 25, 15, -5, \
\ 15, 18, 0, \
\ -5, 0, 11
PROCcholesky(m1())
PROCprint(m1())
PRINT
@% = &2050A
DIM m2(3,3)
m2() = 18, 22, 54, 42, \
\ 22, 70, 86, 62, \
\ 54, 86, 174, 134, \
\ 42, 62, 134, 106
PROCcholesky(m2())
PROCprint(m2())
END
DEF PROCcholesky(a())
LOCAL i%, j%, k%, l(), s
DIM l(DIM(a(),1),DIM(a(),2))
FOR i% = 0 TO DIM(a(),1)
FOR j% = 0 TO i%
s = 0
FOR k% = 0 TO j%-1
s += l(i%,k%) * l(j%,k%)
NEXT
IF i% = j% THEN
l(i%,j%) = SQR(a(i%,i%) - s)
ELSE
l(i%,j%) = (a(i%,j%) - s) / l(j%,j%)
ENDIF
NEXT j%
NEXT i%
a() = l()
ENDPROC
DEF PROCprint(a())
LOCAL row%, col%
FOR row% = 0 TO DIM(a(),1)
FOR col% = 0 TO DIM(a(),2)
PRINT a(row%,col%);
NEXT
PRINT
NEXT row%
ENDPROC</lang>
Output:
5 0 0
3 3 0
-1 1 3
4.24264 0.00000 0.00000 0.00000
5.18545 6.56591 0.00000 0.00000
12.72792 3.04604 1.64974 0.00000
9.89949 1.62455 1.84971 1.39262
C
<lang c>#include <stdio.h>
- include <stdlib.h>
- include <math.h>
double *cholesky(double *A, int n) {
double *L = (double*)calloc(n * n, sizeof(double));
if (L == NULL)
exit(EXIT_FAILURE);
for (int i = 0; i < n; i++)
for (int j = 0; j < (i+1); j++) {
double s = 0;
for (int k = 0; k < j; k++)
s += L[i * n + k] * L[j * n + k];
L[i * n + j] = (i == j) ?
sqrt(A[i * n + i] - s) :
(1.0 / L[j * n + j] * (A[i * n + j] - s));
}
return L;
}
void show_matrix(double *A, int n) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++)
printf("%2.5f ", A[i * n + j]);
printf("\n");
}
}
int main() {
int n = 3;
double m1[] = {25, 15, -5,
15, 18, 0,
-5, 0, 11};
double *c1 = cholesky(m1, n);
show_matrix(c1, n);
printf("\n");
free(c1);
n = 4;
double m2[] = {18, 22, 54, 42,
22, 70, 86, 62,
54, 86, 174, 134,
42, 62, 134, 106};
double *c2 = cholesky(m2, n);
show_matrix(c2, n);
free(c2);
return 0;
}</lang>
- Output:
5.00000 0.00000 0.00000 3.00000 3.00000 0.00000 -1.00000 1.00000 3.00000 4.24264 0.00000 0.00000 0.00000 5.18545 6.56591 0.00000 0.00000 12.72792 3.04604 1.64974 0.00000 9.89949 1.62455 1.84971 1.39262
C#
<lang [C sharp|C#]> using System; using System.Collections.Generic; using System.Linq; using System.Text;
namespace Cholesky {
class Program
{
/// <summary>
/// This is example is written in C#, and compiles with .NET Framework 4.0
/// </summary>
/// <param name="args"></param>
static void Main(string[] args)
{
double[,] test1 = new double[,]
{
{25, 15, -5},
{15, 18, 0},
{-5, 0, 11},
};
double[,] test2 = new double[,]
{
{18, 22, 54, 42},
{22, 70, 86, 62},
{54, 86, 174, 134},
{42, 62, 134, 106},
};
double[,] chol1 = Cholesky(test1);
double[,] chol2 = Cholesky(test2);
Console.WriteLine("Test 1: ");
Print(test1);
Console.WriteLine("");
Console.WriteLine("Lower Cholesky 1: ");
Print(chol1);
Console.WriteLine("");
Console.WriteLine("Test 2: ");
Print(test2);
Console.WriteLine("");
Console.WriteLine("Lower Cholesky 2: ");
Print(chol2);
}
public static void Print(double[,] a)
{
int n = (int)Math.Sqrt(a.Length);
StringBuilder sb = new StringBuilder();
for (int r = 0; r < n; r++)
{
string s = "";
for (int c = 0; c < n; c++)
{
s += a[r, c].ToString("f5").PadLeft(9) + ",";
}
sb.AppendLine(s);
}
Console.WriteLine(sb.ToString());
}
/// <summary>
/// Returns the lower Cholesky Factor, L, of input matrix A.
/// Satisfies the equation: L*L^T = A.
/// </summary>
/// <param name="a">Input matrix must be square, symmetric,
/// and positive definite. This method does not check for these properties,
/// and may produce unexpected results of those properties are not met.</param>
/// <returns></returns>
public static double[,] Cholesky(double[,] a)
{
int n = (int)Math.Sqrt(a.Length);
double[,] ret = new double[n, n];
for (int r = 0; r < n; r++)
for (int c = 0; c <= r; c++)
{
if (c == r)
{
double sum = 0;
for (int j = 0; j < c; j++)
{
sum += ret[c, j] * ret[c, j];
}
ret[c, c] = Math.Sqrt(a[c, c] - sum);
}
else
{
double sum = 0;
for (int j = 0; j < c; j++)
sum += ret[r, j] * ret[c, j];
ret[r, c] = 1.0 / ret[c, c] * (a[r, c] - sum);
}
}
return ret;
}
}
}
</lang>
- Output:
Test 1:
25.00000, 15.00000, -5.00000, 15.00000, 18.00000, 0.00000, -5.00000, 0.00000, 11.00000,
Lower Cholesky 1:
5.00000, 0.00000, 0.00000, 3.00000, 3.00000, 0.00000, -1.00000, 1.00000, 3.00000,
Test 2:
18.00000, 22.00000, 54.00000, 42.00000, 22.00000, 70.00000, 86.00000, 62.00000, 54.00000, 86.00000,174.00000,134.00000, 42.00000, 62.00000,134.00000,106.00000,
Lower Cholesky 2:
4.24264, 0.00000, 0.00000, 0.00000, 5.18545, 6.56591, 0.00000, 0.00000, 12.72792, 3.04604, 1.64974, 0.00000, 9.89949, 1.62455, 1.84971, 1.39262,
Clojure
<lang clojure>(defn cholesky
[matrix]
(let [n (count matrix)
A (to-array-2d matrix)
L (make-array Double/TYPE n n)]
(doseq [i (range n) j (range (inc i))]
(let [s (reduce + (for [k (range j)] (* (aget L i k) (aget L j k))))]
(aset L i j (if (= i j)
(Math/sqrt (- (aget A i i) s))
(* (/ 1.0 (aget L j j)) (- (aget A i j) s))))))
(vec (map vec L))))</lang>
Example: <lang clojure>(cholesky [[25 15 -5] [15 18 0] [-5 0 11]])
- => [[ 5.0 0.0 0.0]
- [ 3.0 3.0 0.0]
- [-1.0 1.0 3.0]]
(cholesky [[18 22 54 42] [22 70 86 62] [54 86 174 134] [42 62 134 106]])
- => [[ 4.242640687119285 0.0 0.0 0.0 ]
- [ 5.185449728701349 6.565905201197403 0.0 0.0 ]
- [12.727922061357857 3.0460384954008553 1.6497422479090704 0.0 ]
- [ 9.899494936611667 1.624553864213788 1.8497110052313648 1.3926212476456026]]</lang>
Common Lisp
<lang lisp>;; Calculates the Cholesky decomposition matrix L
- for a positive-definite, symmetric nxn matrix A.
(defun chol (A)
(let* ((n (car (array-dimensions A)))
(L (make-array `(,n ,n) :initial-element 0)))
(do ((k 0 (incf k))) ((> k (- n 1)) nil)
;; First, calculate diagonal elements L_kk.
(setf (aref L k k)
(sqrt (- (aref A k k)
(do* ((j 0 (incf j))
(sum (expt (aref L k j) 2)
(incf sum (expt (aref L k j) 2))))
((> j (- k 1)) sum)))))
;; Then, all elements below a diagonal element, L_ik, i=k+1..n.
(do ((i (+ k 1) (incf i)))
((> i (- n 1)) nil)
(setf (aref L i k)
(/ (- (aref A i k)
(do* ((j 0 (incf j))
(sum (* (aref L i j) (aref L k j))
(incf sum (* (aref L i j) (aref L k j)))))
((> j (- k 1)) sum)))
(aref L k k)))))
;; Return the calculated matrix L. L))</lang>
<lang lisp>;; Example 1: (setf A (make-array '(3 3) :initial-contents '((25 15 -5) (15 18 0) (-5 0 11)))) (chol A)
- 2A((5.0 0 0)
(3.0 3.0 0) (-1.0 1.0 3.0))</lang>
<lang lisp>;; Example 2: (setf B (make-array '(4 4) :initial-contents '((18 22 54 42) (22 70 86 62) (54 86 174 134) (42 62 134 106)))) (chol B)
- 2A((4.2426405 0 0 0)
(5.18545 6.565905 0 0) (12.727922 3.0460374 1.6497375 0) (9.899495 1.6245536 1.849715 1.3926151))</lang>
<lang lisp>;; case of matrix stored as a list of lists (inner lists are rows of matrix)
- as above, returns the Cholesky decomposition matrix of a square positive-definite, symmetric matrix
(defun cholesky (m)
(let ((l (list (list (sqrt (caar m))))) x (j 0) i)
(dolist (cm (cdr m) (mapcar #'(lambda (x) (nconc x (make-list (- (length m) (length x)) :initial-element 0))) l))
(setq x (list (/ (car cm) (caar l))) i 0)
(dolist (cl (cdr l))
(setf (cdr (last x)) (list (/ (- (elt cm (incf i)) (*v x cl)) (car (last cl))))))
(setf (cdr (last l)) (list (nconc x (list (sqrt (- (elt cm (incf j)) (*v x x))))))))))
- where *v is the scalar product defined as
(defun *v (v1 v2) (reduce #'+ (mapcar #'* v1 v2)))</lang>
<lang lisp>;; example 1 CL-USER> (setf a '((25 15 -5) (15 18 0) (-5 0 11))) ((25 15 -5) (15 18 0) (-5 0 11)) CL-USER> (cholesky a) ((5 0 0) (3 3 0) (-1 1 3)) CL-USER> (format t "~{~{~5d~}~%~}" (cholesky a))
5 0 0 3 3 0 -1 1 3
NIL</lang>
<lang lisp>;; example 2 CL-USER> (setf a '((18 22 54 42) (22 70 86 62) (54 86 174 134) (42 62 134 106))) ((18 22 54 42) (22 70 86 62) (54 86 174 134) (42 62 134 106)) CL-USER> (cholesky a) ((4.2426405 0 0 0) (5.18545 6.565905 0 0) (12.727922 3.0460374 1.6497375 0) (9.899495 1.6245536 1.849715 1.3926151)) CL-USER> (format t "~{~{~10,5f~}~%~}" (cholesky a))
4.24264 0.00000 0.00000 0.00000 5.18545 6.56591 0.00000 0.00000 12.72792 3.04604 1.64974 0.00000 9.89950 1.62455 1.84971 1.39262
NIL</lang>
D
<lang d>import std.stdio, std.math, std.numeric;
T[][] cholesky(T)(in T[][] A) pure nothrow /*@safe*/ {
auto L = new T[][](A.length, A.length);
foreach (immutable r, row; L)
row[r + 1 .. $] = 0;
foreach (immutable i; 0 .. A.length)
foreach (immutable j; 0 .. i + 1) {
auto t = dotProduct(L[i][0 .. j], L[j][0 .. j]);
L[i][j] = (i == j) ? (A[i][i] - t) ^^ 0.5 :
(1.0 / L[j][j] * (A[i][j] - t));
}
return L;
}
void main() {
immutable double[][] m1 = [[25, 15, -5],
[15, 18, 0],
[-5, 0, 11]];
writefln("%(%(%2.0f %)\n%)\n", m1.cholesky);
immutable double[][] m2 = [[18, 22, 54, 42],
[22, 70, 86, 62],
[54, 86, 174, 134],
[42, 62, 134, 106]];
writefln("%(%(%2.3f %)\n%)", m2.cholesky);
}</lang>
- Output:
5 0 0 3 3 0 -1 1 3 4.243 0.000 0.000 0.000 5.185 6.566 0.000 0.000 12.728 3.046 1.650 0.000 9.899 1.625 1.850 1.393
DWScript
<lang delphi>function Cholesky(a : array of Float) : array of Float; var
i, j, k, n : Integer; s : Float;
begin
n:=Round(Sqrt(a.Length));
Result:=new Float[n*n];
for i:=0 to n-1 do begin
for j:=0 to i do begin
s:=0 ;
for k:=0 to j-1 do
s+=Result[i*n+k] * Result[j*n+k];
if i=j then
Result[i*n+j]:=Sqrt(a[i*n+i]-s)
else Result[i*n+j]:=1/Result[j*n+j]*(a[i*n+j]-s);
end;
end;
end;
procedure ShowMatrix(a : array of Float); var
i, j, n : Integer;
begin
n:=Round(Sqrt(a.Length));
for i:=0 to n-1 do begin
for j:=0 to n-1 do
Print(Format('%2.5f ', [a[i*n+j]]));
PrintLn();
end;
end;
var m1 := new Float[9]; m1 := [ 25.0, 15.0, -5.0,
15.0, 18.0, 0.0,
-5.0, 0.0, 11.0 ];
var c1 := Cholesky(m1); ShowMatrix(c1);
PrintLn();
var m2 : array of Float := [ 18.0, 22.0, 54.0, 42.0,
22.0, 70.0, 86.0, 62.0,
54.0, 86.0, 174.0, 134.0,
42.0, 62.0, 134.0, 106.0 ];
var c2 := Cholesky(m2); ShowMatrix(c2);</lang>
Fantom
<lang fantom>**
- Cholesky decomposition
class Main {
// create an array of Floats, initialised to 0.0
Float[][] makeArray (Int i, Int j)
{
Float[][] result := [,]
i.times { result.add ([,]) }
i.times |Int x|
{
j.times
{
result[x].add(0f)
}
}
return result
}
// perform the Cholesky decomposition
Float[][] cholesky (Float[][] array)
{
m := array.size
Float[][] l := makeArray (m, m)
m.times |Int i|
{
(i+1).times |Int k|
{
Float sum := (0..<k).toList.reduce (0f) |Float a, Int j -> Float|
{
a + l[i][j] * l[k][j]
}
if (i == k)
l[i][k] = (array[i][i]-sum).sqrt
else
l[i][k] = (1.0f / l[k][k]) * (array[i][k] - sum)
}
}
return l
}
Void runTest (Float[][] array)
{
echo (array)
echo (cholesky (array))
}
Void main ()
{
runTest ([[25f,15f,-5f],[15f,18f,0f],[-5f,0f,11f]])
runTest ([[18f,22f,54f,42f],[22f,70f,86f,62f],[54f,86f,174f,134f],[42f,62f,134f,106f]])
}
}</lang>
- Output:
[[25.0, 15.0, -5.0], [15.0, 18.0, 0.0], [-5.0, 0.0, 11.0]] [[5.0, 0.0, 0.0], [3.0, 3.0, 0.0], [-1.0, 1.0, 3.0]] [[18.0, 22.0, 54.0, 42.0], [22.0, 70.0, 86.0, 62.0], [54.0, 86.0, 174.0, 134.0], [42.0, 62.0, 134.0, 106.0]] [[4.242640687119285, 0.0, 0.0, 0.0], [5.185449728701349, 6.565905201197403, 0.0, 0.0], [12.727922061357857, 3.0460384954008553, 1.6497422479090704, 0.0], [9.899494936611667, 1.624553864213788, 1.8497110052313648, 1.3926212476456026]]
Fortran
<lang Fortran>Program Cholesky_decomp ! *************************************************! ! LBH @ ULPGC 06/03/2014 ! Compute the Cholesky decomposition for a matrix A ! after the attached ! http://rosettacode.org/wiki/Cholesky_decomposition ! note that the matrix A is complex since there might ! be values, where the sqrt has complex solutions. ! Here, only the real values are taken into account !*************************************************! implicit none
INTEGER, PARAMETER :: m=3 !rows INTEGER, PARAMETER :: n=3 !cols COMPLEX, DIMENSION(m,n) :: A REAL, DIMENSION(m,n) :: L REAL :: sum1, sum2 INTEGER i,j,k
! Assign values to the matrix A(1,:)=(/ 25, 15, -5 /) A(2,:)=(/ 15, 18, 0 /) A(3,:)=(/ -5, 0, 11 /) ! !!!!!!!!!!!another example!!!!!!! ! A(1,:) = (/ 18, 22, 54, 42 /) ! A(2,:) = (/ 22, 70, 86, 62 /) ! A(3,:) = (/ 54, 86, 174, 134 /) ! A(4,:) = (/ 42, 62, 134, 106 /)
! Initialize values L(1,1)=real(sqrt(A(1,1))) L(2,1)=A(2,1)/L(1,1) L(2,2)=real(sqrt(A(2,2)-L(2,1)*L(2,1))) L(3,1)=A(3,1)/L(1,1) ! for greater order than m,n=3 add initial row value ! for instance if m,n=4 then add the following line ! L(4,1)=A(4,1)/L(1,1)
do i=1,n
do k=1,i
sum1=0
sum2=0
do j=1,k-1
if (i==k) then
sum1=sum1+(L(k,j)*L(k,j))
L(k,k)=real(sqrt(A(k,k)-sum1))
elseif (i > k) then
sum2=sum2+(L(i,j)*L(k,j))
L(i,k)=(1/L(k,k))*(A(i,k)-sum2)
else
L(i,k)=0
end if
end do
end do
end do
! write output do i=1,m
print "(3(1X,F6.1))",L(i,:)
end do
End program Cholesky_decomp</lang >
- Output:
5.0 0.0 0.0 3.0 3.0 0.0 -1.0 1.0 3.0
FreeBASIC
<lang freebasic>' version 18-01-2017 ' compile with: fbc -s console
Sub Cholesky_decomp(array() As Double)
Dim As Integer i, j, k Dim As Double s, l(UBound(array), UBound(array, 2))
For i = 0 To UBound(array)
For j = 0 To i
s = 0
For k = 0 To j -1
s += l(i, k) * l(j, k)
Next
If i = j Then
l(i, j) = Sqr(array(i, i) - s)
Else
l(i, j) = (array(i, j) - s) / l(j, j)
End If
Next
Next
For i = 0 To UBound(array)
For j = 0 To UBound(array, 2)
Swap array(i, j), l(i, j)
Next
Next
End Sub
Sub Print_(array() As Double)
Dim As Integer i, j
For i = 0 To UBound(array)
For j = 0 To UBound(array, 2)
Print Using "###.#####";array(i,j);
Next
Print
Next
End Sub
' ------=< MAIN >=------
Dim m1(2,2) As Double => {{25, 15, -5}, _
{15, 18, 0}, _
{-5, 0, 11}}
Dim m2(3, 3) As Double => {{18, 22, 54, 42}, _
{22, 70, 86, 62}, _
{54, 86, 174, 134}, _
{42, 62, 134, 106}}
Cholesky_decomp(m1()) Print_(m1())
Print Cholesky_decomp(m2()) Print_(m2())
' empty keyboard buffer While Inkey <> "" : Wend Print : Print "hit any key to end program" Sleep End</lang>
- Output:
5.00000 0.00000 0.00000 3.00000 3.00000 0.00000 -1.00000 1.00000 3.00000 4.24264 0.00000 0.00000 0.00000 5.18545 6.56591 0.00000 0.00000 12.72792 3.04604 1.64974 0.00000 9.89949 1.62455 1.84971 1.39262
Go
Real
This version works with real matrices, like most other solutions on the page. The representation is packed, however, storing only the lower triange of the input symetric matrix and the output lower matrix. The decomposition algorithm computes rows in order from top to bottom but is a little different thatn Cholesky–Banachiewicz. <lang go>package main
import (
"fmt" "math"
)
// symmetric and lower use a packed representation that stores only // the lower triangle.
type symmetric struct {
order int ele []float64
}
type lower struct {
order int ele []float64
}
// symmetric.print prints a square matrix from the packed representation, // printing the upper triange as a transpose of the lower. func (s *symmetric) print() {
const eleFmt = "%10.5f "
row, diag := 1, 0
for i, e := range s.ele {
fmt.Printf(eleFmt, e)
if i == diag {
for j, col := diag+row, row; col < s.order; j += col {
fmt.Printf(eleFmt, s.ele[j])
col++
}
fmt.Println()
row++
diag += row
}
}
}
// lower.print prints a square matrix from the packed representation, // printing the upper triangle as all zeros. func (l *lower) print() {
const eleFmt = "%10.5f "
row, diag := 1, 0
for i, e := range l.ele {
fmt.Printf(eleFmt, e)
if i == diag {
for j := row; j < l.order; j++ {
fmt.Printf(eleFmt, 0.)
}
fmt.Println()
row++
diag += row
}
}
}
// choleskyLower returns the cholesky decomposition of a symmetric real // matrix. The matrix must be positive definite but this is not checked. func (a *symmetric) choleskyLower() *lower {
l := &lower{a.order, make([]float64, len(a.ele))}
row, col := 1, 1
dr := 0 // index of diagonal element at end of row
dc := 0 // index of diagonal element at top of column
for i, e := range a.ele {
if i < dr {
d := (e - l.ele[i]) / l.ele[dc]
l.ele[i] = d
ci, cx := col, dc
for j := i + 1; j <= dr; j++ {
cx += ci
ci++
l.ele[j] += d * l.ele[cx]
}
col++
dc += col
} else {
l.ele[i] = math.Sqrt(e - l.ele[i])
row++
dr += row
col = 1
dc = 0
}
}
return l
}
func main() {
demo(&symmetric{3, []float64{
25,
15, 18,
-5, 0, 11}})
demo(&symmetric{4, []float64{
18,
22, 70,
54, 86, 174,
42, 62, 134, 106}})
}
func demo(a *symmetric) {
fmt.Println("A:")
a.print()
fmt.Println("L:")
a.choleskyLower().print()
}</lang>
- Output:
A: 25.00000 15.00000 -5.00000 15.00000 18.00000 0.00000 -5.00000 0.00000 11.00000 L: 5.00000 0.00000 0.00000 3.00000 3.00000 0.00000 -1.00000 1.00000 3.00000 A: 18.00000 22.00000 54.00000 42.00000 22.00000 70.00000 86.00000 62.00000 54.00000 86.00000 174.00000 134.00000 42.00000 62.00000 134.00000 106.00000 L: 4.24264 0.00000 0.00000 0.00000 5.18545 6.56591 0.00000 0.00000 12.72792 3.04604 1.64974 0.00000 9.89949 1.62455 1.84971 1.39262
Hermitian
This version handles complex Hermitian matricies as described on the WP page. The matrix representation is flat, and storage is allocated for all elements, not just the lower triangles. The decomposition algorithm is Cholesky–Banachiewicz. <lang go>package main
import (
"fmt" "math/cmplx"
)
type matrix struct {
stride int ele []complex128
}
func like(a *matrix) *matrix {
return &matrix{a.stride, make([]complex128, len(a.ele))}
}
func (m *matrix) print(heading string) {
if heading > "" {
fmt.Print("\n", heading, "\n")
}
for e := 0; e < len(m.ele); e += m.stride {
fmt.Printf("%7.2f ", m.ele[e:e+m.stride])
fmt.Println()
}
}
func (a *matrix) choleskyDecomp() *matrix {
l := like(a)
// Cholesky-Banachiewicz algorithm
for r, rxc0 := 0, 0; r < a.stride; r++ {
// calculate elements along row, up to diagonal
x := rxc0
for c, cxc0 := 0, 0; c < r; c++ {
sum := a.ele[x]
for k := 0; k < c; k++ {
sum -= l.ele[rxc0+k] * cmplx.Conj(l.ele[cxc0+k])
}
l.ele[x] = sum / l.ele[cxc0+c]
x++
cxc0 += a.stride
}
// calcualate diagonal element
sum := a.ele[x]
for k := 0; k < r; k++ {
sum -= l.ele[rxc0+k] * cmplx.Conj(l.ele[rxc0+k])
}
l.ele[x] = cmplx.Sqrt(sum)
rxc0 += a.stride
}
return l
}
func main() {
demo("A:", &matrix{3, []complex128{
25, 15, -5,
15, 18, 0,
-5, 0, 11,
}})
demo("A:", &matrix{4, []complex128{
18, 22, 54, 42,
22, 70, 86, 62,
54, 86, 174, 134,
42, 62, 134, 106,
}})
// one more example, from the Numpy manual, with a non-real
demo("A:", &matrix{2, []complex128{
1, -2i,
2i, 5,
}})
}
func demo(heading string, a *matrix) {
a.print(heading)
a.choleskyDecomp().print("Cholesky factor L:")
}</lang>
- Output:
A: [( 25.00 +0.00i) ( 15.00 +0.00i) ( -5.00 +0.00i)] [( 15.00 +0.00i) ( 18.00 +0.00i) ( 0.00 +0.00i)] [( -5.00 +0.00i) ( 0.00 +0.00i) ( 11.00 +0.00i)] Cholesky factor L: [( 5.00 +0.00i) ( 0.00 +0.00i) ( 0.00 +0.00i)] [( 3.00 +0.00i) ( 3.00 +0.00i) ( 0.00 +0.00i)] [( -1.00 +0.00i) ( 1.00 +0.00i) ( 3.00 +0.00i)] A: [( 18.00 +0.00i) ( 22.00 +0.00i) ( 54.00 +0.00i) ( 42.00 +0.00i)] [( 22.00 +0.00i) ( 70.00 +0.00i) ( 86.00 +0.00i) ( 62.00 +0.00i)] [( 54.00 +0.00i) ( 86.00 +0.00i) ( 174.00 +0.00i) ( 134.00 +0.00i)] [( 42.00 +0.00i) ( 62.00 +0.00i) ( 134.00 +0.00i) ( 106.00 +0.00i)] Cholesky factor L: [( 4.24 +0.00i) ( 0.00 +0.00i) ( 0.00 +0.00i) ( 0.00 +0.00i)] [( 5.19 +0.00i) ( 6.57 +0.00i) ( 0.00 +0.00i) ( 0.00 +0.00i)] [( 12.73 +0.00i) ( 3.05 +0.00i) ( 1.65 +0.00i) ( 0.00 +0.00i)] [( 9.90 +0.00i) ( 1.62 +0.00i) ( 1.85 +0.00i) ( 1.39 +0.00i)] A: [( 1.00 +0.00i) ( 0.00 -2.00i)] [( 0.00 +2.00i) ( 5.00 +0.00i)] Cholesky factor L: [( 1.00 +0.00i) ( 0.00 +0.00i)] [( 0.00 +2.00i) ( 1.00 +0.00i)]
Library gonum/mat
<lang go>package main
import (
"fmt"
"gonum.org/v1/gonum/mat"
)
func cholesky(order int, elements []float64) fmt.Formatter {
var c mat.Cholesky c.Factorize(mat.NewSymDense(order, elements)) return mat.Formatted(c.LTo(nil))
}
func main() {
fmt.Println(cholesky(3, []float64{
25, 15, -5,
15, 18, 0,
-5, 0, 11,
}))
fmt.Printf("\n%.5f\n", cholesky(4, []float64{
18, 22, 54, 42,
22, 70, 86, 62,
54, 86, 174, 134,
42, 62, 134, 106,
}))
}</lang>
- Output:
⎡ 5 0 0⎤ ⎢ 3 3 0⎥ ⎣-1 1 3⎦ ⎡ 4.24264 0.00000 0.00000 0.00000⎤ ⎢ 5.18545 6.56591 0.00000 0.00000⎥ ⎢12.72792 3.04604 1.64974 0.00000⎥ ⎣ 9.89949 1.62455 1.84971 1.39262⎦
Library go.matrix
<lang go>package main
import (
"fmt"
mat "github.com/skelterjohn/go.matrix"
)
func main() {
demo(mat.MakeDenseMatrix([]float64{
25, 15, -5,
15, 18, 0,
-5, 0, 11,
}, 3, 3))
demo(mat.MakeDenseMatrix([]float64{
18, 22, 54, 42,
22, 70, 86, 62,
54, 86, 174, 134,
42, 62, 134, 106,
}, 4, 4))
}
func demo(m *mat.DenseMatrix) {
fmt.Println("A:")
fmt.Println(m)
l, err := m.Cholesky()
if err != nil {
fmt.Println(err)
return
}
fmt.Println("L:")
fmt.Println(l)
}</lang> Output:
A:
{25, 15, -5,
15, 18, 0,
-5, 0, 11}
L:
{ 5, 0, 0,
3, 3, 0,
-1, 1, 3}
A:
{ 18, 22, 54, 42,
22, 70, 86, 62,
54, 86, 174, 134,
42, 62, 134, 106}
L:
{ 4.242641, 0, 0, 0,
5.18545, 6.565905, 0, 0,
12.727922, 3.046038, 1.649742, 0,
9.899495, 1.624554, 1.849711, 1.392621}
Haskell
We use the Cholesky–Banachiewicz algorithm described in the Wikipedia article.
For more serious numerical analysis there is a Cholesky decomposition function in the hmatrix package.
The Cholesky module: <lang haskell>module Cholesky (Arr, cholesky) where
import Data.Array.IArray import Data.Array.MArray import Data.Array.Unboxed import Data.Array.ST
type Idx = (Int,Int) type Arr = UArray Idx Double
-- Return the (i,j) element of the lower triangular matrix. (We assume the -- lower array bound is (0,0).) get :: Arr -> Arr -> Idx -> Double get a l (i,j) | i == j = sqrt $ a!(j,j) - dot
| i > j = (a!(i,j) - dot) / l!(j,j)
| otherwise = 0
where dot = sum [l!(i,k) * l!(j,k) | k <- [0..j-1]]
-- Return the lower triangular matrix of a Cholesky decomposition. We assume -- the input is a real, symmetric, positive-definite matrix, with lower array -- bounds of (0,0). cholesky :: Arr -> Arr cholesky a = let n = maxBnd a
in runSTUArray $ do
l <- thaw a
mapM_ (update a l) [(i,j) | i <- [0..n], j <- [0..n]]
return l
where maxBnd = fst . snd . bounds
update a l i = unsafeFreeze l >>= \l' -> writeArray l i (get a l' i)</lang>
The main module: <lang haskell>import Data.Array.IArray import Data.List import Cholesky
fm _ [] = "" fm _ [x] = fst x fm width ((a,b):xs) = a ++ (take (width - b) $ cycle " ") ++ (fm width xs)
fmt width row (xs,[]) = fm width xs fmt width row (xs,ys) = fm width xs ++ "\n" ++ fmt width row (splitAt row ys)
showMatrice row xs = ys where
vs = map (\s -> let sh = show s in (sh,length sh)) xs width = (maximum $ snd $ unzip vs) + 1 ys = fmt width row (splitAt row vs)
ex1, ex2 :: Arr ex1 = listArray ((0,0),(2,2)) [25, 15, -5,
15, 18, 0,
-5, 0, 11]
ex2 = listArray ((0,0),(3,3)) [18, 22, 54, 42,
22, 70, 86, 62,
54, 86, 174, 134,
42, 62, 134, 106]
main :: IO () main = do
putStrLn $ showMatrice 3 $ elems $ cholesky ex1 putStrLn $ showMatrice 4 $ elems $ cholesky ex2</lang>
output:
5.0 0.0 0.0 3.0 3.0 0.0 -1.0 1.0 3.0 4.242640687119285 0.0 0.0 0.0 5.185449728701349 6.565905201197403 0.0 0.0 12.727922061357857 3.0460384954008553 1.6497422479090704 0.0 9.899494936611665 1.6245538642137891 1.849711005231382 1.3926212476455924
Icon and Unicon
<lang Icon>procedure cholesky (array)
result := make_square_array (*array)
every (i := 1 to *array) do {
every (k := 1 to i) do {
sum := 0
every (j := 1 to (k-1)) do {
sum +:= result[i][j] * result[k][j]
}
if (i = k)
then result[i][k] := sqrt(array[i][i] - sum)
else result[i][k] := 1.0 / result[k][k] * (array[i][k] - sum)
}
}
return result
end
procedure make_square_array (n)
result := [] every (1 to n) do push (result, list(n, 0)) return result
end
procedure print_array (array)
every (row := !array) do {
every writes (!row || " ")
write ()
}
end
procedure do_cholesky (array)
write ("Input:")
print_array (array)
result := cholesky (array)
write ("Result:")
print_array (result)
end
procedure main ()
do_cholesky ([[25,15,-5],[15,18,0],[-5,0,11]]) do_cholesky ([[18,22,54,42],[22,70,86,62],[54,86,174,134],[42,62,134,106]])
end</lang>
- Output:
Input: 25 15 -5 15 18 0 -5 0 11 Result: 5.0 0 0 3.0 3.0 0 -1.0 1.0 3.0 Input: 18 22 54 42 22 70 86 62 54 86 174 134 42 62 134 106 Result: 4.242640687 0 0 0 5.185449729 6.565905201 0 0 12.72792206 3.046038495 1.649742248 0 9.899494937 1.624553864 1.849711005 1.392621248
Idris
works with Idris 0.10
Solution: <lang Idris>module Main
import Data.Vect
Matrix : Nat -> Nat -> Type -> Type Matrix m n t = Vect m (Vect n t)
zeros : (m : Nat) -> (n : Nat) -> Matrix m n Double
zeros m n = replicate m (replicate n 0.0)
indexM : (Fin m, Fin n) -> Matrix m n t -> t
indexM (i, j) a = index j (index i a)
replaceAtM : (Fin m, Fin n) -> t -> Matrix m n t -> Matrix m n t
replaceAtM (i, j) e a = replaceAt i (replaceAt j e (index i a)) a
get : Matrix m m Double -> Matrix m m Double -> (Fin m, Fin m) -> Double
get a l (i, j) {m} = if i == j then sqrt $ indexM (j, j) a - dot
else if i > j then (indexM (i, j) a - dot) / indexM (j, j) l
else 0.0
where
-- Obtain indicies 0 to j -1
ks : List (Fin m)
ks = case (findIndices (\_ => True) a) of
[] => []
(x::xs) => init (x::xs)
dot : Double
dot = sum [(indexM (i, k) l) * (indexM (j, k) l) | k <- ks]
updateL : Matrix m m Double -> Matrix m m Double -> (Fin m, Fin m) -> Matrix m m Double
updateL a l idx = replaceAtM idx (get a l idx) l
cholesky : Matrix m m Double -> Matrix m m Double
cholesky a {m} =
foldl (\l',i =>
foldl (\l,j => updateL a l (i, j)) l' (js i))
l is
where l = zeros m m
is : List (Fin m)
is = findIndices (\_ => True) a
js : Fin m -> List (Fin m)
js n = filter (<= n) is
ex1 : Matrix 3 3 Double
ex1 = cholesky [[25.0, 15.0, -5.0], [15.0, 18.0, 0.0], [-5.0, 0.0, 11.0]]
ex2 : Matrix 4 4 Double ex2 = cholesky [[18.0, 22.0, 54.0, 42.0], [22.0, 70.0, 86.0, 62.0],
[54.0, 86.0, 174.0, 134.0], [42.0, 62.0, 134.0, 106.0]]
main : IO () main = do
print ex1 putStrLn "\n" print ex2 putStrLn "\n"
</lang>
- Output:
[[5, 0, 0], [3, 3, 0], [-1, 1, 3]] [[4.242640687119285, 0, 0, 0], [5.185449728701349, 6.565905201197403, 0, 0], [12.72792206135786, 3.046038495400855, 1.64974224790907, 0], [9.899494936611665, 1.624553864213789, 1.849711005231382, 1.392621247645587]]
J
Solution: <lang j>mp=: +/ . * NB. matrix product h =: +@|: NB. conjugate transpose
cholesky=: 3 : 0
n=. #A=. y
if. 1>:n do.
assert. (A=|A)>0=A NB. check for positive definite
%:A
else.
'X Y t Z'=. , (;~n$(>.-:n){.1) <;.1 A
L0=. cholesky X
L1=. cholesky Z-(T=.(h Y) mp %.X) mp Y
L0,(T mp L0),.L1
end.
)</lang> See Cholesky Decomposition essay on the J Wiki.
- Examples:
<lang j> eg1=: 25 15 _5 , 15 18 0 ,: _5 0 11
eg2=: 18 22 54 42 , 22 70 86 62 , 54 86 174 134 ,: 42 62 134 106 cholesky eg1 5 0 0 3 3 0
_1 1 3
cholesky eg2
4.24264 0 0 0 5.18545 6.56591 0 0 12.7279 3.04604 1.64974 0 9.89949 1.62455 1.84971 1.39262</lang> Using `math/lapack` addon <lang j> load 'math/lapack'
load 'math/lapack/potrf' potrf_jlapack_ eg1 5 0 0 3 3 0
_1 1 3
potrf_jlapack_ eg2
4.24264 0 0 0 5.18545 6.56591 0 0 12.7279 3.04604 1.64974 0 9.89949 1.62455 1.84971 1.39262</lang>
Java
<lang java5>import java.util.Arrays;
public class Cholesky { public static double[][] chol(double[][] a){ int m = a.length; double[][] l = new double[m][m]; //automatically initialzed to 0's for(int i = 0; i< m;i++){ for(int k = 0; k < (i+1); k++){ double sum = 0; for(int j = 0; j < k; j++){ sum += l[i][j] * l[k][j]; } l[i][k] = (i == k) ? Math.sqrt(a[i][i] - sum) : (1.0 / l[k][k] * (a[i][k] - sum)); } } return l; }
public static void main(String[] args){ double[][] test1 = {{25, 15, -5}, {15, 18, 0}, {-5, 0, 11}}; System.out.println(Arrays.deepToString(chol(test1))); double[][] test2 = {{18, 22, 54, 42}, {22, 70, 86, 62}, {54, 86, 174, 134}, {42, 62, 134, 106}}; System.out.println(Arrays.deepToString(chol(test2))); } }</lang>
- Output:
[[5.0, 0.0, 0.0], [3.0, 3.0, 0.0], [-1.0, 1.0, 3.0]] [[4.242640687119285, 0.0, 0.0, 0.0], [5.185449728701349, 6.565905201197403, 0.0, 0.0], [12.727922061357857, 3.0460384954008553, 1.6497422479090704, 0.0], [9.899494936611667, 1.624553864213788, 1.8497110052313648, 1.3926212476456026]]
jq
Infrastructure: <lang jq># Create an m x n matrix
def matrix(m; n; init):
if m == 0 then []
elif m == 1 then [range(0; n)] | map(init)
elif m > 0 then
matrix(1; n; init) as $row
| [range(0; m)] | map( $row )
else error("matrix\(m);_;_) invalid")
end ;
- Print a matrix neatly, each cell ideally occupying n spaces,
- but without truncation
def neatly(n):
def right: tostring | ( " " * (n-length) + .);
. as $in
| length as $length
| reduce range (0; $length) as $i
(""; . + reduce range(0; $length) as $j
(""; "\(.) \($in[$i][$j] | right )" ) + "\n" ) ;
def is_square:
type == "array" and (map(type == "array") | all) and length == 0 or ( (.[0]|length) as $l | map(length == $l) | all) ;
- This implementation of is_symmetric/0 uses a helper function that circumvents
- limitations of jq 1.4:
def is_symmetric:
# [matrix, i,j, len]
def test:
if .[1] > .[3] then true
elif .[1] == .[2] then [ .[0], .[1] + 1, 0, .[3]] | test
elif .[0][.[1]][.[2]] == .[0][.[2]][.[1]]
then [ .[0], .[1], .[2]+1, .[3]] | test
else false
end;
if is_square|not then false
else [ ., 0, 0, length ] | test
end ;
</lang>Cholesky Decomposition:<lang jq>def cholesky_factor:
if is_symmetric then
length as $length
| . as $self
| reduce range(0; $length) as $k
( matrix(length; length; 0); # the matrix that will hold the answer
reduce range(0; $length) as $i
(.;
if $i == $k
then (. as $lower
| reduce range(0; $k) as $j
(0; . + ($lower[$k][$j] | .*.) )) as $sum
| .[$k][$k] = (($self[$k][$k] - $sum) | sqrt)
elif $i > $k
then (. as $lower
| reduce range(0; $k) as $j
(0; . + $lower[$i][$j] * $lower[$k][$j])) as $sum
| .[$i][$k] = (($self[$k][$i] - $sum) / .[$k][$k] )
else .
end ))
else error( "cholesky_factor: matrix is not symmetric" )
end ;</lang>
Task 1:
[[25,15,-5],[15,18,0],[-5,0,11]] | cholesky_factor
- Output:
[[5,0,0],[3,3,0],[-1,1,3]]
Task 2:
[[18, 22, 54, 42], [22, 70, 86, 62], [54, 86, 174, 134], [42, 62, 134, 106]] | cholesky_factor | neatly(20)
- Output:
<lang jq> 4.242640687119285 0 0 0
5.185449728701349 6.565905201197403 0 0 12.727922061357857 3.0460384954008553 1.6497422479090704 0 9.899494936611665 1.6245538642137891 1.849711005231382 1.3926212476455924</lang>
Julia
Julia's strong linear algebra support includes Cholesky decomposition. <lang Julia> a = [25 15 5; 15 18 0; -5 0 11] b = [18 22 54 22; 22 70 86 62; 54 86 174 134; 42 62 134 106]
println(a, "\n => \n", chol(a, :L)) println(b, "\n => \n", chol(b, :L)) </lang>
- Output:
[25 15 5 15 18 0 -5 0 11] => [5.0 0.0 0.0 3.0 3.0 0.0 -1.0 1.0 3.0] [18 22 54 22 22 70 86 62 54 86 174 134 42 62 134 106] => [4.242640687119285 0.0 0.0 0.0 5.185449728701349 6.565905201197403 0.0 0.0 12.727922061357857 3.0460384954008553 1.6497422479090704 0.0 9.899494936611667 1.624553864213788 1.8497110052313648 1.3926212476456026]
Kotlin
<lang scala>// version 1.0.6
fun cholesky(a: DoubleArray): DoubleArray {
val n = Math.sqrt(a.size.toDouble()).toInt()
val l = DoubleArray(a.size)
var s: Double
for (i in 0 until n)
for (j in 0 .. i) {
s = 0.0
for (k in 0 until j) s += l[i * n + k] * l[j * n + k]
l[i * n + j] = when {
(i == j) -> Math.sqrt(a[i * n + i] - s)
else -> 1.0 / l[j * n + j] * (a[i * n + j] - s)
}
}
return l
}
fun showMatrix(a: DoubleArray) {
val n = Math.sqrt(a.size.toDouble()).toInt()
for (i in 0 until n) {
for (j in 0 until n) print("%8.5f ".format(a[i * n + j]))
println()
}
}
fun main(args: Array<String>) {
val m1 = doubleArrayOf(25.0, 15.0, -5.0,
15.0, 18.0, 0.0,
-5.0, 0.0, 11.0)
val c1 = cholesky(m1)
showMatrix(c1)
println()
val m2 = doubleArrayOf(18.0, 22.0, 54.0, 42.0,
22.0, 70.0, 86.0, 62.0,
54.0, 86.0, 174.0, 134.0,
42.0, 62.0, 134.0, 106.0)
val c2 = cholesky(m2)
showMatrix(c2)
}</lang>
- Output:
5.00000 0.00000 0.00000 3.00000 3.00000 0.00000 -1.00000 1.00000 3.00000 4.24264 0.00000 0.00000 0.00000 5.18545 6.56591 0.00000 0.00000 12.72792 3.04604 1.64974 0.00000 9.89949 1.62455 1.84971 1.39262
Maple
The Cholesky decomposition is obtained by passing the `method = Cholesky' option to the LUDecomposition procedure in the LinearAlgebra pacakge. This is illustrated below for the two requested examples. The first is computed exactly; the second is also, but the subsequent application of `evalf' to the result produces a matrix with floating point entries which can be compared with the expected output in the problem statement. <lang Maple>> A := << 25, 15, -5; 15, 18, 0; -5, 0, 11 >>;
[25 15 -5]
[ ]
A := [15 18 0]
[ ]
[-5 0 11]
> B := << 18, 22, 54, 42; 22, 70, 86, 62; 54, 86, 174, 134; 42, 62, 134, 106>>;
[18 22 54 42]
[ ]
[22 70 86 62]
B := [ ]
[54 86 174 134]
[ ]
[42 62 134 106]
> use LinearAlgebra in > LUDecomposition( A, method = Cholesky ); > LUDecomposition( B, method = Cholesky ); > evalf( % ); > end use;
[ 5 0 0]
[ ]
[ 3 3 0]
[ ]
[-1 1 3]
[ 1/2 ]
[3 2 0 0 0 ]
[ ]
[ 1/2 1/2 ]
[11 2 2 97 ]
[------- ------- 0 0 ]
[ 3 3 ]
[ ]
[ 1/2 1/2 ]
[ 1/2 30 97 2 6402 ]
[9 2 -------- --------- 0 ]
[ 97 97 ]
[ ]
[ 1/2 1/2 1/2]
[ 1/2 16 97 74 6402 8 33 ]
[7 2 -------- ---------- -------]
[ 97 3201 33 ]
[4.242640686 0. 0. 0. ]
[ ]
[5.185449728 6.565905202 0. 0. ]
[ ]
[12.72792206 3.046038495 1.649742248 0. ]
[ ]
[9.899494934 1.624553864 1.849711006 1.392621248]</lang>
Mathematica / Wolfram Language
<lang Mathematica>CholeskyDecomposition[{{25, 15, -5}, {15, 18, 0}, {-5, 0, 11}}]</lang> Without the use of built-in functions, making use of memoization: <lang Mathematica>chol[A_] :=
Module[{L},
L[k_, k_] := L[k, k] = Sqrt[Ak, k - Sum[L[k, j]^2, {j, 1, k-1}]];
L[i_, k_] := L[i, k] = L[k, k]^-1 (Ai, k - Sum[L[i, j] L[k, j], {j, 1, k-1}]);
PadRight[Table[L[i, j], {i, Length[A]}, {j, i}]]
]</lang>
MATLAB / Octave
The cholesky decomposition chol() is an internal function <lang Matlab> A = [
25 15 -5 15 18 0 -5 0 11 ];
B = [ 18 22 54 42 22 70 86 62 54 86 174 134 42 62 134 106 ];
[L] = chol(A,'lower') [L] = chol(B,'lower')
</lang>
- Output:
> [L] = chol(A,'lower')
L =
5 0 0
3 3 0
-1 1 3
> [L] = chol(B,'lower')
L =
4.24264 0.00000 0.00000 0.00000
5.18545 6.56591 0.00000 0.00000
12.72792 3.04604 1.64974 0.00000
9.89949 1.62455 1.84971 1.39262
Maxima
<lang maxima>/* Cholesky decomposition is built-in */
a: hilbert_matrix(4)$
b: cholesky(a); /* matrix([1, 0, 0, 0 ],
[1/2, 1/(2*sqrt(3)), 0, 0 ],
[1/3, 1/(2*sqrt(3)), 1/(6*sqrt(5)), 0 ],
[1/4, 3^(3/2)/20, 1/(4*sqrt(5)), 1/(20*sqrt(7))]) */
b . transpose(b) - a; matrix([0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0])</lang>
Nim
<lang nim>import math, strutils
proc cholesky[T](a: T): T =
for i in 0 .. < a[0].len:
for j in 0 .. i:
var s = 0.0
for k in 0 .. < j:
s += result[i][k] * result[j][k]
result[i][j] = if i == j: sqrt(a[i][i]-s)
else: (1.0 / result[j][j] * (a[i][j] - s))
proc `$`(a): string =
result = ""
for b in a:
for c in b:
result.add c.formatFloat(ffDecimal, 5) & " "
result.add "\n"
let m1 = [[25.0, 15.0, -5.0],
[15.0, 18.0, 0.0],
[-5.0, 0.0, 11.0]]
echo cholesky(m1)
let m2 = [[18.0, 22.0, 54.0, 42.0],
[22.0, 70.0, 86.0, 62.0],
[54.0, 86.0, 174.0, 134.0],
[42.0, 62.0, 134.0, 106.0]]
echo cholesky(m2)</lang> Output:
5.00000 0.00000 0.00000 3.00000 3.00000 0.00000 -1.00000 1.00000 3.00000 4.24264 0.00000 0.00000 0.00000 5.18545 6.56591 0.00000 0.00000 12.72792 3.04604 1.64974 0.00000 9.89949 1.62455 1.84971 1.39262
Objeck
<lang objeck> class Cholesky {
function : Main(args : String[]) ~ Nil {
n := 3;
m1 := [25.0, 15.0, -5.0, 15.0, 18.0, 0.0, -5.0, 0.0, 11.0];
c1 := Cholesky(m1, n);
ShowMatrix(c1, n);
IO.Console->PrintLine();
n := 4;
m2 := [18.0, 22.0, 54.0, 42.0, 22.0, 70.0, 86.0, 62.0,
54.0, 86.0, 174.0, 134.0, 42.0, 62.0, 134.0, 106.0];
c2 := Cholesky(m2, n);
ShowMatrix(c2, n);
}
function : ShowMatrix(A : Float[], n : Int) ~ Nil {
for (i := 0; i < n; i+=1;) {
for (j := 0; j < n; j+=1;) {
IO.Console->Print(A[i * n + j])->Print('\t');
};
IO.Console->PrintLine();
};
}
function : Cholesky(A : Float[], n : Int) ~ Float[] {
L := Float->New[n * n];
for (i := 0; i < n; i+=1;) {
for (j := 0; j < (i+1); j+=1;) {
s := 0.0;
for (k := 0; k < j; k+=1;) {
s += L[i * n + k] * L[j * n + k];
};
L[i * n + j] := (i = j) ?
(A[i * n + i] - s)->SquareRoot() :
(1.0 / L[j * n + j] * (A[i * n + j] - s));
};
};
return L;
}
} </lang>
5 0 0 3 3 0 -1 1 3 4.24264069 0 0 0 5.18544973 6.5659052 0 0 12.7279221 3.0460385 1.64974225 0 9.89949494 1.62455386 1.84971101 1.39262125
OCaml
<lang OCaml>let cholesky inp =
let n = Array.length inp in
let res = Array.make_matrix n n 0.0 in
let factor i k =
let rec sum j =
if j = k then 0.0 else
res.(i).(j) *. res.(k).(j) +. sum (j+1) in
inp.(i).(k) -. sum 0 in
for col = 0 to n-1 do
res.(col).(col) <- sqrt (factor col col);
for row = col+1 to n-1 do
res.(row).(col) <- (factor row col) /. res.(col).(col)
done
done;
res
let pr_vec v = Array.iter (Printf.printf " %9.5f") v; print_newline() let show = Array.iter pr_vec let test a =
print_endline "\nin:"; show a; print_endline "out:"; show (cholesky a)
let _ =
test [| [|25.0; 15.0; -5.0|];
[|15.0; 18.0; 0.0|];
[|-5.0; 0.0; 11.0|] |];
test [| [|18.0; 22.0; 54.0; 42.0|];
[|22.0; 70.0; 86.0; 62.0|];
[|54.0; 86.0; 174.0; 134.0|];
[|42.0; 62.0; 134.0; 106.0|] |];</lang>
- Output:
in: 25.00000 15.00000 -5.00000 15.00000 18.00000 0.00000 -5.00000 0.00000 11.00000 out: 5.00000 0.00000 0.00000 3.00000 3.00000 0.00000 -1.00000 1.00000 3.00000 in: 18.00000 22.00000 54.00000 42.00000 22.00000 70.00000 86.00000 62.00000 54.00000 86.00000 174.00000 134.00000 42.00000 62.00000 134.00000 106.00000 out: 4.24264 0.00000 0.00000 0.00000 5.18545 6.56591 0.00000 0.00000 12.72792 3.04604 1.64974 0.00000 9.89949 1.62455 1.84971 1.39262
ooRexx
<lang oorexx>/*REXX program performs the Cholesky decomposition on a square matrix. */ niner = '25 15 -5' , /*define a 3x3 matrix. */
'15 18 0' ,
'-5 0 11'
call Cholesky niner
hexer = 18 22 54 42, /*define a 4x4 matrix. */
22 70 86 62,
54 86 174 134,
42 62 134 106
call Cholesky hexer
exit /*stick a fork in it, we're all done. */ /*----------------------------------------------------------------------------*/ Cholesky: procedure; parse arg mat; say; say; call tell 'input matrix',mat
do r=1 for ord
do c=1 for r; d=0; do i=1 for c-1; d=d+!.r.i*!.c.i; end /*i*/
if r=c then !.r.r=sqrt(!.r.r-d)
else !.r.c=1/!.c.c*(a.r.c-d)
end /*c*/
end /*r*/
call tell 'Cholesky factor',,!.,'-'
return
/*----------------------------------------------------------------------------*/ err: say; say; say '***error***!'; say; say arg(1); say; say; exit 13 /*----------------------------------------------------------------------------*/ tell: parse arg hdr,x,y,sep; n=0; if sep== then sep='-'
dPlaces= 5 /*n decimal places past the decimal point*/
width =10 /*width of field used to display elements*/
if y== then !.=0
else do row=1 for ord; do col=1 for ord; x=x !.row.col; end; end
w=words(x)
do ord=1 until ord**2>=w; end /*a fast way to find matrix's order*/
say
if ord**2\==w then call err "matrix elements don't form a square matrix."
say center(hdr, ((width+1)*w)%ord, sep)
say
do row=1 for ord; z=
do col=1 for ord; n=n+1
a.row.col=word(x,n)
if col<=row then !.row.col=a.row.col
z=z right( format(a.row.col,, dPlaces) / 1, width)
end /*col*/
say z
end /*row*/
return
/*----------------------------------------------------------------------------*/ sqrt: procedure; parse arg x; if x=0 then return 0; d=digits(); i=; m.=9
numeric digits 9; numeric form; h=d+6; if x<0 then do; x=-x; i='i'; end
parse value format(x,2,1,,0) 'E0' with g 'E' _ .; g=g*.5'e'_%2
do j=0 while h>9; m.j=h; h=h%2+1; end /*j*/
do k=j+5 to 0 by -1; numeric digits m.k; g=(g+x/g)*.5; end /*k*/
numeric digits d; return (g/1)i /*make complex if X < 0.*/</lang>
PARI/GP
<lang parigp>cholesky(M) = {
my (L = matrix(#M,#M));
for (i = 1, #M,
for (j = 1, i,
s = sum (k = 1, j-1, L[i,k] * L[j,k]);
L[i,j] = if (i == j, sqrt(M[i,i] - s), (M[i,j] - s) / L[j,j])
)
);
L
}</lang>
Output: (set displayed digits with: \p 5)
gp > cholesky([25,15,-5;15,18,0;-5,0,11]) [ 5.0000 0 0] [ 3.0000 3.0000 0] [-1.0000 1.0000 3.0000] gp > cholesky([18,22,54,42;22,70,86,62;54,86,174,134;42,62,134,106]) [4.2426 0 0 0] [5.1854 6.5659 0 0] [12.728 3.0460 1.6497 0] [9.8995 1.6246 1.8497 1.3926]
Pascal
<lang pascal>Program Cholesky;
type
D2Array = array of array of double;
function cholesky(const A: D2Array): D2Array;
var
i, j, k: integer;
s: double;
begin
setlength(cholesky, length(A), length(A));
for i := low(cholesky) to high(cholesky) do
for j := 0 to i do
begin
s := 0; for k := 0 to j - 1 do s := s + cholesky[i][k] * cholesky[j][k]; if i = j then cholesky[i][j] := sqrt(A[i][i] - s) else
cholesky[i][j] := (A[i][j] - s) / cholesky[j][j]; // save one multiplication compared to the original
end;
end;
procedure printM(const A: D2Array);
var
i, j: integer;
begin
for i := low(A) to high(A) do
begin
for j := low(A) to high(A) do
write(A[i,j]:8:5);
writeln;
end;
end;
const
m1: array[0..2,0..2] of double = ((25, 15, -5),
(15, 18, 0),
(-5, 0, 11));
m2: array[0..3,0..3] of double = ((18, 22, 54, 42),
(22, 70, 86, 62),
(54, 86, 174, 134), (42, 62, 134, 106)); var
index: integer; cIn, cOut: D2Array;
begin
setlength(cIn, length(m1), length(m1)); for index := low(m1) to high(m1) do cIn[index] := m1[index]; cOut := cholesky(cIn); printM(cOut);
writeln; setlength(cIn, length(m2), length(m2)); for index := low(m2) to high(m2) do cIn[index] := m2[index]; cOut := cholesky(cIn); printM(cOut);
end.</lang>
- Output:
5.00000 0.00000 0.00000 3.00000 3.00000 0.00000 -1.00000 1.00000 3.00000 4.24264 0.00000 0.00000 0.00000 5.18545 6.56591 0.00000 0.00000 12.72792 3.04604 1.64974 0.00000 9.89949 1.62455 1.84971 1.39262
Perl
<lang perl>sub cholesky {
my $matrix = shift;
my $chol = [ map { [(0) x @$matrix ] } @$matrix ];
for my $row (0..@$matrix-1) {
for my $col (0..$row) {
my $x = $$matrix[$row][$col];
$x -= $$chol[$row][$_]*$$chol[$col][$_] for 0..$col;
$$chol[$row][$col] = $row == $col ? sqrt $x : $x/$$chol[$col][$col];
}
}
return $chol;
}
my $example1 = [ [ 25, 15, -5 ], [ 15, 18, 0 ], [ -5, 0, 11 ] ]; print "Example 1:\n"; print +(map { sprintf "%7.4f\t", $_ } @$_), "\n" for @{ cholesky $example1 };
my $example2 = [ [ 18, 22, 54, 42], [ 22, 70, 86, 62], [ 54, 86, 174, 134], [ 42, 62, 134, 106] ]; print "\nExample 2:\n"; print +(map { sprintf "%7.4f\t", $_ } @$_), "\n" for @{ cholesky $example2 }; </lang>
- Output:
Example 1: 5.0000 0.0000 0.0000 3.0000 3.0000 0.0000 -1.0000 1.0000 3.0000 Example 2: 4.2426 0.0000 0.0000 0.0000 5.1854 6.5659 0.0000 0.0000 12.7279 3.0460 1.6497 0.0000 9.8995 1.6246 1.8497 1.3926
Perl 6
<lang perl6>sub cholesky(@A) {
my @L = @A »*» 0;
for ^@A -> $i {
for 0..$i -> $j { @L[$i][$j] = ($i == $j ?? &sqrt !! 1/@L[$j][$j] * * )( @A[$i][$j] - [+] (@L[$i;*] Z* @L[$j;*])[^$j] ); }
} return @L;
} .say for cholesky [
[25], [15, 18], [-5, 0, 11],
];
.say for cholesky [
[18, 22, 54, 42], [22, 70, 86, 62], [54, 86, 174, 134], [42, 62, 134, 106],
];</lang>
Phix
<lang Phix>function cholesky(sequence matrix) integer l = length(matrix) sequence chol = repeat(repeat(0,l),l)
for row=1 to l do
for col=1 to row do
atom x = matrix[row][col]
for i=1 to col do
x -= chol[row][i] * chol[col][i]
end for
chol[row][col] = iff(row == col ? sqrt(x) : x/chol[col][col])
end for
end for
return chol
end function
ppOpt({pp_Nest,1}) pp(cholesky({{ 25, 15, -5 },
{ 15, 18, 0 },
{ -5, 0, 11 }}))
pp(cholesky({{ 18, 22, 54, 42},
{ 22, 70, 86, 62},
{ 54, 86, 174, 134},
{ 42, 62, 134, 106}}))</lang>
- Output:
{{5,0,0},
{3,3,0},
{-1,1,3}}
{{4.242640687,0,0,0},
{5.185449729,6.565905201,0,0},
{12.72792206,3.046038495,1.649742248,0},
{9.899494937,1.624553864,1.849711005,1.392621248}}
PicoLisp
<lang PicoLisp>(scl 9) (load "@lib/math.l")
(de cholesky (A)
(let L (mapcar '(() (need (length A) 0)) A)
(for (I . R) A
(for J I
(let S (get R J)
(for K (inc J)
(dec 'S (*/ (get L I K) (get L J K) 1.0)) )
(set (nth L I J)
(if (= I J)
(sqrt S 1.0)
(*/ S 1.0 (get L J J)) ) ) ) ) )
(for R L
(for N R (prin (align 9 (round N 5))))
(prinl) ) ) )</lang>
Test: <lang PicoLisp>(cholesky
'((25.0 15.0 -5.0) (15.0 18.0 0) (-5.0 0 11.0)) )
(prinl)
(cholesky
(quote
(18.0 22.0 54.0 42.0)
(22.0 70.0 86.0 62.0)
(54.0 86.0 174.0 134.0)
(42.0 62.0 134.0 106.0) ) )</lang>
- Output:
5.00000 0.00000 0.00000 3.00000 3.00000 0.00000 -1.00000 1.00000 3.00000 4.24264 0.00000 0.00000 0.00000 5.18545 6.56591 0.00000 0.00000 12.72792 3.04604 1.64974 0.00000 9.89949 1.62455 1.84971 1.39262
PL/I
<lang PL/I>(subscriptrange): decompose: procedure options (main); /* 31 October 2013 */
declare a(*,*) float controlled;
allocate a(3,3) initial (25, 15, -5,
15, 18, 0,
-5, 0, 11);
put skip list ('Original matrix:');
put edit (a) (skip, 3 f(4));
call cholesky(a);
put skip list ('Decomposed matrix');
put edit (a) (skip, 3 f(4));
free a;
allocate a(4,4) initial (18, 22, 54, 42,
22, 70, 86, 62,
54, 86, 174, 134,
42, 62, 134, 106);
put skip list ('Original matrix:');
put edit (a) (skip, (hbound(a,1)) f(12) );
call cholesky(a);
put skip list ('Decomposed matrix');
put edit (a) (skip, (hbound(a,1)) f(12,5) );
cholesky: procedure(a);
declare a(*,*) float; declare L(hbound(a,1), hbound(a,2)) float; declare s float; declare (i, j, k) fixed binary;
L = 0;
do i = lbound(a,1) to hbound(a,1);
do j = lbound(a,2) to i;
s = 0;
do k = lbound(a,2) to j-1;
s = s + L(i,k) * L(j,k);
end;
if i = j then
L(i,j) = sqrt(a(i,i) - s);
else
L(i,j) = (a(i,j) - s) / L(j,j);
end;
end;
a = L;
end cholesky;
end decompose;</lang> ACTUAL RESULTS:-
Original matrix:
25 15 -5
15 18 0
-5 0 11
Decomposed matrix
5 0 0
3 3 0
-1 1 3
Original matrix:
18 22 54 42
22 70 86 62
54 86 174 134
42 62 134 106
Decomposed matrix
4.24264 0.00000 0.00000 0.00000
5.18545 6.56591 0.00000 0.00000
12.72792 3.04604 1.64974 0.00000
9.89950 1.62455 1.84971 1.39262
PowerShell
<lang PowerShell> function cholesky ($a) {
$l = @()
if ($a) {
$n = $a.count
$end = $n - 1
$l = @(0) * $n
foreach ($i in 0..$end) {$l[$i] = @(0) * $n}
foreach ($k in 0..$end) {
$m = $k - 1
$sum = 0
if(0 -lt $k) {
foreach ($j in 0..$m) {$sum += $l[$k][$j]*$l[$k][$j]}
}
$l[$k][$k] = [Math]::Sqrt($a[$k][$k] - $sum)
if ($k -lt $end) {
foreach ($i in ($k+1)..$end) {
$sum = 0
if (0 -lt $k) {
foreach ($j in 0..$m) {$sum += $l[$i][$j]*$l[$k][$j]}
}
$l[$i][$k] = ($a[$i][$k] - $sum)/$l[$k][$k]
}
}
}
}
$l
}
function show($a) {
if($a) {
0..($a.Count - 1) | foreach{ if($a[$_]){"$($a[$_])"}else{""} }
}
}
$a1 = @( @(25, 15, -5), @(15, 18, 0), @(-5, 0, 11) ) "a1 =" show $a1 "" "l1 =" show (cholesky $a1) "" $a2 = @( @(18, 22, 54, 42), @(22, 70, 86, 62), @(54, 86, 174, 134), @(42, 62, 134, 106) ) "a2 =" show $a2 "" "l2 =" show (cholesky $a2) </lang> Output:
a1 = 25 15 -5 15 18 0 -5 0 11 l1 = 5 0 0 3 3 0 -1 1 3 a2 = 18 22 54 42 22 70 86 62 54 86 174 134 42 62 134 106 l2 = 4.24264068711928 0 0 0 5.18544972870135 6.5659052011974 0 0 12.7279220613579 3.04603849540086 1.64974224790907 0 9.89949493661167 1.62455386421379 1.84971100523138 1.39262124764559
Python
Python2.X version
<lang python>from __future__ import print_function
from pprint import pprint from math import sqrt
def cholesky(A):
L = [[0.0] * len(A) for _ in xrange(len(A))]
for i in xrange(len(A)):
for j in xrange(i+1):
s = sum(L[i][k] * L[j][k] for k in xrange(j))
L[i][j] = sqrt(A[i][i] - s) if (i == j) else \
(1.0 / L[j][j] * (A[i][j] - s))
return L
if __name__ == "__main__":
m1 = [[25, 15, -5],
[15, 18, 0],
[-5, 0, 11]]
pprint(cholesky(m1))
print()
m2 = [[18, 22, 54, 42],
[22, 70, 86, 62],
[54, 86, 174, 134],
[42, 62, 134, 106]]
pprint(cholesky(m2), width=120)</lang>
- Output:
[[5.0, 0.0, 0.0], [3.0, 3.0, 0.0], [-1.0, 1.0, 3.0]] [[4.242640687119285, 0.0, 0.0, 0.0], [5.185449728701349, 6.565905201197403, 0.0, 0.0], [12.727922061357857, 3.0460384954008553, 1.6497422479090704, 0.0], [9.899494936611667, 1.624553864213788, 1.8497110052313648, 1.3926212476456026]]
Python3.X version using extra Python idioms
Factors out accesses to A[i], L[i], and L[j] by creating Ai, Li and Lj respectively as well as using enumerate instead of range(len(some_array)).
<lang python>def cholesky(A):
L = [[0.0] * len(A) for _ in range(len(A))]
for i, (Ai, Li) in enumerate(zip(A, L)):
for j, Lj in enumerate(L[:i+1]):
s = sum(Li[k] * Lj[k] for k in range(j))
Li[j] = sqrt(Ai[i] - s) if (i == j) else \
(1.0 / Lj[j] * (Ai[j] - s))
return L</lang>
- Output:
(As above)
q
<lang q>solve:{[A;B] $[0h>type A;B%A;inv[A] mmu B]} ak:{[m;k] (),/:m[;k]til k:k-1} akk:{[m;k] m[k;k:k-1]} transpose:{$[0h=type x;flip x;enlist each x]} mult:{[A;B]$[0h=type A;A mmu B;A*B]} cholesky:{[A] {[A;L;n] l_k:solve[L;ak[A;n]]; l_kk:first over sqrt[akk[A;n] - mult[transpose l_k;l_k]]; ({$[0h<type x;enlist x;x]}L,'0f),enlist raze transpose[l_k],l_kk }[A]/[sqrt A[0;0];1_1+til count first A] }
show cholesky (25 15 -5f;15 18 0f;-5 0 11f) -1""; show cholesky (18 22 54 42f;22 70 86 62f;54 86 174 134f;42 62 134 106f)</lang>
- Output:
5 0 0 3 3 0 -1 1 3 4.242641 0 0 0 5.18545 6.565905 0 0 12.72792 3.046038 1.649742 0 9.899495 1.624554 1.849711 1.392621
R
<lang r>t(chol(matrix(c(25, 15, -5, 15, 18, 0, -5, 0, 11), nrow=3, ncol=3)))
- [,1] [,2] [,3]
- [1,] 5 0 0
- [2,] 3 3 0
- [3,] -1 1 3
t(chol(matrix(c(18, 22, 54, 42, 22, 70, 86, 62, 54, 86, 174, 134, 42, 62, 134, 106), nrow=4, ncol=4)))
- [,1] [,2] [,3] [,4]
- [1,] 4.242641 0.000000 0.000000 0.000000
- [2,] 5.185450 6.565905 0.000000 0.000000
- [3,] 12.727922 3.046038 1.649742 0.000000
- [4,] 9.899495 1.624554 1.849711 1.392621</lang>
Racket
<lang racket>
- lang racket
(require math)
(define (cholesky A)
(define mref matrix-ref)
(define n (matrix-num-rows A))
(define L (for/vector ([_ n]) (for/vector ([_ n]) 0)))
(define (set L i j x) (vector-set! (vector-ref L i) j x))
(define (ref L i j) (vector-ref (vector-ref L i) j))
(for* ([i n] [k n])
(set L i k
(cond
[(= i k)
(sqrt (- (mref A i i) (for/sum ([j k]) (sqr (ref L k j)))))]
[(> i k)
(/ (- (mref A i k) (for/sum ([j k]) (* (ref L i j) (ref L k j))))
(ref L k k))]
[else 0])))
L)
(cholesky (matrix [[25 15 -5]
[15 18 0]
[-5 0 11]]))
(cholesky (matrix [[18 22 54 42]
[22 70 86 62]
[54 86 174 134]
[42 62 134 106]]))
</lang> Output: <lang racket> '#(#(5 0 0)
#(3 3 0) #(-1 1 3))
'#(#(4.242640687119285 0 0 0)
#( 5.185449728701349 6.565905201197403 0 0) #(12.727922061357857 3.0460384954008553 1.6497422479090704 0) #( 9.899494936611665 1.6245538642137891 1.849711005231382 1.3926212476455924))
</lang>
REXX
If trailing zeros are wanted after the decimal point for values of zero (0), the / 1 (a division by unity performs
REXX number normalization) can be removed from the line (number 40) which contains the source statement:
- z=z right( format(@.row.col, , dPlaces) / 1, width)
<lang rexx>/*REXX program performs the Cholesky decomposition on a square matrix & displays results*/ niner = '25 15 -5' , /*define a 3x3 matrix with elements. */
'15 18 0' ,
'-5 0 11'
call Cholesky niner
hexer = 18 22 54 42, /*define a 4x4 matrix with elements. */
22 70 86 62,
54 86 174 134,
42 62 134 106
call Cholesky hexer
exit /*stick a fork in it, we're all done. */ /*──────────────────────────────────────────────────────────────────────────────────────*/ Cholesky: procedure; parse arg mat; say; say; call tell 'input array',mat
do r=1 for ord
do c=1 for r; $=0; do i=1 for c-1; $= $ + !.r.i * !.c.i; end /*i*/
if r=c then !.r.r= sqrt(!.r.r - $) / 1
else !.r.c= 1 / !.c.c * (@.r.c - $)
end /*c*/
end /*r*/
call tell 'Cholesky factor',,!.,'─'
return
/*──────────────────────────────────────────────────────────────────────────────────────*/ err: say; say; say '***error***!'; say; say arg(1); say; say; exit 13 /*──────────────────────────────────────────────────────────────────────────────────────*/ tell: parse arg hdr,x,y,sep; #=0; if sep== then sep= '═'
dPlaces= 5 /*# dec. places past the decimal point.*/
width =10 /*field width used to display elements.*/
if y== then !.=0
else do row=1 for ord; do col=1 for ord; x=x !.row.col; end; end
w=words(x)
do ord=1 until ord**2>=w; end /*a fast way to find the matrix's order*/
say
if ord**2\==w then call err "matrix elements don't form a square matrix."
say center(hdr, ((width + 1) * w) % ord, sep)
say
do row=1 for ord; z=
do col=1 for ord; #= # + 1
@.row.col= word(x, #)
if col<=row then !.row.col= @.row.col
z=z right( format(@.row.col, , dPlaces) / 1, width)
end /*col*/ /* ↑↑↑ */
say z /* └┴┴──◄──normalization for zero*/
end /*row*/
return
/*──────────────────────────────────────────────────────────────────────────────────────*/ sqrt: procedure; parse arg x; if x=0 then return 0; d=digits(); numeric digits; h=d+6
numeric form; m.=9; parse value format(x,2,1,,0) 'E0' with g 'E' _ .; g=g*.5'e'_ %2
do j=0 while h>9; m.j=h; h=h%2+1; end /*j*/
do k=j+5 to 0 by -1; numeric digits m.k; g=(g+x/g)*.5; end /*k*/; return g</lang>
- output:
═══════════input matrix══════════
25 15 -5
15 18 0
-5 0 11
─────────Cholesky factor─────────
5 0 0
3 3 0
-1 1 3
════════════════input matrix════════════════
18 22 54 42
22 70 86 62
54 86 174 134
42 62 134 106
──────────────Cholesky factor───────────────
4.24264 0 0 0
5.18545 6.56591 0 0
12.72792 3.04604 1.64974 0
9.89949 1.62455 1.84971 1.39262
Ring
<lang ring>
- Project : Cholesky decomposition
load "stdlib.ring" decimals(5) m1 = [[25, 15, -5],
[15, 18, 0],
[-5, 0, 11]]
cholesky(m1) printarray(m1) see nl
m2 = [[18, 22, 54, 42],
[22, 70, 86, 62],
[54, 86, 174, 134],
[42, 62, 134, 106]]
cholesky(m2) printarray(m2)
func cholesky(a) l = newlist(len(a), len(a)) for i = 1 to len(a)
for j = 1 to i
s = 0
for k = 1 to j
s = s + l[i][k] * l[j][k]
next
if i = j
l[i][j] = sqrt(a[i][i] - s)
else
l[i][j] = (a[i][j] - s) / l[j][j]
ok
next
next a = l
func printarray(a)
for row = 1 to len(a)
for col = 1 to len(a)
see "" + a[row][col] + " "
next
see nl
next
</lang> Output:
5 0 0 3 3 0 -1 1 3 4.24264 0 0 0 5.18545 6.56591 0 0 12.72792 3.04604 1.64974 0 9.89949 1.62455 1.84971 1.39262
Ruby
<lang ruby>require 'matrix'
class Matrix
def symmetric?
return false if not square?
(0 ... row_size).each do |i|
(0 .. i).each do |j|
return false if self[i,j] != self[j,i]
end
end
true
end
def cholesky_factor
raise ArgumentError, "must provide symmetric matrix" unless symmetric?
l = Array.new(row_size) {Array.new(row_size, 0)}
(0 ... row_size).each do |k|
(0 ... row_size).each do |i|
if i == k
sum = (0 .. k-1).inject(0.0) {|sum, j| sum + l[k][j] ** 2}
val = Math.sqrt(self[k,k] - sum)
l[k][k] = val
elsif i > k
sum = (0 .. k-1).inject(0.0) {|sum, j| sum + l[i][j] * l[k][j]}
val = (self[k,i] - sum) / l[k][k]
l[i][k] = val
end
end
end
Matrix[*l]
end
end
puts Matrix[[25,15,-5],[15,18,0],[-5,0,11]].cholesky_factor puts Matrix[[18, 22, 54, 42],
[22, 70, 86, 62],
[54, 86, 174, 134],
[42, 62, 134, 106]].cholesky_factor</lang>
- Output:
Matrix[[5.0, 0, 0], [3.0, 3.0, 0], [-1.0, 1.0, 3.0]] Matrix[[4.242640687119285, 0, 0, 0], [5.185449728701349, 6.565905201197403, 0, 0], [12.727922061357857, 3.0460384954008553, 1.6497422479090704, 0], [9.899494936611665, 1.6245538642137891, 1.849711005231382, 1.3926212476455924]]
Rust
<lang rust>fn cholesky(mat: Vec<f64>, n: usize) -> Vec<f64> {
let mut res = vec![0.0; mat.len()];
for i in 0..n {
for j in 0..(i+1){
let mut s = 0.0;
for k in 0..j {
s += res[i * n + k] * res[j * n + k];
}
res[i * n + j] = if i == j { (mat[i * n + i] - s).sqrt() } else { (1.0 / res[j * n + j] * (mat[i * n + j] - s)) };
}
}
res
}
fn show_matrix(matrix: Vec<f64>, n: usize){
for i in 0..n {
for j in 0..n {
print!("{:.4}\t", matrix[i * n + j]);
}
println!("");
}
println!("");
}
fn main(){
let dimension = 3 as usize;
let m1 = vec![25.0, 15.0, -5.0,
15.0, 18.0, 0.0,
-5.0, 0.0, 11.0];
let res1 = cholesky(m1, dimension);
show_matrix(res1, dimension);
let dimension = 4 as usize;
let m2 = vec![18.0, 22.0, 54.0, 42.0,
22.0, 70.0, 86.0, 62.0,
54.0, 86.0, 174.0, 134.0,
42.0, 62.0, 134.0, 106.0];
let res2 = cholesky(m2, dimension);
show_matrix(res2, dimension);
} </lang>
- Output:
5.0000 0.0000 0.0000 3.0000 3.0000 0.0000 -1.0000 1.0000 3.0000 4.2426 0.0000 0.0000 0.0000 5.1854 6.5659 0.0000 0.0000 12.7279 3.0460 1.6497 0.0000 9.8995 1.6246 1.8497 1.3926
Scala
<lang scala>case class Matrix( val matrix:Array[Array[Double]] ) {
// Assuming matrix is positive-definite, symmetric and not empty...
val rows,cols = matrix.size
def getOption( r:Int, c:Int ) : Option[Double] = Pair(r,c) match {
case (r,c) if r < rows && c < rows => Some(matrix(r)(c))
case _ => None
}
def isLowerTriangle( r:Int, c:Int ) : Boolean = { c <= r }
def isDiagonal( r:Int, c:Int ) : Boolean = { r == c}
override def toString = matrix.map(_.mkString(", ")).mkString("\n")
/**
* Perform Cholesky Decomposition of this matrix
*/
lazy val cholesky : Matrix = {
val l = Array.ofDim[Double](rows*cols)
for( i <- (0 until rows); j <- (0 until cols) ) yield {
val s = (for( k <- (0 until j) ) yield { l(i*rows+k) * l(j*rows+k) }).sum
l(i*rows+j) = (i,j) match {
case (r,c) if isDiagonal(r,c) => scala.math.sqrt(matrix(i)(i) - s)
case (r,c) if isLowerTriangle(r,c) => (1.0 / l(j*rows+j) * (matrix(i)(j) - s))
case _ => 0
}
}
val m = Array.ofDim[Double](rows,cols) for( i <- (0 until rows); j <- (0 until cols) ) m(i)(j) = l(i*rows+j) Matrix(m) }
}
// A little test... val a1 = Matrix(Array[Array[Double]](Array(25,15,-5),Array(15,18,0),Array(-5,0,11))) val a2 = Matrix(Array[Array[Double]](Array(18,22,54,42), Array(22,70,86,62), Array(54,86,174,134), Array(42,62,134,106)))
val l1 = a1.cholesky val l2 = a2.cholesky
// Given test results
val r1 = Array[Double](5,0,0,3,3,0,-1,1,3)
val r2 = Array[Double](4.24264,0.00000,0.00000,0.00000,5.18545,6.56591,0.00000,0.00000,
12.72792,3.04604,1.64974,0.00000,9.89949,1.62455,1.84971,1.39262)
// Verify assertions (l1.matrix.flatten.zip(r1)).foreach{ case (result,test) =>
assert(math.round( result * 100000 ) * 0.00001 == math.round( test * 100000 ) * 0.00001)
}
(l2.matrix.flatten.zip(r2)).foreach{ case (result,test) =>
assert(math.round( result * 100000 ) * 0.00001 == math.round( test * 100000 ) * 0.00001)
}</lang>
Scilab
The Cholesky decomposition is builtin, and an upper triangular matrix is returned, such that $A=L^TL$.
<lang scilab>a = [25 15 -5; 15 18 0; -5 0 11]; chol(a)
ans =
5. 3. -1. 0. 3. 1. 0. 0. 3.
a = [18 22 54 42; 22 70 86 62;
54 86 174 134; 42 62 134 106];
chol(a)
ans =
4.2426407 5.1854497 12.727922 9.8994949 0. 6.5659052 3.0460385 1.6245539 0. 0. 1.6497422 1.849711 0. 0. 0. 1.3926212</lang>
Seed7
<lang seed7>$ include "seed7_05.s7i";
include "float.s7i"; include "math.s7i";
const type: matrix is array array float;
const func matrix: cholesky (in matrix: a) is func
result
var matrix: cholesky is 0 times 0 times 0.0;
local
var integer: i is 0;
var integer: j is 0;
var integer: k is 0;
var float: sum is 0.0;
begin
cholesky := length(a) times length(a) times 0.0;
for key i range cholesky do
for j range 1 to i do
sum := 0.0; for k range 1 to j do sum +:= cholesky[i][k] * cholesky[j][k];
end for;
if i = j then cholesky[i][i] := sqrt(a[i][i] - sum) else
cholesky[i][j] := (a[i][j] - sum) / cholesky[j][j];
end if;
end for;
end for;
end func;
const proc: writeMat (in matrix: a) is func
local
var integer: i is 0;
var float: num is 0.0;
begin
for key i range a do
for num range a[i] do
write(num digits 5 lpad 8);
end for;
writeln;
end for;
end func;
const matrix: m1 is [] (
[] (25.0, 15.0, -5.0), [] (15.0, 18.0, 0.0), [] (-5.0, 0.0, 11.0));
const matrix: m2 is [] (
[] (18.0, 22.0, 54.0, 42.0), [] (22.0, 70.0, 86.0, 62.0), [] (54.0, 86.0, 174.0, 134.0), [] (42.0, 62.0, 134.0, 106.0));
const proc: main is func
begin writeMat(cholesky(m1)); writeln; writeMat(cholesky(m2)); end func;</lang>
Output:
5.00000 0.00000 0.00000 3.00000 3.00000 0.00000 -1.00000 1.00000 3.00000 4.24264 0.00000 0.00000 0.00000 5.18545 6.56591 0.00000 0.00000 12.72792 3.04604 1.64974 0.00000 9.89950 1.62455 1.84971 1.39262
Sidef
<lang ruby>func cholesky(matrix) {
var chol = matrix.len.of { matrix.len.of(0) }
for row in ^matrix {
for col in (0..row) {
var x = matrix[row][col]
for i in (0..col) {
x -= (chol[row][i] * chol[col][i])
}
chol[row][col] = (row == col ? x.sqrt : x/chol[col][col])
}
}
return chol
}</lang>
Examples: <lang ruby>var example1 = [ [ 25, 15, -5 ],
[ 15, 18, 0 ],
[ -5, 0, 11 ] ];
say "Example 1:"; cholesky(example1).each { |row|
say row.map {'%7.4f' % _}.join(' ');
}
var example2 = [ [ 18, 22, 54, 42],
[ 22, 70, 86, 62],
[ 54, 86, 174, 134],
[ 42, 62, 134, 106] ];
say "\nExample 2:"; cholesky(example2).each { |row|
say row.map {'%7.4f' % _}.join(' ');
}</lang>
- Output:
Example 1: 5.0000 0.0000 0.0000 3.0000 3.0000 0.0000 -1.0000 1.0000 3.0000 Example 2: 4.2426 0.0000 0.0000 0.0000 5.1854 6.5659 0.0000 0.0000 12.7279 3.0460 1.6497 0.0000 9.8995 1.6246 1.8497 1.3926
Smalltalk
<lang Smalltalk> FloatMatrix>>#cholesky | l | l := FloatMatrix zero: numRows. 1 to: numRows do: [:i | 1 to: i do: [:k | | rowSum lkk factor aki partialSum | i = k ifTrue: [ rowSum := (1 to: k - 1) sum: [:j | | lkj | lkj := l at: j @ k. lkj squared]. lkk := (self at: k @ k) - rowSum. lkk := lkk sqrt. l at: k @ k put: lkk] ifFalse: [ factor := l at: k @ k. aki := self at: k @ i. partialSum := (1 to: k - 1) sum: [:j | | ljk lji | lji := l at: j @ i. ljk := l at: j @ k. lji * ljk]. l at: k @ i put: aki - partialSum * factor reciprocal]]]. ^l </lang>
Stata
See Cholesky square-root decomposition in Stata help. <lang stata>mata
- a=25,15,-5\15,18,0\-5,0,11
- a
[symmetric]
1 2 3 +----------------+ 1 | 25 | 2 | 15 18 | 3 | -5 0 11 | +----------------+
- cholesky(a)
1 2 3 +----------------+ 1 | 5 0 0 | 2 | 3 3 0 | 3 | -1 1 3 | +----------------+
- a=18,22,54,42\22,70,86,62\54,86,174,134\42,62,134,106
- a
[symmetric]
1 2 3 4 +-------------------------+ 1 | 18 | 2 | 22 70 | 3 | 54 86 174 | 4 | 42 62 134 106 | +-------------------------+
- cholesky(a)
1 2 3 4 +---------------------------------------------------------+ 1 | 4.242640687 0 0 0 | 2 | 5.185449729 6.565905201 0 0 | 3 | 12.72792206 3.046038495 1.649742248 0 | 4 | 9.899494937 1.624553864 1.849711005 1.392621248 | +---------------------------------------------------------+</lang>
Swift
<lang swift>func cholesky(matrix: [Double], n: Int) -> [Double] {
var res = [Double](repeating: 0, count: matrix.count)
for i in 0..<n {
for j in 0..<i+1 {
var s = 0.0
for k in 0..<j {
s += res[i * n + k] * res[j * n + k]
}
if i == j {
res[i * n + j] = (matrix[i * n + i] - s).squareRoot()
} else {
res[i * n + j] = (1.0 / res[j * n + j] * (matrix[i * n + j] - s))
}
}
}
return res
}
func printMatrix(_ matrix: [Double], n: Int) {
for i in 0..<n {
for j in 0..<n {
print(matrix[i * n + j], terminator: " ")
}
print() }
}
let res1 = cholesky(
matrix: [25.0, 15.0, -5.0,
15.0, 18.0, 0.0,
-5.0, 0.0, 11.0],
n: 3
)
let res2 = cholesky(
matrix: [18.0, 22.0, 54.0, 42.0,
22.0, 70.0, 86.0, 62.0,
54.0, 86.0, 174.0, 134.0,
42.0, 62.0, 134.0, 106.0],
n: 4
)
printMatrix(res1, n: 3) print() printMatrix(res2, n: 4)</lang>
- Output:
5.0 0.0 0.0 3.0 3.0 0.0 -1.0 1.0 3.0 4.242640687119285 0.0 0.0 0.0 5.185449728701349 6.565905201197403 0.0 0.0 12.727922061357857 3.0460384954008553 1.6497422479090704 0.0 9.899494936611667 1.624553864213788 1.8497110052313648 1.3926212476456026
Tcl
<lang tcl>proc cholesky a {
set m [llength $a]
set n [llength [lindex $a 0]]
set l [lrepeat $m [lrepeat $n 0.0]]
for {set i 0} {$i < $m} {incr i} {
for {set k 0} {$k < $i+1} {incr k} { set sum 0.0 for {set j 0} {$j < $k} {incr j} { set sum [expr {$sum + [lindex $l $i $j] * [lindex $l $k $j]}] } lset l $i $k [expr { $i == $k ? sqrt([lindex $a $i $i] - $sum) : (1.0 / [lindex $l $k $k] * ([lindex $a $i $k] - $sum)) }] }
} return $l
}</lang> Demonstration code: <lang tcl>set test1 {
{25 15 -5}
{15 18 0}
{-5 0 11}
} puts [cholesky $test1] set test2 {
{18 22 54 42}
{22 70 86 62}
{54 86 174 134}
{42 62 134 106}
} puts [cholesky $test2]</lang>
- Output:
{5.0 0.0 0.0} {3.0 3.0 0.0} {-1.0 1.0 3.0}
{4.242640687119285 0.0 0.0 0.0} {5.185449728701349 6.565905201197403 0.0 0.0} {12.727922061357857 3.0460384954008553 1.6497422479090704 0.0} {9.899494936611667 1.624553864213788 1.8497110052313648 1.3926212476456026}
VBA
This function returns the lower Cholesky decomposition of a square matrix fed to it. It does not check for positive semi-definiteness, although it does check for squareness. It assumes that Option Base 0 is set, and thus the matrix entry indices need to be adjusted if Base is set to 1. It also assumes a matrix of size less than 256x256. To handle larger matrices, change all Byte-type variables to Long. It takes the square matrix range as an input, and can be implemented as an array function on the same sized square range of cells as output. For example, if the matrix is in cells A1:E5, highlighting cells A10:E14, typing "=Cholesky(A1:E5)" and htting Ctrl-Shift-Enter will populate the target cells with the lower Cholesky decomposition.
<lang vb>Function Cholesky(Mat As Range) As Variant
Dim A() As Double, L() As Double, sum As Double, sum2 As Double Dim m As Byte, i As Byte, j As Byte, k As Byte
'Ensure matrix is square
If Mat.Rows.Count <> Mat.Columns.Count Then
MsgBox ("Correlation matrix is not square")
Exit Function
End If
m = Mat.Rows.Count
'Initialize and populate matrix A of values and matrix L which will be the lower Cholesky
ReDim A(0 To m - 1, 0 To m - 1)
ReDim L(0 To m - 1, 0 To m - 1)
For i = 0 To m - 1
For j = 0 To m - 1
A(i, j) = Mat(i + 1, j + 1).Value2
L(i, j) = 0
Next j
Next i
'Handle the simple cases explicitly to save time
Select Case m
Case Is = 1
L(0, 0) = Sqr(A(0, 0))
Case Is = 2
L(0, 0) = Sqr(A(0, 0))
L(1, 0) = A(1, 0) / L(0, 0)
L(1, 1) = Sqr(A(1, 1) - L(1, 0) * L(1, 0))
Case Else
L(0, 0) = Sqr(A(0, 0))
L(1, 0) = A(1, 0) / L(0, 0)
L(1, 1) = Sqr(A(1, 1) - L(1, 0) * L(1, 0))
For i = 2 To m - 1
sum2 = 0
For k = 0 To i - 1
sum = 0
For j = 0 To k
sum = sum + L(i, j) * L(k, j)
Next j
L(i, k) = (A(i, k) - sum) / L(k, k)
sum2 = sum2 + L(i, k) * L(i, k)
Next k
L(i, i) = Sqr(A(i, i) - sum2)
Next i
End Select
Cholesky = L
End Function </lang>
zkl
Using the GNU Scientific Library: <lang zkl>var [const] GSL=Import("zklGSL"); // libGSL (GNU Scientific Library) fcn lowerCholesky(m){ // trans: C
rows:=m.rows;
lcm:=GSL.Matrix(rows,rows); // zero filled
foreach i,j in (rows,i+1){
s:=(0).reduce(j,'wrap(s,k){ s + lcm[i,k]*lcm[j,k] },0.0);
lcm[i,j]=( if(i==j)(m[i,i] - s).sqrt()
else 1.0/lcm[j,j]*(m[i,j] - s) );
} lcm
}</lang>
- Output:
lowerCholesky(GSL.Matrix(3,3).set(25, 15, -5, // example 1 15, 18, 0, -5, 0, 11)) .format(6).println(); 5.00, 0.00, 0.00 3.00, 3.00, 0.00 -1.00, 1.00, 3.00
- Output:
lowerCholesky(GSL.Matrix(4,4).set( // example 2
18, 22, 54, 42,
22, 70, 86, 62,
54, 86, 174, 134,
42, 62, 134, 106) )
.format(8,4).println();
4.2426, 0.0000, 0.0000, 0.0000
5.1854, 6.5659, 0.0000, 0.0000
12.7279, 3.0460, 1.6497, 0.0000
9.8995, 1.6246, 1.8497, 1.3926
Or, using lists:
<lang zkl>fcn cholesky(mat){
rows:=mat.len();
r:=(0).pump(rows,List().write, (0).pump(rows,List,0.0).copy); // matrix of zeros
foreach i,j in (rows,i+1){
s:=(0).reduce(j,'wrap(s,k){ s + r[i][k]*r[j][k] },0.0);
r[i][j]=( if(i==j)(mat[i][i] - s).sqrt()
else 1.0/r[j][j]*(mat[i][j] - s) );
} r
}</lang> <lang zkl>ex1:=L( L(25.0,15.0,-5.0), L(15.0,18.0,0.0), L(-5.0,0.0,11.0) ); printM(cholesky(ex1)); println("-----------------"); ex2:=L( L(18.0, 22.0, 54.0, 42.0,),
L(22.0, 70.0, 86.0, 62.0,),
L(54.0, 86.0, 174.0, 134.0,), L(42.0, 62.0, 134.0, 106.0,) ); printM(cholesky(ex2));</lang> <lang zkl>fcn printM(m){ m.pump(Console.println,rowFmt) } fcn rowFmt(row){ ("%9.5f "*row.len()).fmt(row.xplode()) }</lang>
- Output:
5.00000 0.00000 0.00000 3.00000 3.00000 0.00000 -1.00000 1.00000 3.00000 ----------------- 4.24264 0.00000 0.00000 0.00000 5.18545 6.56591 0.00000 0.00000 12.72792 3.04604 1.64974 0.00000 9.89949 1.62455 1.84971 1.39262
ZX Spectrum Basic
<lang zxbasic>10 LET d=2000: GO SUB 1000: GO SUB 4000: GO SUB 5000 20 LET d=3000: GO SUB 1000: GO SUB 4000: GO SUB 5000 30 STOP 1000 RESTORE d 1010 READ a,b 1020 DIM m(a,b) 1040 FOR i=1 TO a 1050 FOR j=1 TO b 1060 READ m(i,j) 1070 NEXT j 1080 NEXT i 1090 RETURN 2000 DATA 3,3,25,15,-5,15,18,0,-5,0,11 3000 DATA 4,4,18,22,54,42,22,70,86,62,54,86,174,134,42,62,134,106 4000 REM Cholesky decomposition 4005 DIM l(a,b) 4010 FOR i=1 TO a 4020 FOR j=1 TO i 4030 LET s=0 4050 FOR k=1 TO j-1 4060 LET s=s+l(i,k)*l(j,k) 4070 NEXT k 4080 IF i=j THEN LET l(i,j)=SQR (m(i,i)-s): GO TO 4100 4090 LET l(i,j)=(m(i,j)-s)/l(j,j) 4100 NEXT j 4110 NEXT i 4120 RETURN 5000 REM Print 5010 FOR r=1 TO a 5020 FOR c=1 TO b 5030 PRINT l(r,c);" "; 5040 NEXT c 5050 PRINT 5060 NEXT r 5070 RETURN</lang>
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