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This means it might contain formatting issues, incorrect code, conceptual problems, or other severe issues.
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{{wikipedia|Rref#Pseudocode}} {{task|Matrices}} {{omit from|GUISS}}
;Task: Show how to compute the '''reduced row echelon form''' (a.k.a. '''row canonical form''') of a matrix.
The matrix can be stored in any datatype that is convenient (for most languages, this will probably be a two-dimensional array).
Built-in functions or this pseudocode (from Wikipedia) may be used: '''function''' ToReducedRowEchelonForm(Matrix M) '''is''' ''lead'' := 0 ''rowCount'' := the number of rows in M ''columnCount'' := the number of columns in M '''for''' 0 ≤ ''r'' < ''rowCount'' '''do''' '''if''' ''columnCount'' ≤ ''lead'' '''then''' '''stop''' '''end if''' ''i'' = ''r'' '''while''' M[''i'', ''lead''] = 0 '''do''' ''i'' = ''i'' + 1 '''if''' ''rowCount'' = ''i'' '''then''' ''i'' = ''r'' ''lead'' = ''lead'' + 1 '''if''' ''columnCount'' = ''lead'' '''then''' '''stop''' '''end if''' '''end if''' '''end while''' Swap rows ''i'' and ''r'' If M[''r'', ''lead''] is not 0 divide row ''r'' by M[''r'', ''lead''] '''for''' 0 ≤ ''i'' < ''rowCount'' '''do''' '''if''' ''i'' ≠ ''r'' '''do''' Subtract M[i, lead] multiplied by row ''r'' from row ''i'' '''end if''' '''end for''' ''lead'' = ''lead'' + 1 '''end for''' '''end function'''
For testing purposes, the RREF of this matrix:
1 2 -1 -4
2 3 -1 -11
-2 0 -3 22
is:
1 0 0 -8
0 1 0 1
0 0 1 -2
360 Assembly
{{trans|BBC BASIC}}
* reduced row echelon form 27/08/2015
RREF CSECT
USING RREF,R12
LR R12,R15
LA R10,1 lead=1
LA R7,1
LOOPR CH R7,NROWS do r=1 to nrows
BH ELOOPR
CH R10,NCOLS if lead>=ncols
BNL ELOOPR
LR R8,R7 i=r
WHILE LR R1,R8 do while m(i,lead)=0
BCTR R1,0
MH R1,NCOLS
LR R6,R10 lead
BCTR R6,0
AR R1,R6
SLA R1,2
L R6,M(R1) m(i,lead)
LTR R6,R6
BNZ EWHILE m(i,lead)<>0
LA R8,1(R8) i=i+1
CH R8,NROWS if i=nrows
BNE EIF
LR R8,R7 i=r
LA R10,1(R10) lead=lead+1
CH R10,NCOLS if lead=ncols
BE ELOOPR
EIF B WHILE
EWHILE LA R9,1
LOOPJ1 CH R9,NCOLS do j=1 to ncols
BH ELOOPJ1
LR R1,R7 r
BCTR R1,0
MH R1,NCOLS
LR R6,R9 j
BCTR R6,0
AR R1,R6
SLA R1,2
LA R3,M(R1) R3=@m(r,j)
LR R1,R8 i
BCTR R1,0
MH R1,NCOLS
LR R6,R9 j
BCTR R6,0
AR R1,R6
SLA R1,2
LA R4,M(R1) R4=@m(i,j)
L R2,0(R3)
MVC 0(2,R3),0(R4) swap m(i,j),m(r,j)
ST R2,0(R4)
LA R9,1(R9) j=j+1
B LOOPJ1
ELOOPJ1 LR R1,R7 r
BCTR R1,0
MH R1,NCOLS
LR R6,R10 lead
BCTR R6,0
AR R1,R6
SLA R1,2
L R11,M(R1) n=m(r,lead)
CH R11,=H'1' if n^=1
BE ELOOPJ2
LA R9,1
LOOPJ2 CH R9,NCOLS do j=1 to ncols
BH ELOOPJ2
LR R1,R7 r
BCTR R1,0
MH R1,NCOLS
LR R6,R9 j
BCTR R6,0
AR R1,R6
SLA R1,2
LA R5,M(R1) R5=@m(i,j)
L R2,0(R5) m(r,j)
LR R1,R11 n
SRDA R2,32
DR R2,R1 m(r,j)/n
ST R3,0(R5) m(r,j)=m(r,j)/n
LA R9,1(R9) j=j+1
B LOOPJ2
ELOOPJ2 LA R8,1
LOOPI3 CH R8,NROWS do i=1 to nrows
BH ELOOPI3
CR R8,R7 if i^=r
BE ELOOPJ3
LR R1,R8 i
BCTR R1,0
MH R1,NCOLS
LR R6,R10 lead
BCTR R6,0
AR R1,R6
SLA R1,2
L R11,M(R1) n=m(i,lead)
LA R9,1
LOOPJ3 CH R9,NCOLS do j=1 to ncols
BH ELOOPJ3
LR R1,R8 i
BCTR R1,0
MH R1,NCOLS
LR R6,R9 j
BCTR R6,0
AR R1,R6
SLA R1,2
LA R4,M(R1) R4=@m(i,j)
L R5,0(R4) m(i,j)
LR R1,R7 r
BCTR R1,0
MH R1,NCOLS
LR R6,R9 j
BCTR R6,0
AR R1,R6
SLA R1,2
L R3,M(R1) m(r,j)
MR R2,R11 m(r,j)*n
SR R5,R3 m(i,j)-m(r,j)*n
ST R5,0(R4) m(i,j)=m(i,j)-m(r,j)*n
LA R9,1(R9) j=j+1
B LOOPJ3
ELOOPJ3 LA R8,1(R8) i=i+1
B LOOPI3
ELOOPI3 LA R10,1(R10) lead=lead+1
LA R7,1(R7) r=r+1
B LOOPR
ELOOPR LA R8,1
LOOPI4 CH R8,NROWS do i=1 to nrows
BH ELOOPI4
SR R10,R10 pgi=0
LA R9,1
LOOPJ4 CH R9,NCOLS do j=1 to ncols
BH ELOOPJ4
LR R1,R8 i
BCTR R1,0
MH R1,NCOLS
LR R6,R9 j
BCTR R6,0
AR R1,R6
SLA R1,2
L R6,M(R1) m(i,j)
LA R3,PG
AR R3,R10
XDECO R6,0(R3) edit m(i,j)
LA R10,12(10) pgi=pgi+12
LA R9,1(R9) j=j+1
B LOOPJ4
ELOOPJ4 XPRNT PG,48 print m(i,j)
LA R8,1(R8) i=i+1
B LOOPI4
ELOOPI4 XR R15,R15
BR R14
NROWS DC H'3'
NCOLS DC H'4'
M DC F'1',F'2',F'-1',F'-4'
DC F'2',F'3',F'-1',F'-11'
DC F'-2',F'0',F'-3',F'22'
PG DC CL48' '
YREGS
END RREF
{{out}}
1 0 0 -8
0 1 0 1
0 0 1 -2
ActionScript
_m being of type Vector.<Vector.
public function RREF():Matrix {
var lead:uint, i:uint, j:uint, r:uint = 0;
for(r = 0; r < rows; r++) {
if(columns <= lead)
break;
i = r;
while(_m[i][lead] == 0) {
i++;
if(rows == i) {
i = r;
lead++;
if(columns == lead)
return this;
}
}
rowSwitch(i, r);
var val:Number = _m[r][lead];
for(j = 0; j < columns; j++)
_m[r][j] /= val;
for(i = 0; i < rows; i++) {
if(i == r)
continue;
val = _m[i][lead];
for(j = 0; j < columns; j++)
_m[i][j] -= val * _m[r][j];
}
lead++;
}
return this;
}
Ada
matrices.ads:
generic
type Element_Type is private;
Zero : Element_Type;
with function "-" (Left, Right : in Element_Type) return Element_Type is <>;
with function "*" (Left, Right : in Element_Type) return Element_Type is <>;
with function "/" (Left, Right : in Element_Type) return Element_Type is <>;
package Matrices is
type Matrix is
array (Positive range <>, Positive range <>) of Element_Type;
function Reduced_Row_Echelon_form (Source : Matrix) return Matrix;
end Matrices;
matrices.adb:
package body Matrices is
procedure Swap_Rows (From : in out Matrix; First, Second : in Positive) is
Temporary : Element_Type;
begin
for Col in From'Range (2) loop
Temporary := From (First, Col);
From (First, Col) := From (Second, Col);
From (Second, Col) := Temporary;
end loop;
end Swap_Rows;
procedure Divide_Row
(From : in out Matrix;
Row : in Positive;
Divisor : in Element_Type)
is
begin
for Col in From'Range (2) loop
From (Row, Col) := From (Row, Col) / Divisor;
end loop;
end Divide_Row;
procedure Subtract_Rows
(From : in out Matrix;
Subtrahend, Minuend : in Positive;
Factor : in Element_Type)
is
begin
for Col in From'Range (2) loop
From (Minuend, Col) := From (Minuend, Col) -
From (Subtrahend, Col) * Factor;
end loop;
end Subtract_Rows;
function Reduced_Row_Echelon_form (Source : Matrix) return Matrix is
Result : Matrix := Source;
Lead : Positive := Result'First (2);
I : Positive;
begin
Rows : for Row in Result'Range (1) loop
exit Rows when Lead > Result'Last (2);
I := Row;
while Result (I, Lead) = Zero loop
I := I + 1;
if I = Result'Last (1) then
I := Row;
Lead := Lead + 1;
exit Rows when Lead = Result'Last (2);
end if;
end loop;
if I /= Row then
Swap_Rows (From => Result, First => I, Second => Row);
end if;
Divide_Row
(From => Result,
Row => Row,
Divisor => Result (Row, Lead));
for Other_Row in Result'Range (1) loop
if Other_Row /= Row then
Subtract_Rows
(From => Result,
Subtrahend => Row,
Minuend => Other_Row,
Factor => Result (Other_Row, Lead));
end if;
end loop;
Lead := Lead + 1;
end loop Rows;
return Result;
end Reduced_Row_Echelon_form;
end Matrices;
Example use: main.adb:
with Matrices;
with Ada.Text_IO;
procedure Main is
package Float_IO is new Ada.Text_IO.Float_IO (Float);
package Float_Matrices is new Matrices (
Element_Type => Float,
Zero => 0.0);
procedure Print_Matrix (Matrix : in Float_Matrices.Matrix) is
begin
for Row in Matrix'Range (1) loop
for Col in Matrix'Range (2) loop
Float_IO.Put (Matrix (Row, Col), 0, 0, 0);
Ada.Text_IO.Put (' ');
end loop;
Ada.Text_IO.New_Line;
end loop;
end Print_Matrix;
My_Matrix : Float_Matrices.Matrix :=
((1.0, 2.0, -1.0, -4.0),
(2.0, 3.0, -1.0, -11.0),
(-2.0, 0.0, -3.0, 22.0));
Reduced : Float_Matrices.Matrix :=
Float_Matrices.Reduced_Row_Echelon_form (My_Matrix);
begin
Print_Matrix (My_Matrix);
Ada.Text_IO.Put_Line ("reduced to:");
Print_Matrix (Reduced);
end Main;
{{out}}
1.0 2.0 -1.0 -4.0
2.0 3.0 -1.0 -11.0
-2.0 0.0 -3.0 22.0
reduced to:
1.0 0.0 0.0 -8.0
-0.0 1.0 0.0 1.0
-0.0 -0.0 1.0 -2.0
Aime
rref(list l, integer rows, columns)
{
integer e, f, i, j, lead, r;
list u, v;
lead = r = 0;
while (r < rows && lead < columns) {
i = r;
while (!l.q_list(i)[lead]) {
i += 1;
if (i == rows) {
i = r;
lead += 1;
if (lead == columns) {
break;
}
}
}
if (lead == columns) {
break;
}
u = l[i];
l.spin(i, r);
e = u[lead];
if (e) {
for (j, f in u) {
u[j] = f / e;
}
}
for (i, v in l) {
if (i != r) {
e = v[lead];
for (j, f in v) {
v[j] = f - u[j] * e;
}
}
}
lead += 1;
r += 1;
}
}
display_2(list l)
{
for (, list u in l) {
u.ucall(o_winteger, -1, 4);
o_byte('\n');
}
}
main(void)
{
list l;
l = list(list(1, 2, -1, -4),
list(2, 3, -1, -11),
list(-2, 0, -3, 22));
rref(l, 3, 4);
display_2(l);
0;
}
{{Out}}
1 0 0 -8
0 1 0 1
0 0 1 -2
ALGOL 68
{{trans|Python}} {{works with|ALGOL 68|Standard - no extensions to language used}} {{works with|ALGOL 68G|Any - tested with release mk15-0.8b.fc9.i386}}
MODE FIELD = REAL; # FIELD can be REAL, LONG REAL etc, or COMPL, FRAC etc #
MODE VEC = [0]FIELD;
MODE MAT = [0,0]FIELD;
PROC to reduced row echelon form = (REF MAT m)VOID: (
INT lead col := 2 LWB m;
FOR this row FROM LWB m TO UPB m DO
IF lead col > 2 UPB m THEN return FI;
INT other row := this row;
WHILE m[other row,lead col] = 0 DO
other row +:= 1;
IF other row > UPB m THEN
other row := this row;
lead col +:= 1;
IF lead col > 2 UPB m THEN return FI
FI
OD;
IF this row /= other row THEN
VEC swap = m[this row,lead col:];
m[this row,lead col:] := m[other row,lead col:];
m[other row,lead col:] := swap
FI;
FIELD scale = 1/m[this row,lead col];
IF scale /= 1 THEN
m[this row,lead col] := 1;
FOR col FROM lead col+1 TO 2 UPB m DO m[this row,col] *:= scale OD
FI;
FOR other row FROM LWB m TO UPB m DO
IF this row /= other row THEN
REAL scale = m[other row,lead col];
m[other row,lead col]:=0;
FOR col FROM lead col+1 TO 2 UPB m DO m[other row,col] -:= scale*m[this row,col] OD
FI
OD;
lead col +:= 1
OD;
return: EMPTY
);
[3,4]FIELD mat := (
( 1, 2, -1, -4),
( 2, 3, -1, -11),
(-2, 0, -3, 22)
);
to reduced row echelon form( mat );
FORMAT
real repr = $g(-7,4)$,
vec repr = $"("n(2 UPB mat-1)(f(real repr)", ")f(real repr)")"$,
mat repr = $"("n(1 UPB mat-1)(f(vec repr)", "lx)f(vec repr)")"$;
printf((mat repr, mat, $l$))
{{out}}
(( 1.0000, 0.0000, 0.0000, -8.0000),
( 0.0000, 1.0000, 0.0000, 1.0000),
( 0.0000, 0.0000, 1.0000, -2.0000))
AutoIt
Global $ivMatrix[3][4] = [[1, 2, -1, -4],[2, 3, -1, -11],[-2, 0, -3, 22]]
ToReducedRowEchelonForm($ivMatrix)
Func ToReducedRowEchelonForm($matrix)
Local $clonematrix, $i
Local $lead = 0
Local $rowCount = UBound($matrix) - 1
Local $columnCount = UBound($matrix, 2) - 1
For $r = 0 To $rowCount
If $columnCount = $lead Then ExitLoop
$i = $r
While $matrix[$i][$lead] = 0
$i += 1
If $rowCount = $i Then
$i = $r
$lead += 1
If $columnCount = $lead Then ExitLoop
EndIf
WEnd
; There´s no built in Function to swap Rows of a 2-Dimensional Array
; We need to clone our matrix to swap complete lines
$clonematrix = $matrix ; Swap Lines, no
For $s = 0 To $columnCount
$matrix[$r][$s] = $clonematrix[$i][$s]
$matrix[$i][$s] = $clonematrix[$r][$s]
Next
Local $m = $matrix[$r][$lead]
For $k = 0 To $columnCount
$matrix[$r][$k] = $matrix[$r][$k] / $m
Next
For $i = 0 To $rowCount
If $i <> $r Then
Local $m = $matrix[$i][$lead]
For $k = 0 To $columnCount
$matrix[$i][$k] -= $m * $matrix[$r][$k]
Next
EndIf
Next
$lead += 1
Next
; Console Output
For $i = 0 To $rowCount
ConsoleWrite("[")
For $k = 0 To $columnCount
ConsoleWrite($matrix[$i][$k])
If $k <> $columnCount Then ConsoleWrite(",")
Next
ConsoleWrite("]" & @CRLF)
Next
; End of Console Output
Return $matrix
EndFunc ;==>ToReducedRowEchelonForm
{{out}}
[1,0,0,-8]
[-0,1,0,1]
[-0,-0,1,-2]
BBC BASIC
{{works with|BBC BASIC for Windows}}
DIM matrix(2,3)
matrix() = 1, 2, -1, -4, \
\ 2, 3, -1, -11, \
\ -2, 0, -3, 22
PROCrref(matrix())
FOR row% = 0 TO 2
FOR col% = 0 TO 3
PRINT matrix(row%,col%);
NEXT
PRINT
NEXT row%
END
DEF PROCrref(m())
LOCAL lead%, nrows%, ncols%, i%, j%, r%, n
nrows% = DIM(m(),1)+1
ncols% = DIM(m(),2)+1
FOR r% = 0 TO nrows%-1
IF lead% >= ncols% EXIT FOR
i% = r%
WHILE m(i%,lead%) = 0
i% += 1
IF i% = nrows% THEN
i% = r%
lead% += 1
IF lead% = ncols% EXIT FOR
ENDIF
ENDWHILE
FOR j% = 0 TO ncols%-1 : SWAP m(i%,j%),m(r%,j%) : NEXT
n = m(r%,lead%)
IF n <> 0 FOR j% = 0 TO ncols%-1 : m(r%,j%) /= n : NEXT
FOR i% = 0 TO nrows%-1
IF i% <> r% THEN
n = m(i%,lead%)
FOR j% = 0 TO ncols%-1
m(i%,j%) -= m(r%,j%) * n
NEXT
ENDIF
NEXT
lead% += 1
NEXT r%
ENDPROC
{{out}}
1 0 0 -8
0 1 0 1
0 0 1 -2
C
#include <stdio.h>
#define TALLOC(n,typ) malloc(n*sizeof(typ))
#define EL_Type int
typedef struct sMtx {
int dim_x, dim_y;
EL_Type *m_stor;
EL_Type **mtx;
} *Matrix, sMatrix;
typedef struct sRvec {
int dim_x;
EL_Type *m_stor;
} *RowVec, sRowVec;
Matrix NewMatrix( int x_dim, int y_dim )
{
int n;
Matrix m;
m = TALLOC( 1, sMatrix);
n = x_dim * y_dim;
m->dim_x = x_dim;
m->dim_y = y_dim;
m->m_stor = TALLOC(n, EL_Type);
m->mtx = TALLOC(m->dim_y, EL_Type *);
for(n=0; n<y_dim; n++) {
m->mtx[n] = m->m_stor+n*x_dim;
}
return m;
}
void MtxSetRow(Matrix m, int irow, EL_Type *v)
{
int ix;
EL_Type *mr;
mr = m->mtx[irow];
for(ix=0; ix<m->dim_x; ix++)
mr[ix] = v[ix];
}
Matrix InitMatrix( int x_dim, int y_dim, EL_Type **v)
{
Matrix m;
int iy;
m = NewMatrix(x_dim, y_dim);
for (iy=0; iy<y_dim; iy++)
MtxSetRow(m, iy, v[iy]);
return m;
}
void MtxDisplay( Matrix m )
{
int iy, ix;
const char *sc;
for (iy=0; iy<m->dim_y; iy++) {
printf(" ");
sc = " ";
for (ix=0; ix<m->dim_x; ix++) {
printf("%s %3d", sc, m->mtx[iy][ix]);
sc = ",";
}
printf("\n");
}
printf("\n");
}
void MtxMulAndAddRows(Matrix m, int ixrdest, int ixrsrc, EL_Type mplr)
{
int ix;
EL_Type *drow, *srow;
drow = m->mtx[ixrdest];
srow = m->mtx[ixrsrc];
for (ix=0; ix<m->dim_x; ix++)
drow[ix] += mplr * srow[ix];
// printf("Mul row %d by %d and add to row %d\n", ixrsrc, mplr, ixrdest);
// MtxDisplay(m);
}
void MtxSwapRows( Matrix m, int rix1, int rix2)
{
EL_Type *r1, *r2, temp;
int ix;
if (rix1 == rix2) return;
r1 = m->mtx[rix1];
r2 = m->mtx[rix2];
for (ix=0; ix<m->dim_x; ix++)
temp = r1[ix]; r1[ix]=r2[ix]; r2[ix]=temp;
// printf("Swap rows %d and %d\n", rix1, rix2);
// MtxDisplay(m);
}
void MtxNormalizeRow( Matrix m, int rix, int lead)
{
int ix;
EL_Type *drow;
EL_Type lv;
drow = m->mtx[rix];
lv = drow[lead];
for (ix=0; ix<m->dim_x; ix++)
drow[ix] /= lv;
// printf("Normalize row %d\n", rix);
// MtxDisplay(m);
}
#define MtxGet( m, rix, cix ) m->mtx[rix][cix]
void MtxToReducedREForm(Matrix m)
{
int lead;
int rix, iix;
EL_Type lv;
int rowCount = m->dim_y;
lead = 0;
for (rix=0; rix<rowCount; rix++) {
if (lead >= m->dim_x)
return;
iix = rix;
while (0 == MtxGet(m, iix,lead)) {
iix++;
if (iix == rowCount) {
iix = rix;
lead++;
if (lead == m->dim_x)
return;
}
}
MtxSwapRows(m, iix, rix );
MtxNormalizeRow(m, rix, lead );
for (iix=0; iix<rowCount; iix++) {
if ( iix != rix ) {
lv = MtxGet(m, iix, lead );
MtxMulAndAddRows(m,iix, rix, -lv) ;
}
}
lead++;
}
}
int main()
{
Matrix m1;
static EL_Type r1[] = {1,2,-1,-4};
static EL_Type r2[] = {2,3,-1,-11};
static EL_Type r3[] = {-2,0,-3,22};
static EL_Type *im[] = { r1, r2, r3 };
m1 = InitMatrix( 4,3, im );
printf("Initial\n");
MtxDisplay(m1);
MtxToReducedREForm(m1);
printf("Reduced R-E form\n");
MtxDisplay(m1);
return 0;
}
C++
Note: This code is written in generic form. While it slightly complicates the code, it allows to use the same code for both built-in arrays and matrix classes. To use it with a matrix class, either program the matrix class to the specifications given in the matrix_traits comment, or specialize matrix_traits for the specific interface of your matrix class.
The test code uses a built-in array for the matrix.
{{works with|g++|4.1.2 20061115 (prerelease) (Debian 4.1.1-21)}}
#include <algorithm> // for std::swap
#include <cstddef>
#include <cassert>
// Matrix traits: This describes how a matrix is accessed. By
// externalizing this information into a traits class, the same code
// can be used both with native arrays and matrix classes. To use the
// default implementation of the traits class, a matrix type has to
// provide the following definitions as members:
//
// * typedef ... index_type;
// - The type used for indexing (e.g. size_t)
// * typedef ... value_type;
// - The element type of the matrix (e.g. double)
// * index_type min_row() const;
// - returns the minimal allowed row index
// * index_type max_row() const;
// - returns the maximal allowed row index
// * index_type min_column() const;
// - returns the minimal allowed column index
// * index_type max_column() const;
// - returns the maximal allowed column index
// * value_type& operator()(index_type i, index_type k)
// - returns a reference to the element i,k, where
// min_row() <= i <= max_row()
// min_column() <= k <= max_column()
// * value_type operator()(index_type i, index_type k) const
// - returns the value of element i,k
//
// Note that the functions are all inline and simple, so the compiler
// should completely optimize them away.
template<typename MatrixType> struct matrix_traits
{
typedef typename MatrixType::index_type index_type;
typedef typename MatrixType::value_type value_type;
static index_type min_row(MatrixType const& A)
{ return A.min_row(); }
static index_type max_row(MatrixType const& A)
{ return A.max_row(); }
static index_type min_column(MatrixType const& A)
{ return A.min_column(); }
static index_type max_column(MatrixType const& A)
{ return A.max_column(); }
static value_type& element(MatrixType& A, index_type i, index_type k)
{ return A(i,k); }
static value_type element(MatrixType const& A, index_type i, index_type k)
{ return A(i,k); }
};
// specialization of the matrix traits for built-in two-dimensional
// arrays
template<typename T, std::size_t rows, std::size_t columns>
struct matrix_traits<T[rows][columns]>
{
typedef std::size_t index_type;
typedef T value_type;
static index_type min_row(T const (&)[rows][columns])
{ return 0; }
static index_type max_row(T const (&)[rows][columns])
{ return rows-1; }
static index_type min_column(T const (&)[rows][columns])
{ return 0; }
static index_type max_column(T const (&)[rows][columns])
{ return columns-1; }
static value_type& element(T (&A)[rows][columns],
index_type i, index_type k)
{ return A[i][k]; }
static value_type element(T const (&A)[rows][columns],
index_type i, index_type k)
{ return A[i][k]; }
};
// Swap rows i and k of a matrix A
// Note that due to the reference, both dimensions are preserved for
// built-in arrays
template<typename MatrixType>
void swap_rows(MatrixType& A,
typename matrix_traits<MatrixType>::index_type i,
typename matrix_traits<MatrixType>::index_type k)
{
matrix_traits<MatrixType> mt;
typedef typename matrix_traits<MatrixType>::index_type index_type;
// check indices
assert(mt.min_row(A) <= i);
assert(i <= mt.max_row(A));
assert(mt.min_row(A) <= k);
assert(k <= mt.max_row(A));
for (index_type col = mt.min_column(A); col <= mt.max_column(A); ++col)
std::swap(mt.element(A, i, col), mt.element(A, k, col));
}
// divide row i of matrix A by v
template<typename MatrixType>
void divide_row(MatrixType& A,
typename matrix_traits<MatrixType>::index_type i,
typename matrix_traits<MatrixType>::value_type v)
{
matrix_traits<MatrixType> mt;
typedef typename matrix_traits<MatrixType>::index_type index_type;
assert(mt.min_row(A) <= i);
assert(i <= mt.max_row(A));
assert(v != 0);
for (index_type col = mt.min_column(A); col <= mt.max_column(A); ++col)
mt.element(A, i, col) /= v;
}
// in matrix A, add v times row k to row i
template<typename MatrixType>
void add_multiple_row(MatrixType& A,
typename matrix_traits<MatrixType>::index_type i,
typename matrix_traits<MatrixType>::index_type k,
typename matrix_traits<MatrixType>::value_type v)
{
matrix_traits<MatrixType> mt;
typedef typename matrix_traits<MatrixType>::index_type index_type;
assert(mt.min_row(A) <= i);
assert(i <= mt.max_row(A));
assert(mt.min_row(A) <= k);
assert(k <= mt.max_row(A));
for (index_type col = mt.min_column(A); col <= mt.max_column(A); ++col)
mt.element(A, i, col) += v * mt.element(A, k, col);
}
// convert A to reduced row echelon form
template<typename MatrixType>
void to_reduced_row_echelon_form(MatrixType& A)
{
matrix_traits<MatrixType> mt;
typedef typename matrix_traits<MatrixType>::index_type index_type;
index_type lead = mt.min_row(A);
for (index_type row = mt.min_row(A); row <= mt.max_row(A); ++row)
{
if (lead > mt.max_column(A))
return;
index_type i = row;
while (mt.element(A, i, lead) == 0)
{
++i;
if (i > mt.max_row(A))
{
i = row;
++lead;
if (lead > mt.max_column(A))
return;
}
}
swap_rows(A, i, row);
divide_row(A, row, mt.element(A, row, lead));
for (i = mt.min_row(A); i <= mt.max_row(A); ++i)
{
if (i != row)
add_multiple_row(A, i, row, -mt.element(A, i, lead));
}
}
}
// test code
#include <iostream>
int main()
{
double M[3][4] = { { 1, 2, -1, -4 },
{ 2, 3, -1, -11 },
{ -2, 0, -3, 22 } };
to_reduced_row_echelon_form(M);
for (int i = 0; i < 3; ++i)
{
for (int j = 0; j < 4; ++j)
std::cout << M[i][j] << '\t';
std::cout << "\n";
}
return EXIT_SUCCESS;
}
{{out}}
1 0 0 -8
-0 1 0 1
-0 -0 1 -2
C#
using System;
namespace rref
{
class Program
{
static void Main(string[] args)
{
int[,] matrix = new int[3, 4]{
{ 1, 2, -1, -4 },
{ 2, 3, -1, -11 },
{ -2, 0, -3, 22 }
};
matrix = rref(matrix);
}
private static int[,] rref(int[,] matrix)
{
int lead = 0, rowCount = matrix.GetLength(0), columnCount = matrix.GetLength(1);
for (int r = 0; r < rowCount; r++)
{
if (columnCount <= lead) break;
int i = r;
while (matrix[i, lead] == 0)
{
i++;
if (i == rowCount)
{
i = r;
lead++;
if (columnCount == lead)
{
lead--;
break;
}
}
}
for (int j = 0; j < columnCount; j++)
{
int temp = matrix[r, j];
matrix[r, j] = matrix[i, j];
matrix[i, j] = temp;
}
int div = matrix[r, lead];
if(div != 0)
for (int j = 0; j < columnCount; j++) matrix[r, j] /= div;
for (int j = 0; j < rowCount; j++)
{
if (j != r)
{
int sub = matrix[j, lead];
for (int k = 0; k < columnCount; k++) matrix[j, k] -= (sub * matrix[r, k]);
}
}
lead++;
}
return matrix;
}
}
}
Common Lisp
Direct implementation of the pseudo-code given.
(defun convert-to-row-echelon-form (matrix)
(let* ((dimensions (array-dimensions matrix))
(row-count (first dimensions))
(column-count (second dimensions))
(lead 0))
(labels ((find-pivot (start lead)
(let ((i start))
(loop
:while (zerop (aref matrix i lead))
:do (progn
(incf i)
(when (= i row-count)
(setf i start)
(incf lead)
(when (= lead column-count)
(return-from convert-to-row-echelon-form matrix))))
:finally (return (values i lead)))))
(swap-rows (r1 r2)
(loop
:for c :upfrom 0 :below column-count
:do (rotatef (aref matrix r1 c) (aref matrix r2 c))))
(divide-row (r value)
(loop
:for c :upfrom 0 :below column-count
:do (setf (aref matrix r c)
(/ (aref matrix r c) value)))))
(loop
:for r :upfrom 0 :below row-count
:when (<= column-count lead)
:do (return matrix)
:do (multiple-value-bind (i nlead) (find-pivot r lead)
(setf lead nlead)
(swap-rows i r)
(divide-row r (aref matrix r lead))
(loop
:for i :upfrom 0 :below row-count
:when (/= i r)
:do (let ((scale (aref matrix i lead)))
(loop
:for c :upfrom 0 :below column-count
:do (decf (aref matrix i c)
(* scale (aref matrix r c))))))
(incf lead))
:finally (return matrix)))))
D
import std.stdio, std.algorithm, std.array, std.conv;
void toReducedRowEchelonForm(T)(T[][] M) pure nothrow @nogc {
if (M.empty)
return;
immutable nrows = M.length;
immutable ncols = M[0].length;
size_t lead;
foreach (immutable r; 0 .. nrows) {
if (ncols <= lead)
return;
{
size_t i = r;
while (M[i][lead] == 0) {
i++;
if (nrows == i) {
i = r;
lead++;
if (ncols == lead)
return;
}
}
swap(M[i], M[r]);
}
M[r][] /= M[r][lead];
foreach (j, ref mj; M)
if (j != r)
mj[] -= M[r][] * mj[lead];
lead++;
}
}
void main() {
auto A = [[ 1, 2, -1, -4],
[ 2, 3, -1, -11],
[-2, 0, -3, 22]];
A.toReducedRowEchelonForm;
writefln("%(%(%2d %)\n%)", A);
}
{{out}}
1 0 0 -8
0 1 0 1
0 0 1 -2
Euphoria
function ToReducedRowEchelonForm(sequence M)
integer lead,rowCount,columnCount,i
sequence temp
lead = 1
rowCount = length(M)
columnCount = length(M[1])
for r = 1 to rowCount do
if columnCount <= lead then
exit
end if
i = r
while M[i][lead] = 0 do
i += 1
if rowCount = i then
i = r
lead += 1
if columnCount = lead then
exit
end if
end if
end while
temp = M[i]
M[i] = M[r]
M[r] = temp
M[r] /= M[r][lead]
for j = 1 to rowCount do
if j != r then
M[j] -= M[j][lead]*M[r]
end if
end for
lead += 1
end for
return M
end function
? ToReducedRowEchelonForm(
{ { 1, 2, -1, -4 },
{ 2, 3, -1, -11 },
{ -2, 0, -3, 22 } })
{{out}}
{
{1,0,0,-8},
{0,1,0,1},
{0,0,1,-2}
}
Factor
USE: math.matrices.elimination
{ { 1 2 -1 -4 } { 2 3 -1 -11 } { -2 0 -3 22 } } solution .
{{out}}
{ { 1 0 0 -8 } { 0 1 0 1 } { 0 0 1 -2 } }
Fortran
module Rref
implicit none
contains
subroutine to_rref(matrix)
real, dimension(:,:), intent(inout) :: matrix
integer :: pivot, norow, nocolumn
integer :: r, i
real, dimension(:), allocatable :: trow
pivot = 1
norow = size(matrix, 1)
nocolumn = size(matrix, 2)
allocate(trow(nocolumn))
do r = 1, norow
if ( nocolumn <= pivot ) exit
i = r
do while ( matrix(i, pivot) == 0 )
i = i + 1
if ( norow == i ) then
i = r
pivot = pivot + 1
if ( nocolumn == pivot ) return
end if
end do
trow = matrix(i, :)
matrix(i, :) = matrix(r, :)
matrix(r, :) = trow
matrix(r, :) = matrix(r, :) / matrix(r, pivot)
do i = 1, norow
if ( i /= r ) matrix(i, :) = matrix(i, :) - matrix(r, :) * matrix(i, pivot)
end do
pivot = pivot + 1
end do
deallocate(trow)
end subroutine to_rref
end module Rref
program prg_test
use rref
implicit none
real, dimension(3, 4) :: m = reshape( (/ 1, 2, -1, -4, &
2, 3, -1, -11, &
-2, 0, -3, 22 /), &
(/ 3, 4 /), order = (/ 2, 1 /) )
integer :: i
print *, "Original matrix"
do i = 1, size(m,1)
print *, m(i, :)
end do
call to_rref(m)
print *, "Reduced row echelon form"
do i = 1, size(m,1)
print *, m(i, :)
end do
end program prg_test
Go
2D representation
From WP pseudocode:
package main
import "fmt"
type matrix [][]float64
func (m matrix) print() {
for _, r := range m {
fmt.Println(r)
}
fmt.Println("")
}
func main() {
m := matrix{
{ 1, 2, -1, -4},
{ 2, 3, -1, -11},
{-2, 0, -3, 22},
}
m.print()
rref(m)
m.print()
}
func rref(m matrix) {
lead := 0
rowCount := len(m)
columnCount := len(m[0])
for r := 0; r < rowCount; r++ {
if lead >= columnCount {
return
}
i := r
for m[i][lead] == 0 {
i++
if rowCount == i {
i = r
lead++
if columnCount == lead {
return
}
}
}
m[i], m[r] = m[r], m[i]
f := 1 / m[r][lead]
for j, _ := range m[r] {
m[r][j] *= f
}
for i = 0; i < rowCount; i++ {
if i != r {
f = m[i][lead]
for j, e := range m[r] {
m[i][j] -= e * f
}
}
}
lead++
}
}
{{out}} (not so pretty, sorry)
[1 2 -1 -4]
[2 3 -1 -11]
[-2 0 -3 22]
[1 0 0 -8]
[-0 1 0 1]
[-0 -0 1 -2]
Flat representation
package main
import "fmt"
type matrix struct {
ele []float64
stride int
}
func matrixFromRows(rows [][]float64) *matrix {
if len(rows) == 0 {
return &matrix{nil, 0}
}
m := &matrix{make([]float64, len(rows)*len(rows[0])), len(rows[0])}
for rx, row := range rows {
copy(m.ele[rx*m.stride:(rx+1)*m.stride], row)
}
return m
}
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("%6.2f ", m.ele[e:e+m.stride])
fmt.Println()
}
}
func (m *matrix) rref() {
lead := 0
for rxc0 := 0; rxc0 < len(m.ele); rxc0 += m.stride {
if lead >= m.stride {
return
}
ixc0 := rxc0
for m.ele[ixc0+lead] == 0 {
ixc0 += m.stride
if ixc0 == len(m.ele) {
ixc0 = rxc0
lead++
if lead == m.stride {
return
}
}
}
for c, ix, rx := 0, ixc0, rxc0; c < m.stride; c++ {
m.ele[ix], m.ele[rx] = m.ele[rx], m.ele[ix]
ix++
rx++
}
if d := m.ele[rxc0+lead]; d != 0 {
d := 1 / d
for c, rx := 0, rxc0; c < m.stride; c++ {
m.ele[rx] *= d
rx++
}
}
for ixc0 = 0; ixc0 < len(m.ele); ixc0 += m.stride {
if ixc0 != rxc0 {
f := m.ele[ixc0+lead]
for c, ix, rx := 0, ixc0, rxc0; c < m.stride; c++ {
m.ele[ix] -= m.ele[rx] * f
ix++
rx++
}
}
}
lead++
}
}
func main() {
m := matrixFromRows([][]float64{
{1, 2, -1, -4},
{2, 3, -1, -11},
{-2, 0, -3, 22},
})
m.print("Input:")
m.rref()
m.print("Reduced:")
}
{{out}}
Input:
[ 1.00 2.00 -1.00 -4.00]
[ 2.00 3.00 -1.00 -11.00]
[ -2.00 0.00 -3.00 22.00]
Reduced:
[ 1.00 0.00 0.00 -8.00]
[ -0.00 1.00 0.00 1.00]
[ -0.00 -0.00 1.00 -2.00]
Groovy
This solution implements the transformation to reduced row echelon form with optional pivoting. Options are provided for both ''partial pivoting'' and ''scaled partial pivoting''. The default option is no pivoting at all.
enum Pivoting {
NONE({ i, it -> 1 }),
PARTIAL({ i, it -> - (it[i].abs()) }),
SCALED({ i, it -> - it[i].abs()/(it.inject(0) { sum, elt -> sum + elt.abs() } ) });
public final Closure comparer
private Pivoting(Closure c) {
comparer = c
}
}
def isReducibleMatrix = { matrix ->
def m = matrix.size()
m > 1 && matrix[0].size() > m && matrix[1..<m].every { row -> row.size() == matrix[0].size() }
}
def reducedRowEchelonForm = { matrix, Pivoting pivoting = Pivoting.NONE ->
assert isReducibleMatrix(matrix)
def m = matrix.size()
def n = matrix[0].size()
(0..<m).each { i ->
matrix[i..<m].sort(pivoting.comparer.curry(i))
matrix[i][i..<n] = matrix[i][i..<n].collect { it/matrix[i][i] }
((0..<i) + ((i+1)..<m)).each { k ->
(i..<n).reverse().each { j ->
matrix[k][j] -= matrix[i][j]*matrix[k][i]
}
}
}
matrix
}
This test first demonstrates the test case provided, and then demonstrates another test case designed to show the dangers of not using pivoting on an otherwise solvable matrix. Both test cases exercise all three pivoting options.
def matrixCopy = { matrix -> matrix.collect { row -> row.collect { it } } }
println "Tests for matrix A:"
def a = [
[1, 2, -1, -4],
[2, 3, -1, -11],
[-2, 0, -3, 22]
]
a.each { println it }
println()
println "pivoting == Pivoting.NONE"
reducedRowEchelonForm(matrixCopy(a)).each { println it }
println()
println "pivoting == Pivoting.PARTIAL"
reducedRowEchelonForm(matrixCopy(a), Pivoting.PARTIAL).each { println it }
println()
println "pivoting == Pivoting.SCALED"
reducedRowEchelonForm(matrixCopy(a), Pivoting.SCALED).each { println it }
println()
println "Tests for matrix B (divides by 0 without pivoting):"
def b = [
[1, 2, -1, -4],
[2, 4, -1, -11],
[-2, 0, -6, 24]
]
b.each { println it }
println()
println "pivoting == Pivoting.NONE"
try {
reducedRowEchelonForm(matrixCopy(b)).each { println it }
println()
} catch (e) {
println "KABOOM! ${e.message}"
println()
}
println "pivoting == Pivoting.PARTIAL"
reducedRowEchelonForm(matrixCopy(b), Pivoting.PARTIAL).each { println it }
println()
println "pivoting == Pivoting.SCALED"
reducedRowEchelonForm(matrixCopy(b), Pivoting.SCALED).each { println it }
println()
{{out}}
Tests for matrix A:
[1, 2, -1, -4]
[2, 3, -1, -11]
[-2, 0, -3, 22]
pivoting == Pivoting.NONE
[1, 0, 0, -8]
[0, 1, 0, 1]
[0, 0, 1, -2]
pivoting == Pivoting.PARTIAL
[1, 0.0, 0E-11, -7.9999999997000000000150]
[0, 1, 0E-10, 0.999999999700000000010]
[0, 0.0, 1, -2.00000000030]
pivoting == Pivoting.SCALED
[1, 0, 0, -8]
[0, 1, 0, 1]
[0, 0, 1, -2]
Tests for matrix B (divides by 0 without pivoting):
[1, 2, -1, -4]
[2, 4, -1, -11]
[-2, 0, -6, 24]
pivoting == Pivoting.NONE
KABOOM! Division undefined
pivoting == Pivoting.PARTIAL
[1, 0, 0.00, -3.00]
[0, 1, 0.00, -2.00]
[0, 0, 1, -3]
pivoting == Pivoting.SCALED
[1, 0, 0, -3]
[0, 1, 0, -2]
[0, 0, 1, -3]
Haskell
This program was produced by translating from the Python and gradually refactoring the result into a more functional style.
import Data.List (find)
rref :: Fractional a => [[a]] -> [[a]]
rref m = f m 0 [0 .. rows - 1]
where rows = length m
cols = length $ head m
f m _ [] = m
f m lead (r : rs)
| indices == Nothing = m
| otherwise = f m' (lead' + 1) rs
where indices = find p l
p (col, row) = m !! row !! col /= 0
l = [(col, row) |
col <- [lead .. cols - 1],
row <- [r .. rows - 1]]
Just (lead', i) = indices
newRow = map (/ m !! i !! lead') $ m !! i
m' = zipWith g [0..] $
replace r newRow $
replace i (m !! r) m
g n row
| n == r = row
| otherwise = zipWith h newRow row
where h = subtract . (* row !! lead')
replace :: Int -> a -> [a] -> [a]
{- Replaces the element at the given index. -}
replace n e l = a ++ e : b
where (a, _ : b) = splitAt n l
=={{header|Icon}} and {{header|Unicon}}==
Works in both languages:
procedure main(A)
tM := [[ 1, 2, -1, -4],
[ 2, 3, -1,-11],
[ -2, 0, -3, 22]]
showMat(rref(tM))
end
procedure rref(M)
lead := 1
rCount := *\M | stop("no Matrix?")
cCount := *(M[1]) | 0
every r := !rCount do {
i := r
while M[i,lead] = 0 do {
if (i+:=1) > rCount then {
i := r
if cCount < (lead +:= 1) then stop("can't reduce")
}
}
M[i] :=: M[r]
if 0 ~= (m0 := M[r,lead]) then every !M[r] /:= real(m0)
every r ~= (i := !rCount) do {
every !(mr := copy(M[r])) *:= M[i,lead]
every M[i,j := !cCount] -:= mr[j]
}
lead +:= 1
}
return M
end
procedure showMat(M)
every r := !M do every writes(right(!r,5)||" " | "\n")
end
{{out}}
->rref
1.0 0.0 0.0 -8.0
0.0 1.0 0.0 1.0
0.0 0.0 1.0 -2.0
->
J
The reduced row echelon form of a matrix can be obtained using the gauss_jordan verb from the [http://www.jsoftware.com/wsvn/addons/trunk/math/misc/linear.ijs linear.ijs script], available as part of the math/misc addon. gauss_jordan and the verb pivot are shown below for completeness:
'''Solution:'''
NB.*pivot v Pivot at row, column
NB. form: (row,col) pivot M
pivot=: dyad define
'r c'=. x
col=. c{"1 y
y - (col - r = i.#y) */ (r{y) % r{col
)
NB.*gauss_jordan v Gauss-Jordan elimination (full pivoting)
NB. y is: matrix
NB. x is: optional minimum tolerance, default 1e_15.
NB. If a column below the current pivot has numbers of magnitude all
NB. less then x, it is treated as all zeros.
gauss_jordan=: verb define
1e_15 gauss_jordan y
:
mtx=. y
'r c'=. $mtx
rows=. i.r
i=. j=. 0
max=. i.>./
while. (i<r) *. j<c do.
k=. max col=. | i}. j{"1 mtx
if. 0 < x-k{col do. NB. if all col < tol, set to 0:
mtx=. 0 (<(i}.rows);j) } mtx
else. NB. otherwise sort and pivot:
if. k do.
mtx=. (<i,i+k) C. mtx
end.
mtx=. (i,j) pivot mtx
i=. >:i
end.
j=. >:j
end.
mtx
)
'''Usage:'''
require 'math/misc/linear'
]A=: 1 2 _1 _4 , 2 3 _1 _11 ,: _2 0 _3 22
1 2 _1 _4
2 3 _1 _11
_2 0 _3 22
gauss_jordan A
1 0 0 _8
0 1 0 1
0 0 1 _2
Additional examples, recommended on talk page:
gauss_jordan 2 0 _1 0 0,1 0 0 _1 0,3 0 0 _2 _1,0 1 0 0 _2,:0 1 _1 0 0
1 0 0 0 _1
0 1 0 0 _2
0 0 1 0 _2
0 0 0 1 _1
0 0 0 0 0
gauss_jordan 1 2 3 4 3 1,2 4 6 2 6 2,3 6 18 9 9 _6,4 8 12 10 12 4,:5 10 24 11 15 _4
1 2 0 0 3 0
0 0 1 0 0 0
0 0 0 1 0 0
0 0 0 0 0 1
0 0 0 0 0 0
gauss_jordan 0 1,1 2,:0 5
1 0
0 1
0 0
And:
mat=: 0 ". ];._2 noun define
1 0 0 0 0 0 1 0 0 0 0 _1 0 0 0 0 0 0
1 0 0 0 0 0 0 1 0 0 0 0 _1 0 0 0 0 0
1 0 0 0 0 0 0 0 1 0 0 0 0 _1 0 0 0 0
0 1 0 0 0 0 1 0 0 0 0 0 0 0 _1 0 0 0
0 1 0 0 0 0 0 0 1 0 0 _1 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 _1 0
0 0 1 0 0 0 1 0 0 0 0 0 _1 0 0 0 0 0
0 0 1 0 0 0 0 0 0 1 0 0 0 0 _1 0 0 0
0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 _1 0 0
0 0 0 1 0 0 0 0 0 1 0 0 _1 0 0 0 0 0
0 0 0 0 1 0 0 1 0 0 0 0 0 _1 0 0 0 0
0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 _1 0
0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 _1 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1
0 0 0 0 0 1 0 0 0 0 1 0 0 0 _1 0 0 0
)
gauss_jordan mat
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.435897
0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.307692
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.512821
0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.717949
0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0.487179
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0.205128
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0.282051
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0.333333
0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0.512821
0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0.641026
0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0.717949
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0.769231
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0.512821
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.820513
Java
''This requires Apache Commons 2.2+''
import java.util.*;
import java.lang.Math;
import org.apache.commons.math.fraction.Fraction;
import org.apache.commons.math.fraction.FractionConversionException;
/* Matrix class
* Handles elementary Matrix operations:
* Interchange
* Multiply and Add
* Scale
* Reduced Row Echelon Form
*/
class Matrix {
LinkedList<LinkedList<Fraction>> matrix;
int numRows;
int numCols;
static class Coordinate {
int row;
int col;
Coordinate(int r, int c) {
row = r;
col = c;
}
public String toString() {
return "(" + row + ", " + col + ")";
}
}
Matrix(double [][] m) {
numRows = m.length;
numCols = m[0].length;
matrix = new LinkedList<LinkedList<Fraction>>();
for (int i = 0; i < numRows; i++) {
matrix.add(new LinkedList<Fraction>());
for (int j = 0; j < numCols; j++) {
try {
matrix.get(i).add(new Fraction(m[i][j]));
} catch (FractionConversionException e) {
System.err.println("Fraction could not be converted from double by apache commons . . .");
}
}
}
}
public void Interchange(Coordinate a, Coordinate b) {
LinkedList<Fraction> temp = matrix.get(a.row);
matrix.set(a.row, matrix.get(b.row));
matrix.set(b.row, temp);
int t = a.row;
a.row = b.row;
b.row = t;
}
public void Scale(Coordinate x, Fraction d) {
LinkedList<Fraction> row = matrix.get(x.row);
for (int i = 0; i < numCols; i++) {
row.set(i, row.get(i).multiply(d));
}
}
public void MultiplyAndAdd(Coordinate to, Coordinate from, Fraction scalar) {
LinkedList<Fraction> row = matrix.get(to.row);
LinkedList<Fraction> rowMultiplied = matrix.get(from.row);
for (int i = 0; i < numCols; i++) {
row.set(i, row.get(i).add((rowMultiplied.get(i).multiply(scalar))));
}
}
public void RREF() {
Coordinate pivot = new Coordinate(0,0);
int submatrix = 0;
for (int x = 0; x < numCols; x++) {
pivot = new Coordinate(pivot.row, x);
//Step 1
//Begin with the leftmost nonzero column. This is a pivot column. The pivot position is at the top.
for (int i = x; i < numCols; i++) {
if (isColumnZeroes(pivot) == false) {
break;
} else {
pivot.col = i;
}
}
//Step 2
//Select a nonzero entry in the pivot column with the highest absolute value as a pivot.
pivot = findPivot(pivot);
if (getCoordinate(pivot).doubleValue() == 0.0) {
pivot.row++;
continue;
}
//If necessary, interchange rows to move this entry into the pivot position.
//move this row to the top of the submatrix
if (pivot.row != submatrix) {
Interchange(new Coordinate(submatrix, pivot.col), pivot);
}
//Force pivot to be 1
if (getCoordinate(pivot).doubleValue() != 1) {
/*
System.out.println(getCoordinate(pivot));
System.out.println(pivot);
System.out.println(matrix);
*/
Fraction scalar = getCoordinate(pivot).reciprocal();
Scale(pivot, scalar);
}
//Step 3
//Use row replacement operations to create zeroes in all positions below the pivot.
//belowPivot = belowPivot + (Pivot * -belowPivot)
for (int i = pivot.row; i < numRows; i++) {
if (i == pivot.row) {
continue;
}
Coordinate belowPivot = new Coordinate(i, pivot.col);
Fraction complement = (getCoordinate(belowPivot).negate().divide(getCoordinate(pivot)));
MultiplyAndAdd(belowPivot, pivot, complement);
}
//Step 5
//Beginning with the rightmost pivot and working upward and to the left, create zeroes above each pivot.
//If a pivot is not 1, make it 1 by a scaling operation.
//Use row replacement operations to create zeroes in all positions above the pivot
for (int i = pivot.row; i >= 0; i--) {
if (i == pivot.row) {
if (getCoordinate(pivot).doubleValue() != 1.0) {
Scale(pivot, getCoordinate(pivot).reciprocal());
}
continue;
}
if (i == pivot.row) {
continue;
}
Coordinate abovePivot = new Coordinate(i, pivot.col);
Fraction complement = (getCoordinate(abovePivot).negate().divide(getCoordinate(pivot)));
MultiplyAndAdd(abovePivot, pivot, complement);
}
//Step 4
//Ignore the row containing the pivot position and cover all rows, if any, above it.
//Apply steps 1-3 to the remaining submatrix. Repeat until there are no more nonzero entries.
if ((pivot.row + 1) >= numRows || isRowZeroes(new Coordinate(pivot.row+1, pivot.col))) {
break;
}
submatrix++;
pivot.row++;
}
}
public boolean isColumnZeroes(Coordinate a) {
for (int i = 0; i < numRows; i++) {
if (matrix.get(i).get(a.col).doubleValue() != 0.0) {
return false;
}
}
return true;
}
public boolean isRowZeroes(Coordinate a) {
for (int i = 0; i < numCols; i++) {
if (matrix.get(a.row).get(i).doubleValue() != 0.0) {
return false;
}
}
return true;
}
public Coordinate findPivot(Coordinate a) {
int first_row = a.row;
Coordinate pivot = new Coordinate(a.row, a.col);
Coordinate current = new Coordinate(a.row, a.col);
for (int i = a.row; i < (numRows - first_row); i++) {
current.row = i;
if (getCoordinate(current).doubleValue() == 1.0) {
Interchange(current, a);
}
}
current.row = a.row;
for (int i = current.row; i < (numRows - first_row); i++) {
current.row = i;
if (getCoordinate(current).doubleValue() != 0) {
pivot.row = i;
break;
}
}
return pivot;
}
public Fraction getCoordinate(Coordinate a) {
return matrix.get(a.row).get(a.col);
}
public String toString() {
return matrix.toString().replace("], ", "]\n");
}
public static void main (String[] args) {
double[][] matrix_1 = {
{1, 2, -1, -4},
{2, 3, -1, -11},
{-2, 0, -3, 22}
};
Matrix x = new Matrix(matrix_1);
System.out.println("before\n" + x.toString() + "\n");
x.RREF();
System.out.println("after\n" + x.toString() + "\n");
double matrix_2 [][] = {
{2, 0, -1, 0, 0},
{1, 0, 0, -1, 0},
{3, 0, 0, -2, -1},
{0, 1, 0, 0, -2},
{0, 1, -1, 0, 0}
};
Matrix y = new Matrix(matrix_2);
System.out.println("before\n" + y.toString() + "\n");
y.RREF();
System.out.println("after\n" + y.toString() + "\n");
double matrix_3 [][] = {
{1, 2, 3, 4, 3, 1},
{2, 4, 6, 2, 6, 2},
{3, 6, 18, 9, 9, -6},
{4, 8, 12, 10, 12, 4},
{5, 10, 24, 11, 15, -4}
};
Matrix z = new Matrix(matrix_3);
System.out.println("before\n" + z.toString() + "\n");
z.RREF();
System.out.println("after\n" + z.toString() + "\n");
double matrix_4 [][] = {
{0, 1},
{1, 2},
{0,5}
};
Matrix a = new Matrix(matrix_4);
System.out.println("before\n" + a.toString() + "\n");
a.RREF();
System.out.println("after\n" + a.toString() + "\n");
}
}
JavaScript
{{works with|SpiderMonkey}} for the print() function.
Extends the Matrix class defined at [[Matrix Transpose#JavaScript]]
// modifies the matrix in-place
Matrix.prototype.toReducedRowEchelonForm = function() {
var lead = 0;
for (var r = 0; r < this.rows(); r++) {
if (this.columns() <= lead) {
return;
}
var i = r;
while (this.mtx[i][lead] == 0) {
i++;
if (this.rows() == i) {
i = r;
lead++;
if (this.columns() == lead) {
return;
}
}
}
var tmp = this.mtx[i];
this.mtx[i] = this.mtx[r];
this.mtx[r] = tmp;
var val = this.mtx[r][lead];
for (var j = 0; j < this.columns(); j++) {
this.mtx[r][j] /= val;
}
for (var i = 0; i < this.rows(); i++) {
if (i == r) continue;
val = this.mtx[i][lead];
for (var j = 0; j < this.columns(); j++) {
this.mtx[i][j] -= val * this.mtx[r][j];
}
}
lead++;
}
return this;
}
var m = new Matrix([
[ 1, 2, -1, -4],
[ 2, 3, -1,-11],
[-2, 0, -3, 22]
]);
print(m.toReducedRowEchelonForm());
print();
m = new Matrix([
[ 1, 2, 3, 7],
[-4, 7,-2, 7],
[ 3, 3, 0, 7]
]);
print(m.toReducedRowEchelonForm());
{{out}}
1,0,0,-8
0,1,0,1
0,0,1,-2
1,0,0,0.6666666666666663
0,1,0,1.666666666666667
0,0,1,1
Julia
RowEchelon.jl offers the function rref to compute the reduced-row echelon form:
julia> matrix = [1 2 -1 -4 ; 2 3 -1 -11 ; -2 0 -3 22]
3x4 Int32 Array:
1 2 -1 -4
2 3 -1 -11
-2 0 -3 22
julia> rref(matrix)
3x4 Array{Float64,2}:
1.0 0.0 0.0 -8.0
0.0 1.0 0.0 1.0
0.0 0.0 1.0 -2.0
Kotlin
// version 1.1.51
typealias Matrix = Array<DoubleArray>
/* changes the matrix to RREF 'in place' */
fun Matrix.toReducedRowEchelonForm() {
var lead = 0
val rowCount = this.size
val colCount = this[0].size
for (r in 0 until rowCount) {
if (colCount <= lead) return
var i = r
while (this[i][lead] == 0.0) {
i++
if (rowCount == i) {
i = r
lead++
if (colCount == lead) return
}
}
val temp = this[i]
this[i] = this[r]
this[r] = temp
if (this[r][lead] != 0.0) {
val div = this[r][lead]
for (j in 0 until colCount) this[r][j] /= div
}
for (k in 0 until rowCount) {
if (k != r) {
val mult = this[k][lead]
for (j in 0 until colCount) this[k][j] -= this[r][j] * mult
}
}
lead++
}
}
fun Matrix.printf(title: String) {
println(title)
val rowCount = this.size
val colCount = this[0].size
for (r in 0 until rowCount) {
for (c in 0 until colCount) {
if (this[r][c] == -0.0) this[r][c] = 0.0 // get rid of negative zeros
print("${"% 6.2f".format(this[r][c])} ")
}
println()
}
println()
}
fun main(args: Array<String>) {
val matrices = listOf(
arrayOf(
doubleArrayOf( 1.0, 2.0, -1.0, -4.0),
doubleArrayOf( 2.0, 3.0, -1.0, -11.0),
doubleArrayOf(-2.0, 0.0, -3.0, 22.0)
),
arrayOf(
doubleArrayOf(1.0, 2.0, 3.0, 4.0, 3.0, 1.0),
doubleArrayOf(2.0, 4.0, 6.0, 2.0, 6.0, 2.0),
doubleArrayOf(3.0, 6.0, 18.0, 9.0, 9.0, -6.0),
doubleArrayOf(4.0, 8.0, 12.0, 10.0, 12.0, 4.0),
doubleArrayOf(5.0, 10.0, 24.0, 11.0, 15.0, -4.0)
)
)
for (m in matrices) {
m.printf("Original matrix:")
m.toReducedRowEchelonForm()
m.printf("Reduced row echelon form:")
}
}
{{out}}
Original matrix:
1.00 2.00 -1.00 -4.00
2.00 3.00 -1.00 -11.00
-2.00 0.00 -3.00 22.00
Reduced row echelon form:
1.00 0.00 0.00 -8.00
0.00 1.00 0.00 1.00
0.00 0.00 1.00 -2.00
Original matrix:
1.00 2.00 3.00 4.00 3.00 1.00
2.00 4.00 6.00 2.00 6.00 2.00
3.00 6.00 18.00 9.00 9.00 -6.00
4.00 8.00 12.00 10.00 12.00 4.00
5.00 10.00 24.00 11.00 15.00 -4.00
Reduced row echelon form:
1.00 2.00 0.00 0.00 3.00 4.00
0.00 0.00 1.00 0.00 0.00 -1.00
0.00 0.00 0.00 1.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00 0.00
Lua
function ToReducedRowEchelonForm ( M )
local lead = 1
local n_rows, n_cols = #M, #M[1]
for r = 1, n_rows do
if n_cols <= lead then break end
local i = r
while M[i][lead] == 0 do
i = i + 1
if n_rows == i then
i = r
lead = lead + 1
if n_cols == lead then break end
end
end
M[i], M[r] = M[r], M[i]
local m = M[r][lead]
for k = 1, n_cols do
M[r][k] = M[r][k] / m
end
for i = 1, n_rows do
if i ~= r then
local m = M[i][lead]
for k = 1, n_cols do
M[i][k] = M[i][k] - m * M[r][k]
end
end
end
lead = lead + 1
end
end
M = { { 1, 2, -1, -4 },
{ 2, 3, -1, -11 },
{ -2, 0, -3, 22 } }
res = ToReducedRowEchelonForm( M )
for i = 1, #M do
for j = 1, #M[1] do
io.write( M[i][j], " " )
end
io.write( "\n" )
end
{{out}}
1 0 0 -8
0 1 0 1
0 0 1 -2
M2000 Interpreter
low bound 1 for array
Module Base1 {
dim base 1, A(3, 4)
A(1, 1)= 1, 2, -1, -4, 2 , 3, -1, -11, -2 , 0 , -3, 22
lead=1
rowcount=3
columncount=4
gosub disp()
for r=1 to rowcount {
if columncount<lead then exit
i=r
while A(i,lead)=0 {
i++
if rowcount=i then i=r : lead++ : if columncount<lead then exit
}
for c =1 to columncount {
swap A(i, c), A(r, c)
}
if A(r, lead)<>0 then {
div1=A(r,lead)
For c =1 to columncount {
A( r, c)/=div1
}
}
for i=1 to rowcount {
if i<>r then {
mult=A(i,lead)
for j=1 to columncount {
A(i,j)-=A(r,j)*mult
}
}
}
lead=lead+1
}
disp()
sub disp()
local i, j
for i=1 to rowcount
for j=1 to columncount
Print A(i, j),
Next j
if pos>0 then print
Next i
End sub
}
Base1
Low bound 0 for array
Module base0 {
dim base 0, A(3, 4)
A(0, 0)= 1, 2, -1, -4, 2 , 3, -1, -11, -2 , 0 , -3, 22
lead=0
rowcount=3
columncount=4
gosub disp()
for r=0 to rowcount-1 {
if columncount<=lead then exit
i=r
while A(i,lead)=0 {
i++
if rowcount=i then i=r : lead++ : if columncount<lead then exit
}
for c =0 to columncount-1 {
swap A(i, c), A(r, c)
}
if A(r, lead)<>0 then {
div1=A(r,lead)
For c =0 to columncount-1 {
A( r, c)/=div1
}
}
for i=0 to rowcount-1 {
if i<>r then {
mult=A(i,lead)
for j=0 to columncount-1 {
A(i,j)-=A(r,j)*mult
}
}
}
lead=lead+1
}
disp()
sub disp()
local i, j
for i=0 to rowcount-1
for j=0 to columncount-1
Print A(i, j),
Next j
if pos>0 then print
Next i
End sub
}
base0
Maple
with(LinearAlgebra):
ReducedRowEchelonForm(<<1,2,-2>|<2,3,0>|<-1,-1,-3>|<-4,-11,22>>);
{{out}}
[1 0 0 -8]
[ ]
[0 1 0 1]
[ ]
[0 0 1 -2]
Mathematica
RowReduce[{{1, 2, -1, -4}, {2, 3, -1, -11}, {-2, 0, -3, 22}}]
gives back:
{{1, 0, 0, -8}, {0, 1, 0, 1}, {0, 0, 1, -2}}
MATLAB
rref([1, 2, -1, -4; 2, 3, -1, -11; -2, 0, -3, 22])
Maxima
rref(a):=block([p,q,k],[p,q]:matrix_size(a),a:echelon(a),
k:min(p,q),
for i thru min(p,q) do (if a[i,i]=0 then (k:i-1,return())),
for i:k thru 2 step -1 do (for j from i-1 thru 1 step -1 do a:rowop(a,j,i,a[j,i])),
a)$
a: matrix([12,-27,36,44,59],
[26,41,-54,24,23],
[33,70,59,15,-68],
[43,16,29,-52,-61],
[-43,20,71,88,11])$
rref(a);
matrix([1,0,0,0,1/2],[0,1,0,0,-1],[0,0,1,0,-1/2],[0,0,0,1,1],[0,0,0,0,0])
Objeck
class RowEchelon {
function : Main(args : String[]) ~ Nil {
matrix := [
[1, 2, -1, -4 ]
[2, 3, -1, -11 ]
[-2, 0, -3, 22]
];
matrix := Rref(matrix);
sizes := matrix->Size();
for(i := 0; i < sizes[0]; i += 1;) {
for(j := 0; j < sizes[1]; j += 1;) {
IO.Console->Print(matrix[i,j])->Print(",");
};
IO.Console->PrintLine();
};
}
function : native : Rref(matrix : Int[,]) ~ Int[,] {
lead := 0;
sizes := matrix->Size();
rowCount := sizes[0];
columnCount := sizes[1];
for(r := 0; r < rowCount; r+=1;) {
if (columnCount <= lead) {
break;
};
i := r;
while(matrix[i, lead] = 0) {
i+=1;
if (i = rowCount) {
i := r;
lead += 1;
if (columnCount = lead) {
lead-=1;
break;
};
};
};
for (j := 0; j < columnCount; j+=1;) {
temp := matrix[r, j];
matrix[r, j] := matrix[i, j];
matrix[i, j] := temp;
};
div := matrix[r, lead];
for(j := 0; j < columnCount; j+=1;) {
matrix[r, j] /= div;
};
for(j := 0; j < rowCount; j+=1;) {
if (j <> r) {
sub := matrix[j, lead];
for (k := 0; k < columnCount; k+=1;) {
matrix[j, k] -= sub * matrix[r, k];
};
};
};
lead+=1;
};
return matrix;
}
}
OCaml
let swap_rows m i j =
let tmp = m.(i) in
m.(i) <- m.(j);
m.(j) <- tmp;
;;
let rref m =
try
let lead = ref 0
and rows = Array.length m
and cols = Array.length m.(0) in
for r = 0 to pred rows do
if cols <= !lead then
raise Exit;
let i = ref r in
while m.(!i).(!lead) = 0 do
incr i;
if rows = !i then begin
i := r;
incr lead;
if cols = !lead then
raise Exit;
end
done;
swap_rows m !i r;
let lv = m.(r).(!lead) in
m.(r) <- Array.map (fun v -> v / lv) m.(r);
for i = 0 to pred rows do
if i <> r then
let lv = m.(i).(!lead) in
m.(i) <- Array.mapi (fun i iv -> iv - lv * m.(r).(i)) m.(i);
done;
incr lead;
done
with Exit -> ()
;;
let () =
let m =
[| [| 1; 2; -1; -4 |];
[| 2; 3; -1; -11 |];
[| -2; 0; -3; 22 |]; |]
in
rref m;
Array.iter (fun row ->
Array.iter (fun v ->
Printf.printf " %d" v
) row;
print_newline()
) m
Another implementation:
let rref m =
let nr, nc = Array.length m, Array.length m.(0) in
let add r s k =
for i = 0 to nc-1 do m.(r).(i) <- m.(r).(i) +. m.(s).(i)*.k done in
for c = 0 to min (nc-1) (nr-1) do
for r = c+1 to nr-1 do
if abs_float m.(c).(c) < abs_float m.(r).(c) then
let v = m.(r) in (m.(r) <- m.(c); m.(c) <- v)
done;
let t = m.(c).(c) in
if t <> 0.0 then
begin
for r = 0 to nr-1 do if r <> c then add r c (-.m.(r).(c)/.t) done;
for i = 0 to nc-1 do m.(c).(i) <- m.(c).(i)/.t done
end
done;;
let mat = [|
[| 1.0; 2.0; -.1.0; -.4.0;|];
[| 2.0; 3.0; -.1.0; -.11.0;|];
[|-.2.0; 0.0; -.3.0; 22.0;|]
|] in
let pr v = Array.iter (Printf.printf " %9.4f") v; print_newline() in
let show = Array.iter pr in
show mat;
print_newline();
rref mat;
show mat
Octave
A = [ 1, 2, -1, -4; 2, 3, -1, -11; -2, 0, -3, 22];
refA = rref(A);
disp(refA);
PARI/GP
PARI has a built-in matrix type, but no commands for row-echelon form. A dimension-limited one can be constructed from the built-in matsolve command:
rref(M)={
my(d=matsize(M));
if(d[1]+1 != d[2], error("Bad size in rref"), d=d[1]);
concat(matid(d), matsolve(matrix(d,d,x,y,M[x,y]), M[,d+1]))
};
Example:
rref([1,2,-1,-4;2,3,-1,-11;-2,0,-3,22])
{{out}}
%1 =
[1 0 0 -8]
[0 1 0 1]
[0 0 1 -2]
Perl
{{trans|Python}} Note that the function defined here takes an array reference, which is modified in place.
sub rref
{our @m; local *m = shift;
@m or return;
my ($lead, $rows, $cols) = (0, scalar(@m), scalar(@{$m[0]}));
foreach my $r (0 .. $rows - 1)
{$lead < $cols or return;
my $i = $r;
until ($m[$i][$lead])
{++$i == $rows or next;
$i = $r;
++$lead == $cols and return;}
@m[$i, $r] = @m[$r, $i];
my $lv = $m[$r][$lead];
$_ /= $lv foreach @{ $m[$r] };
my @mr = @{ $m[$r] };
foreach my $i (0 .. $rows - 1)
{$i == $r and next;
($lv, my $n) = ($m[$i][$lead], -1);
$_ -= $lv * $mr[++$n] foreach @{ $m[$i] };}
++$lead;}}
sub display { join("\n" => map join(" " => map(sprintf("%4d", $_), @$_)), @{+shift})."\n" }
@m =
(
[ 1, 2, -1, -4 ],
[ 2, 3, -1, -11 ],
[ -2, 0, -3, 22 ]
);
rref(\@m);
print display(\@m);
{{out}}
1 0 0 -8
0 1 0 1
0 0 1 -2
Perl 6
{{trans|Perl}} {{works with|Rakudo|2018.03}}
sub rref (@m) {
return unless @m;
my ($lead, $rows, $cols) = 0, +@m, +@m[0];
for ^$rows -> $r {
$lead < $cols or return @m;
my $i = $r;
until @m[$i;$lead] {
++$i == $rows or next;
$i = $r;
++$lead == $cols and return @m;
}
@m[$i, $r] = @m[$r, $i] if $r != $i;
my $lv = @m[$r;$lead];
@m[$r] »/=» $lv;
for ^$rows -> $n {
next if $n == $r;
@m[$n] »-=» @m[$r] »*» (@m[$n;$lead] // 0);
}
++$lead;
}
@m
}
sub rat-or-int ($num) {
return $num unless $num ~~ Rat;
return $num.narrow if $num.narrow.WHAT ~~ Int;
$num.nude.join: '/';
}
sub say_it ($message, @array) {
say "\n$message";
$_».&rat-or-int.fmt(" %5s").say for @array;
}
my @M = (
[ # base test case
[ 1, 2, -1, -4 ],
[ 2, 3, -1, -11 ],
[ -2, 0, -3, 22 ],
],
[ # mix of number styles
[ 3, 0, -3, 1 ],
[ .5, 3/2, -3, -2 ],
[ .2, 4/5, -1.6, .3 ],
],
[ # degenerate case
[ 1, 2, 3, 4, 3, 1],
[ 2, 4, 6, 2, 6, 2],
[ 3, 6, 18, 9, 9, -6],
[ 4, 8, 12, 10, 12, 4],
[ 5, 10, 24, 11, 15, -4],
],
[ # larger matrix
[1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, -1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, -1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, -1, 0],
[0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, -1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, -1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, -1, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, -1, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, -1, 0, 0, 0],
]
);
for @M -> @matrix {
say_it( 'Original Matrix', @matrix );
say_it( 'Reduced Row Echelon Form Matrix', rref(@matrix) );
say "\n";
}
Perl 6 handles rational numbers internally as a ratio of two integers to maintain precision. For some situations it is useful to return the ratio rather than the floating point result.
{{out}}
Original Matrix
1 2 -1 -4
2 3 -1 -11
-2 0 -3 22
Reduced Row Echelon Form Matrix
1 0 0 -8
0 1 0 1
0 0 1 -2
Original Matrix
3 0 -3 1
1/2 3/2 -3 -2
1/5 4/5 -8/5 3/10
Reduced Row Echelon Form Matrix
1 0 0 -41/2
0 1 0 -217/6
0 0 1 -125/6
Original Matrix
1 2 3 4 3 1
2 4 6 2 6 2
3 6 18 9 9 -6
4 8 12 10 12 4
5 10 24 11 15 -4
Reduced Row Echelon Form Matrix
1 2 0 0 3 4
0 0 1 0 0 -1
0 0 0 1 0 0
0 0 0 0 0 0
0 0 0 0 0 0
Original Matrix
1 0 0 0 0 0 1 0 0 0 0 -1 0 0 0 0 0 0
1 0 0 0 0 0 0 1 0 0 0 0 -1 0 0 0 0 0
1 0 0 0 0 0 0 0 1 0 0 0 0 -1 0 0 0 0
0 1 0 0 0 0 1 0 0 0 0 0 0 0 -1 0 0 0
0 1 0 0 0 0 0 0 1 0 0 -1 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 -1 0
0 0 1 0 0 0 1 0 0 0 0 0 -1 0 0 0 0 0
0 0 1 0 0 0 0 0 0 1 0 0 0 0 -1 0 0 0
0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 -1 0 0
0 0 0 1 0 0 0 0 0 1 0 0 -1 0 0 0 0 0
0 0 0 0 1 0 0 1 0 0 0 0 0 -1 0 0 0 0
0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 -1 0
0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 -1 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1
0 0 0 0 0 1 0 0 0 0 1 0 0 0 -1 0 0 0
Reduced Row Echelon Form Matrix
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17/39
0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4/13
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20/39
0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 28/39
0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 19/39
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 8/39
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 11/39
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1/3
0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 20/39
0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 25/39
0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 28/39
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 10/13
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 20/39
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 32/39
Re-implemented without the pseudocode, expressed as elementary matrix row operations. See http://unapologetic.wordpress.com/2009/08/27/elementary-row-and-column-operations/ and http://unapologetic.wordpress.com/2009/09/03/reduced-row-echelon-form/
First, a procedural version:
sub swap_rows ( @M, $r1, $r2 ) { @M[ $r1, $r2 ] = @M[ $r2, $r1 ] };
sub scale_row ( @M, $scale, $r ) { @M[$r] = @M[$r] »*» $scale };
sub shear_row ( @M, $scale, $r1, $r2 ) { @M[$r1] = @M[$r1].list »+» ( @M[$r2] »*» $scale ) };
sub reduce_row ( @M, $r, $c ) { scale_row( @M, 1/@M[$r][$c], $r ) };
sub clear_column ( @M, $r, $c ) {
for @M.keys.grep( * != $r ) -> $row_num {
shear_row( @M, -1*@M[$row_num][$c], $row_num, $r );
}
}
my @M = (
[< 1 2 -1 -4 >],
[< 2 3 -1 -11 >],
[< -2 0 -3 22 >],
);
my $column_count = +@( @M[0] );
my $current_col = 0;
while all( @M».[$current_col] ) == 0 {
$current_col++;
return if $current_col == $column_count; # Matrix was all-zeros.
}
for @M.keys -> $current_row {
reduce_row( @M, $current_row, $current_col );
clear_column( @M, $current_row, $current_col );
$current_col++;
return if $current_col == $column_count;
}
say @($_)».fmt(' %4g') for @M;
And the same code, recast into OO. Also, scale and shear are recast as unscale and unshear, which fit the problem better.
class Matrix is Array {
method unscale_row ( @M: $scale, $row ) {
@M[$row] = @M[$row] »/» $scale;
}
method unshear_row ( @M: $scale, $r1, $r2 ) {
@M[$r1] = @M[$r1] »-» ( @M[$r2] »*» $scale );
}
method reduce_row ( @M: $row, $col ) {
@M.unscale_row( @M[$row][$col], $row );
}
method clear_column ( @M: $row, $col ) {
for @M.keys.grep( * != $row ) -> $scanning_row {
@M.unshear_row( @M[$scanning_row][$col], $scanning_row, $row );
}
}
method reduced_row_echelon_form ( @M: ) {
my $column_count = +@( @M[0] );
my $current_col = 0;
# Skip past all-zero columns.
while all( @M».[$current_col] ) == 0 {
$current_col++;
return if $current_col == $column_count; # Matrix was all-zeros.
}
for @M.keys -> $current_row {
@M.reduce_row( $current_row, $current_col );
@M.clear_column( $current_row, $current_col );
$current_col++;
return if $current_col == $column_count;
}
}
}
my $M = Matrix.new(
[< 1 2 -1 -4 >],
[< 2 3 -1 -11 >],
[< -2 0 -3 22 >],
);
$M.reduced_row_echelon_form;
say @($_)».fmt(' %4g') for @($M);
Note that both versions can be simplified using Z+=, Z-=, X*=, and X/= to scale and shear. Currently, Rakudo has a bug related to Xop= and Zop=.
Note that the negative zeros in the output are innocuous, and also occur in the Perl 5 version.
Phix
{{Trans|Euphoria}}
function ToReducedRowEchelonForm(sequence M)
integer lead = 1,
rowCount = length(M),
columnCount = length(M[1]),
i
for r=1 to rowCount do
if lead>=columnCount then exit end if
i = r
while M[i][lead]=0 do
i += 1
if i=rowCount then
i = r
lead += 1
if lead=columnCount then exit end if
end if
end while
-- nb M[i] is assigned before M[r], which matters when i=r:
{M[r],M[i]} = {sq_div(M[i],M[i][lead]),M[r]}
for j=1 to rowCount do
if j!=r then
M[j] = sq_sub(M[j],sq_mul(M[j][lead],M[r]))
end if
end for
lead += 1
end for
return M
end function
? ToReducedRowEchelonForm(
{ { 1, 2, -1, -4 },
{ 2, 3, -1, -11 },
{ -2, 0, -3, 22 } })
{{out}}
{{1,0,0,-8},{0,1,0,1},{0,0,1,-2}}
PHP
{{works with|PHP|5.x}} {{trans|Java}}
<?php
function rref($matrix)
{
$lead = 0;
$rowCount = count($matrix);
if ($rowCount == 0)
return $matrix;
$columnCount = 0;
if (isset($matrix[0])) {
$columnCount = count($matrix[0]);
}
for ($r = 0; $r < $rowCount; $r++) {
if ($lead >= $columnCount)
break; {
$i = $r;
while ($matrix[$i][$lead] == 0) {
$i++;
if ($i == $rowCount) {
$i = $r;
$lead++;
if ($lead == $columnCount)
return $matrix;
}
}
$temp = $matrix[$r];
$matrix[$r] = $matrix[$i];
$matrix[$i] = $temp;
} {
$lv = $matrix[$r][$lead];
for ($j = 0; $j < $columnCount; $j++) {
$matrix[$r][$j] = $matrix[$r][$j] / $lv;
}
}
for ($i = 0; $i < $rowCount; $i++) {
if ($i != $r) {
$lv = $matrix[$i][$lead];
for ($j = 0; $j < $columnCount; $j++) {
$matrix[$i][$j] -= $lv * $matrix[$r][$j];
}
}
}
$lead++;
}
return $matrix;
}
?>
PicoLisp
(de reducedRowEchelonForm (Mat)
(let (Lead 1 Cols (length (car Mat)))
(for (X Mat X (cdr X))
(NIL
(loop
(T (seek '((R) (n0 (get R 1 Lead))) X)
@ )
(T (> (inc 'Lead) Cols)) ) )
(xchg @ X)
(let D (get X 1 Lead)
(map
'((R) (set R (/ (car R) D)))
(car X) ) )
(for Y Mat
(unless (== Y (car X))
(let N (- (get Y Lead))
(map
'((Dst Src)
(inc Dst (* N (car Src))) )
Y
(car X) ) ) ) )
(T (> (inc 'Lead) Cols)) ) )
Mat )
{{out}}
(reducedRowEchelonForm
'(( 1 2 -1 -4) ( 2 3 -1 -11) (-2 0 -3 22)) )
-> ((1 0 0 -8) (0 1 0 1) (0 0 1 -2))
Python
def ToReducedRowEchelonForm( M):
if not M: return
lead = 0
rowCount = len(M)
columnCount = len(M[0])
for r in range(rowCount):
if lead >= columnCount:
return
i = r
while M[i][lead] == 0:
i += 1
if i == rowCount:
i = r
lead += 1
if columnCount == lead:
return
M[i],M[r] = M[r],M[i]
lv = M[r][lead]
M[r] = [ mrx / float(lv) for mrx in M[r]]
for i in range(rowCount):
if i != r:
lv = M[i][lead]
M[i] = [ iv - lv*rv for rv,iv in zip(M[r],M[i])]
lead += 1
mtx = [
[ 1, 2, -1, -4],
[ 2, 3, -1, -11],
[-2, 0, -3, 22],]
ToReducedRowEchelonForm( mtx )
for rw in mtx:
print ', '.join( (str(rv) for rv in rw) )
R
{{trans|Fortran}}
rref <- function(m) {
pivot <- 1
norow <- nrow(m)
nocolumn <- ncol(m)
for(r in 1:norow) {
if ( nocolumn <= pivot ) break;
i <- r
while( m[i,pivot] == 0 ) {
i <- i + 1
if ( norow == i ) {
i <- r
pivot <- pivot + 1
if ( nocolumn == pivot ) return(m)
}
}
trow <- m[i, ]
m[i, ] <- m[r, ]
m[r, ] <- trow
m[r, ] <- m[r, ] / m[r, pivot]
for(i in 1:norow) {
if ( i != r )
m[i, ] <- m[i, ] - m[r, ] * m[i, pivot]
}
pivot <- pivot + 1
}
return(m)
}
m <- matrix(c(1, 2, -1, -4,
2, 3, -1, -11,
-2, 0, -3, 22), 3, 4, byrow=TRUE)
print(m)
print(rref(m))
Racket
#lang racket
(require math)
(define (reduced-echelon M)
(matrix-row-echelon M #t #t))
(reduced-echelon
(matrix [[1 2 -1 -4]
[2 3 -1 -11]
[-2 0 -3 22]]))
{{out}}
(mutable-array
#[#[1 0 0 -8]
#[0 1 0 1]
#[0 0 1 -2]])
REXX
''Reduced Row Echelon Form'' (a.k.a. ''row canonical form'') of a matrix, with optimization added.
/*REXX pgm performs Reduced Row Echelon Form (RREF), AKA row canonical form on a matrix)*/
cols=0; w=0; @.=0 /*max cols in a row; max width; matrix.*/
mat.=; mat.1= ' 1 2 -1 -4 '
mat.2= ' 2 3 -1 -11 '
mat.3= ' -2 0 -3 22 '
do r=1 until mat.r==''; _=mat.r /*build @.row.col from (matrix) mat.X*/
do c=1 until _=''; parse var _ @.r.c _
w=max(w, length(@.r.c) + 1) /*find the maximum width of an element.*/
end /*c*/
cols=max(cols, c) /*save the maximum number of columns. */
end /*r*/
rows=r - 1 /*adjust the row count (from DO loop).*/
call showMat 'original matrix' /*display the original matrix to screen*/
!=1 /*set the working column pointer to 1.*/
/* ┌──────────────────────◄────────────────◄──── Reduced Row Echelon Form on matrix.*/
do r=1 for rows while cols>! /*begin to perform the heavy lifting. */
j=r /*use a subsitute index for the DO loop*/
do while @.j.!==0; j=j + 1
if j==rows then do; j=r; !=! + 1; if cols==! then leave r; end
end /*while*/
/* [↓] swap rows J,R (but not if same)*/
do _=1 for cols while j\==r; parse value @.r._ @.j._ with @.j._ @._._
end /*_*/
?=@.r.!
do d=1 for cols while ?\=1; @.r.d= @.r.d / ?
end /*d*/ /* [↑] divide row J by @.r.p ──unless≡1*/
do k=1 for rows; ?= @.k.! /*subtract (row K) @.r.s from row K.*/
if k==r | ?=0 then iterate /*skip if row K is the same as row R.*/
do s=1 for cols; @.k.s= @.k.s - ? * @.r.s
end /*s*/
end /*k*/ /* [↑] for the rest of numbers in row.*/
!=! + 1 /*bump the column pointer. */
end /*r*/
call showMat 'matrix RREF' /*display the reduced row echelon form.*/
exit /*stick a fork in it, we're all done. */
/*──────────────────────────────────────────────────────────────────────────────────────*/
showMat: parse arg title; say; say center(title, 3 + (cols+1) * w, '─'); say
do r=1 for rows; _=
do c=1 for cols
if @.r.c=='' then do; say "***error*** matrix element isn't defined:"
say 'row' r", column" c'.'; exit 13
end
_=_ right(@.r.c, w)
end /*c*/
say _ /*display a matrix row to the terminal.*/
end /*r*/; return
{{out|output|text= when using the default (internal) input:}}
────original matrix────
1 2 -1 -4
2 3 -1 -11
-2 0 -3 22
──────matrix RREF──────
1 0 0 -8
0 1 0 1
0 0 1 -2
Ring
# Project : Reduced row echelon form
matrix = [[1, 2, -1, -4],
[2, 3, -1, -11],
[ -2, 0, -3, 22]]
ref(matrix)
for row = 1 to 3
for col = 1 to 4
if matrix[row][col] = -0
see "0 "
else
see "" + matrix[row][col] + " "
ok
next
see nl
next
func ref(m)
nrows = 3
ncols = 4
lead = 1
for r = 1 to nrows
if lead >= ncols
exit
ok
i = r
while m[i][lead] = 0
i = i + 1
if i = nrows
i = r
lead = lead + 1
if lead = ncols
exit 2
ok
ok
end
for j = 1 to ncols
temp = m[i][j]
m[i][j] = m[r][j]
m[r][j] = temp
next
n = m[r][lead]
if n != 0
for j = 1 to ncols
m[r][j] = m[r][j] / n
next
ok
for i = 1 to nrows
if i != r
n = m[i][lead]
for j = 1 to ncols
m[i][j] = m[i][j] - m[r][j] * n
next
ok
next
lead = lead + 1
next
Output:
1 0 0 -8
0 1 0 1
0 0 1 -2
Ruby
{{works with|Ruby|1.9.3}}
# returns an 2-D array where each element is a Rational
def reduced_row_echelon_form(ary)
lead = 0
rows = ary.size
cols = ary[0].size
rary = convert_to(ary, :to_r) # use rational arithmetic
catch :done do
rows.times do |r|
throw :done if cols <= lead
i = r
while rary[i][lead] == 0
i += 1
if rows == i
i = r
lead += 1
throw :done if cols == lead
end
end
# swap rows i and r
rary[i], rary[r] = rary[r], rary[i]
# normalize row r
v = rary[r][lead]
rary[r].collect! {|x| x / v}
# reduce other rows
rows.times do |i|
next if i == r
v = rary[i][lead]
rary[i].each_index {|j| rary[i][j] -= v * rary[r][j]}
end
lead += 1
end
end
rary
end
# type should be one of :to_s, :to_i, :to_f, :to_r
def convert_to(ary, type)
ary.each_with_object([]) do |row, new|
new << row.collect {|elem| elem.send(type)}
end
end
class Rational
alias _to_s to_s
def to_s
denominator==1 ? numerator.to_s : _to_s
end
end
def print_matrix(m)
max = m[0].collect {-1}
m.each {|row| row.each_index {|i| max[i] = [max[i], row[i].to_s.length].max}}
m.each {|row| row.each_index {|i| print "%#{max[i]}s " % row[i]}; puts}
end
mtx = [
[ 1, 2, -1, -4],
[ 2, 3, -1,-11],
[-2, 0, -3, 22]
]
print_matrix reduced_row_echelon_form(mtx)
puts
mtx = [
[ 1, 2, 3, 7],
[-4, 7,-2, 7],
[ 3, 3, 0, 7]
]
reduced = reduced_row_echelon_form(mtx)
print_matrix reduced
print_matrix convert_to(reduced, :to_f)
{{out}}
1 0 0 -8
0 1 0 1
0 0 1 -2
1 0 0 2/3
0 1 0 5/3
0 0 1 1
1.0 0.0 0.0 0.6666666666666666
0.0 1.0 0.0 1.6666666666666667
0.0 0.0 1.0 1.0
Sage
{{works with|Sage|4.6.2}}
sage: m = matrix(ZZ, [[1,2,-1,-4],[2,3,-1,-11],[-2,0,-3,22]])
sage: m.rref()
[ 1 0 0 -8]
[ 0 1 0 1]
[ 0 0 1 -2]
Scheme
{{Works with|Scheme|RRS}}
(define (reduced-row-echelon-form matrix)
(define (clean-down matrix from-row column)
(cons (car matrix)
(if (zero? from-row)
(map (lambda (row)
(map -
row
(map (lambda (element)
(/ (* element (list-ref row column))
(list-ref (car matrix) column)))
(car matrix))))
(cdr matrix))
(clean-down (cdr matrix) (- from-row 1) column))))
(define (clean-up matrix until-row column)
(if (zero? until-row)
matrix
(cons (map -
(car matrix)
(map (lambda (element)
(/ (* element (list-ref (car matrix) column))
(list-ref (list-ref matrix until-row) column)))
(list-ref matrix until-row)))
(clean-up (cdr matrix) (- until-row 1) column))))
(define (normalise matrix row with-column)
(if (zero? row)
(cons (map (lambda (element)
(/ element (list-ref (car matrix) with-column)))
(car matrix))
(cdr matrix))
(cons (car matrix) (normalise (cdr matrix) (- row 1) with-column))))
(define (repeat procedure matrix indices)
(if (null? indices)
matrix
(repeat procedure
(procedure matrix (car indices) (car indices))
(cdr indices))))
(define (iota start stop)
(if (> start stop)
(list)
(cons start (iota (+ start 1) stop))))
(let ((indices (iota 0 (- (length matrix) 1))))
(repeat normalise
(repeat clean-up
(repeat clean-down
matrix
indices)
indices)
indices)))
Example:
(define matrix
(list (list 1 2 -1 -4) (list 2 3 -1 -11) (list -2 0 -3 22)))
(display (reduced-row-echelon-form matrix))
(newline)
{{out}}
## Seed7
```seed7
const type: matrix is array array float;
const proc: toReducedRowEchelonForm (inout matrix: mat) is func
local
var integer: numRows is 0;
var integer: numColumns is 0;
var integer: row is 0;
var integer: column is 0;
var integer: pivot is 0;
var float: factor is 0.0;
begin
numRows := length(mat);
numColumns := length(mat[1]);
for row range numRows downto 1 do
column := 1;
while column <= numColumns and mat[row][column] = 0.0 do
incr(column);
end while;
if column > numColumns then
# Empty rows are moved to the bottom
mat := mat[.. pred(row)] & mat[succ(row) ..] & [] (mat[row]);
decr(numRows);
end if;
end for;
for pivot range 1 to numRows do
if mat[pivot][pivot] = 0.0 then
# Find a row were the pivot column is not zero
row := 1;
while row <= numRows and mat[row][pivot] = 0.0 do
incr(row);
end while;
# Add row were the pivot column is not zero
for column range 1 to numColumns do
mat[pivot][column] +:= mat[row][column];
end for;
end if;
if mat[pivot][pivot] <> 1.0 then
# Make sure that the pivot element is 1.0
factor := 1.0 / mat[pivot][pivot];
for column range pivot to numColumns do
mat[pivot][column] := mat[pivot][column] * factor;
end for;
end if;
for row range 1 to numRows do
if row <> pivot and mat[row][pivot] <> 0.0 then
# Make sure that in all other rows the pivot column contains zero
factor := -mat[row][pivot];
for column range pivot to numColumns do
mat[row][column] +:= mat[pivot][column] * factor;
end for;
end if;
end for;
end for;
end func;
Original source: [http://seed7.sourceforge.net/algorith/math.htm#toReducedRowEchelonForm]
Sidef
{{trans|Perl 6}}
func rref (M) {
var (j, rows, cols) = (0, M.len, M[0].len)
for r in (^rows) {
j < cols || return M
var i = r
while (!M[i][j]) {
++i == rows || next
i = r
++j == cols && return M
}
M[i, r] = M[r, i] if (r != i)
M[r] = (M[r] »/» M[r][j])
for n in (^rows) {
next if (n == r)
M[n] = (M[n] »-« (M[r] »*» M[n][j]))
}
++j
}
return M
}
func say_it (message, array) {
say "\n#{message}";
array.each { |row|
say row.map { |n| " %5s" % n.as_rat }.join
}
}
var M = [
[ # base test case
[ 1, 2, -1, -4 ],
[ 2, 3, -1, -11 ],
[ -2, 0, -3, 22 ],
],
[ # mix of number styles
[ 3, 0, -3, 1 ],
[ .5, 3/2, -3, -2 ],
[ .2, 4/5, -1.6, .3 ],
],
[ # degenerate case
[ 1, 2, 3, 4, 3, 1],
[ 2, 4, 6, 2, 6, 2],
[ 3, 6, 18, 9, 9, -6],
[ 4, 8, 12, 10, 12, 4],
[ 5, 10, 24, 11, 15, -4],
],
];
M.each { |matrix|
say_it('Original Matrix', matrix);
say_it('Reduced Row Echelon Form Matrix', rref(matrix));
say '';
}
{{out}}
Original Matrix
1 2 -1 -4
2 3 -1 -11
-2 0 -3 22
Reduced Row Echelon Form Matrix
1 0 0 -8
0 1 0 1
0 0 1 -2
Original Matrix
3 0 -3 1
1/2 3/2 -3 -2
1/5 4/5 -8/5 3/10
Reduced Row Echelon Form Matrix
1 0 0 -41/2
0 1 0 -217/6
0 0 1 -125/6
Original Matrix
1 2 3 4 3 1
2 4 6 2 6 2
3 6 18 9 9 -6
4 8 12 10 12 4
5 10 24 11 15 -4
Reduced Row Echelon Form Matrix
1 2 0 0 3 4
0 0 1 0 0 -1
0 0 0 1 0 0
0 0 0 0 0 0
0 0 0 0 0 0
Swift
var lead = 0
for r in 0..<rows {
if (cols <= lead) { break }
var i = r
while (m[i][lead] == 0) {
i += 1
if (i == rows) {
i = r
lead += 1
if (cols == lead) {
lead -= 1
break
}
}
}
for j in 0..<cols {
let temp = m[r][j]
m[r][j] = m[i][j]
m[i][j] = temp
}
let div = m[r][lead]
if (div != 0) {
for j in 0..<cols {
m[r][j] /= div
}
}
for j in 0..<rows {
if (j != r) {
let sub = m[j][lead]
for k in 0..<cols {
m[j][k] -= (sub * m[r][k])
}
}
}
lead += 1
}
Tcl
Using utility procs defined at [[Matrix Transpose#Tcl]]
package require Tcl 8.5
namespace path {::tcl::mathop ::tcl::mathfunc}
proc toRREF {m} {
set lead 0
lassign [size $m] rows cols
for {set r 0} {$r < $rows} {incr r} {
if {$cols <= $lead} {
break
}
set i $r
while {[lindex $m $i $lead] == 0} {
incr i
if {$rows == $i} {
set i $r
incr lead
if {$cols == $lead} {
# Tcl can't break out of nested loops
return $m
}
}
}
# swap rows i and r
foreach idx [list $i $r] row [list [lindex $m $r] [lindex $m $i]] {
lset m $idx $row
}
# divide row r by m(r,lead)
set val [lindex $m $r $lead]
for {set j 0} {$j < $cols} {incr j} {
lset m $r $j [/ [double [lindex $m $r $j]] $val]
}
for {set i 0} {$i < $rows} {incr i} {
if {$i != $r} {
# subtract m(i,lead) multiplied by row r from row i
set val [lindex $m $i $lead]
for {set j 0} {$j < $cols} {incr j} {
lset m $i $j [- [lindex $m $i $j] [* $val [lindex $m $r $j]]]
}
}
}
incr lead
}
return $m
}
set m {{1 2 -1 -4} {2 3 -1 -11} {-2 0 -3 22}}
print_matrix $m
print_matrix [toRREF $m]
{{out}}
1 2 -1 -4
2 3 -1 -11
-2 0 -3 22
1.0 0.0 0.0 -8.0
-0.0 1.0 0.0 1.0
-0.0 -0.0 1.0 -2.0
=={{header|TI-83 BASIC}}== Builtin function: rref()
rref([[1,2,-1,-4][2,3,-1,-11][-2,0,-3,22]])
{{out}}
[[1 0 0 -8]
[0 1 0 1]
[0 0 1 -2]]
=={{header|TI-89 BASIC}}==
rref([1,2,–1,–4; 2,3,–1,–11; –2,0,–3,22])
Output (in prettyprint mode):
Matrices can also be stored in variables, and entered interactively using the Data/Matrix Editor.
Ursala
The most convenient representation for a matrix in Ursala is as a list of lists. Several auxiliary functions are defined to make this task more manageable. The pivot function reorders the rows to position the first column entry with maximum magnitude in the first row. The descending function is a second order function abstracting the pattern of recursion down the major diagonal of a matrix. The reflect function allows the code for the first phase in the reduction to be reused during the upward traversal by appropriately permuting the rows and columns. The row_reduce function adds a multiple of the top row to each subsequent row so as to cancel the first column. These are all combined in the main rref function.
#import std
#import flo
pivot = -<x fleq+ abs~~bh
descending = ~&a^&+ ^|ahPathS2fattS2RpC/~&
reflect = ~&lxPrTSx+ *iiD ~&l-~brS+ zipp0
row_reduce = ^C/vid*hhiD *htD minus^*p/~&r times^*D/vid@bh ~&l
rref = reflect+ (descending row_reduce)+ reflect+ descending row_reduce+ pivot
#show+
test =
printf/*=*'%8.4f' rref <
<1.,2.,-1.,-4.>,
<2.,3.,-1.,-11.>,
<-2.,0.,-3.,22.>>
{{out}}
1.0000 0.0000 0.0000 -8.0000
0.0000 1.0000 0.0000 1.0000
0.0000 0.0000 1.0000 -2.0000
An alternative and more efficient solution is to use the msolve library function as shown, which interfaces with the lapack library if available. This solution is applicable only if the input is a non-singular augmented square matrix.
#import lin
rref = @ySzSX msolve; ^plrNCTS\~& ~&iiDlSzyCK9+ :/1.+ 0.!*t
VBA
{{trans|Phix}}
Private Function ToReducedRowEchelonForm(M As Variant) As Variant
Dim lead As Integer: lead = 0
Dim rowCount As Integer: rowCount = UBound(M)
Dim columnCount As Integer: columnCount = UBound(M(0))
Dim i As Integer
For r = 0 To rowCount
If lead >= columnCount Then
Exit For
End If
i = r
Do While M(i)(lead) = 0
i = i + 1
If i = rowCount Then
i = r
lead = lead + 1
If lead = columnCount Then
Exit For
End If
End If
Loop
Dim tmp As Variant
tmp = M(r)
M(r) = M(i)
M(i) = tmp
If M(r)(lead) <> 0 Then
div = M(r)(lead)
For t = LBound(M(r)) To UBound(M(r))
M(r)(t) = M(r)(t) / div
Next t
End If
For j = 0 To rowCount
If j <> r Then
subt = M(j)(lead)
For t = LBound(M(j)) To UBound(M(j))
M(j)(t) = M(j)(t) - subt * M(r)(t)
Next t
End If
Next j
lead = lead + 1
Next r
ToReducedRowEchelonForm = M
End Function
Public Sub main()
r = ToReducedRowEchelonForm(Array( _
Array(1, 2, -1, -4), _
Array(2, 3, -1, -11), _
Array(-2, 0, -3, 22)))
For i = LBound(r) To UBound(r)
Debug.Print Join(r(i), vbTab)
Next i
End Sub
{{out}}
1 0 0 -8
0 1 0 1
0 0 1 -2
Visual FoxPro
Translation of Fortran.
CLOSE DATABASES ALL
LOCAL lnRows As Integer, lnCols As Integer, lcSafety As String
LOCAL ARRAY matrix[1]
lcSafety = SET("Safety")
SET SAFETY OFF
CLEAR
CREATE CURSOR results (c1 B(6), c2 B(6), c3 B(6), c4 B(6))
CREATE CURSOR curs1(c1 I, c2 I, c3 I, c4 I)
INSERT INTO curs1 VALUES (1,2,-1,-4)
INSERT INTO curs1 VALUES (2,3,-1,-11)
INSERT INTO curs1 VALUES (-2,0,-3,22)
lnRows = RECCOUNT() && 3
lnCols = FCOUNT() && 4
SELECT * FROM curs1 INTO ARRAY matrix
IF RREF(@matrix, lnRows, lnCols)
SELECT results
APPEND FROM ARRAY matrix
BROWSE NORMAL IN SCREEN
ENDIF
SET SAFETY &lcSafety
FUNCTION RREF(mat, tnRows As Integer, tnCols As Integer) As Boolean
LOCAL lnPivot As Integer, i As Integer, r As Integer, j As Integer, ;
p As Double. llResult As Boolean, llExit As Boolean
llResult = .T.
llExit = .F.
lnPivot = 1
FOR r = 1 TO tnRows
IF lnPivot > tnCols
EXIT
ENDIF
i = r
DO WHILE mat[i,lnPivot] = 0
i = i + 1
IF i = tnRows
i = r
lnPivot = lnPivot + 1
IF lnPivot > tnCols
llExit = .T.
EXIT
ENDIF
ENDIF
ENDDO
IF llExit
EXIT
ENDIF
ASwapRows(@mat, i, r)
p = mat[r,lnPivot]
IF p # 0
FOR j = 1 TO tnCols
mat[r,j] = mat[r,j]/p
ENDFOR
ELSE
? "Divison by zero."
llResult = .F.
EXIT
ENDIF
FOR i = 1 TO tnRows
IF i # r
p = mat[i,lnPivot]
FOR j = 1 TO tnCols
mat[i,j] = mat[i,j] - mat[r,j]*p
ENDFOR
ENDIF
ENDFOR
lnPivot = lnPivot + 1
ENDFOR
RETURN llResult
ENDFUNC
PROCEDURE ASwapRows(arr, tnRow1 As Integer, tnRow2 As Integer)
*!* Interchange rows tnRow1 and tnRow2 of array arr.
LOCAL n As Integer
n = ALEN(arr,2)
LOCAL ARRAY tmp[1,n]
STORE 0 TO tmp
ACPY2(@arr, @tmp, tnRow1, 1)
ACPY2(@arr, @arr, tnRow2, tnRow1)
ACPY2(@tmp, @arr, 1, tnRow2)
ENDPROC
PROCEDURE ACPY2(m1, m2, tnSrcRow As Integer, tnDestRow As Integer)
*!* Copy m1[tnSrcRow,*] to m2[tnDestRow,*]
*!* m1 and m2 must have the same number of columns.
LOCAL n As Integer, e1 As Integer, e2 As Integer
n = ALEN(m1,2)
e1 = AELEMENT(m1,tnSrcRow,1)
e2 = AELEMENT(m2,tnDestRow,1)
ACOPY(m1, m2, e1, n, e2)
ENDPROC
{{out}}
C1 C2 C3 C4
1.000000 0.000000 0.000000 -8.000000
0.000000 1.000000 0.000000 1.000000
0.000000 0.000000 1.000000 -2.000000
zkl
The "best" way is to use the GNU Scientific Library:
var [const] GSL=Import("zklGSL"); // libGSL (GNU Scientific Library)
fcn toReducedRowEchelonForm(M){ // in place
lead,rows,columns := 0,M.rows,M.cols;
foreach r in (rows){
if (columns<=lead) return(M);
i:=r;
while(M[i,lead]==0){ // not a great check to use with real numbers
i+=1;
if(i==rows){
i=r; lead+=1;
if(lead==columns) return(M);
}
}
M.swapRows(i,r);
if(x:=M[r,lead]) M[r]/=x;
foreach i in (rows){ if(i!=r) M[i]-=M[r]*M[i,lead] }
lead+=1;
}
M
}
A:=GSL.Matrix(3,4).set( 1, 2, -1, -4,
2, 3, -1, -11,
-2, 0, -3, 22);
toReducedRowEchelonForm(A).format(5,1).println();
{{out}}
1.0, 0.0, 0.0, -8.0
0.0, 1.0, 0.0, 1.0
0.0, 0.0, 1.0, -2.0
Or, using lists of lists and direct implementation of the pseudo-code given, lots of generating new rows rather than modifying the rows themselves.
fcn toReducedRowEchelonForm(m){ // m is modified, the rows are not
lead,rowCount,columnCount := 0,m.len(),m[1].len();
foreach r in (rowCount){
if(columnCount<=lead) break;
i:=r;
while(m[i][lead]==0){
i+=1;
if(rowCount==i){
i=r; lead+=1;
if(columnCount==lead) break;
}
}//while
m.swap(i,r); // Swap rows i and r
if(n:=m[r][lead]) m[r]=m[r].apply('/(n)); //divide row r by M[r,lead]
foreach i in (rowCount){
if(i!=r) // Subtract M[i, lead] multiplied by row r from row i
m[i]=m[i].zipWith('-,m[r].apply('*(m[i][lead])))
}//foreach
lead+=1;
}//foreach
m
}
m:=List( T( 1, 2, -1, -4,), // T is read only list
T( 2, 3, -1, -11,),
T(-2, 0, -3, 22,));
printM(m);
println("-->");
printM(toReducedRowEchelonForm(m));
fcn printM(m){ m.pump(Console.println,rowFmt) }
fcn rowFmt(row){ ("%4d "*row.len()).fmt(row.xplode()) }
{{out}}
1 2 -1 -4
2 3 -1 -11
-2 0 -3 22
-->
1 0 0 -8
0 1 0 1
0 0 1 -2
== References ==