Reduced row echelon form: Difference between revisions
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=={{header|REXX}}== |
=={{header|REXX}}== |
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''Reduced Row Echelon Form'' (a.k.a. ''row canonical form'') of a matrix, with optimization added. |
''Reduced Row Echelon Form'' (a.k.a. ''row canonical form'') of a matrix, with optimization added. |
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<lang rexx>/*REXX |
<lang rexx>/*REXX pgm performs Reduced Row Echelon Form (RREF), AKA row canonical form on a matrix)*/ |
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cols=0; w=0; @.=0 /*max cols in a row; max width; matrix.*/ |
cols=0; w=0; @.=0 /*max cols in a row; max width; matrix.*/ |
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mat.=; |
mat.=; mat.1= ' 1 2 -1 -4 ' |
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mat.2= ' 2 3 -1 -11 ' |
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mat.3= ' -2 0 -3 22 ' |
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do r=1 until mat.r==''; _=mat.r /*build @.row.col from mat.X |
do r=1 until mat.r==''; _=mat.r /*build @.row.col from (matrix) mat.X*/ |
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do c=1 until _=''; parse var _ @.r.c _ |
do c=1 until _=''; parse var _ @.r.c _ |
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w=max(w, length(@.r.c)) |
w=max(w, length(@.r.c) +1) /*find the maximum width of an element.*/ |
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end /*c*/ |
end /*c*/ |
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cols=max(cols,c) /*remember max number of cols.*/ |
cols=max(cols,c) /*remember max number of cols.*/ |
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end /*r*/ |
end /*r*/ |
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rows=r-1 /*adjust the row count (from DO loop).*/ |
rows=r-1 /*adjust the row count (from DO loop).*/ |
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call showMat 'original matrix' /*display the original matrix to screen*/ |
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!=1 /*set the working column pointer to 1.*/ |
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/* ┌─────────────────────────────────────────◄── Reduced Row Echelon Form on matrix.*/ |
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!=1 /*set the working column pointer to 1.*/ |
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| ⚫ | |||
/* ┌───────────────────────────────◄── Reduced Row Echelon Form on matrix.*/ |
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| ⚫ | |||
j=r |
j=r |
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do while @.j.!==0; j=j+1 |
do while @.j.!==0; j=j+1 |
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if j==rows then do; j=r; !=!+1; if cols==! then leave r; end |
if j==rows then do; j=r; !=!+1; if cols==! then leave r; end |
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end /*while*/ |
end /*while*/ |
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/* [↓] swap rows J,R (but not if same)*/ |
/* [↓] swap rows J,R (but not if same)*/ |
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do _=1 for cols while j\==r; parse value @.r._ @.j._ with @.j._ @._._; end |
do _=1 for cols while j\==r; parse value @.r._ @.j._ with @.j._ @._._; end /*_*/ |
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?=@.r.! |
?=@.r.! |
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do d=1 for cols while ?\=1; @.r.d=@.r.d/?; end /*d*/ |
do d=1 for cols while ?\=1; @.r.d=@.r.d/?; end /*d*/ |
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/* [↑] divide row J by @.r.p ──unless≡1*/ |
/* [↑] divide row J by @.r.p ──unless≡1*/ |
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do k=1 for rows; ?=@.k.! /*subtract (row K) @.r.s from row K.*/ |
do k=1 for rows; ?=@.k.! /*subtract (row K) @.r.s from row K.*/ |
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if k==r | ?=0 then iterate /*skip if row K is the same as row R.*/ |
if k==r | ?=0 then iterate /*skip if row K is the same as row R.*/ |
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do s=1 for cols; @.k.s=@.k.s - ?*@.r.s; end /*s*/ |
do s=1 for cols; @.k.s=@.k.s - ?*@.r.s; end /*s*/ |
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end /*k*/ /* [↑] for the rest of numbers in row.*/ |
end /*k*/ /* [↑] for the rest of numbers in row.*/ |
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!=!+1 /*bump the column pointer. */ |
!=!+1 /*bump the column pointer. */ |
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end /*r*/ |
end /*r*/ |
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call showMat 'matrix RREF' /*display the reduced row echelon form.*/ |
call showMat 'matrix RREF' /*display the reduced row echelon form.*/ |
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exit /*stick a fork in it, we're all done. */ |
exit /*stick a fork in it, we're all done. */ |
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/*──────────────────────────────────────────────────────────────────────────────────────*/ |
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/*────────────────────────────────────────────────────────────────────────────*/ |
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showMat: parse arg title; say; say center(title, 3+(cols+1)*w, '─'); say |
showMat: parse arg title; say; say center(title, 3+(cols+1)*w, '─'); say |
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do r=1 for rows; _= |
do r=1 for rows; _= |
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do c=1 for cols |
do c=1 for cols |
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if @.r.c=='' then do; say "***error |
if @.r.c=='' then do; say "***error*** matrix element isn't defined:" |
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say 'row' row", column" c'.'; exit 13 |
say 'row' row", column" c'.'; exit 13 |
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end |
end |
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_=_ right(@.r.c,w) |
_=_ right(@.r.c,w) |
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end /*c*/ |
end /*c*/ |
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say _ /*display a row of the matrix to screen*/ |
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end /*r*/ |
end /*r*/ |
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return</lang> |
return</lang> |
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{{out|output|text= when using the default (internal) input:}} |
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{{out}} |
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<pre> |
<pre> |
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────original matrix──── |
────original matrix──── |
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Revision as of 18:08, 8 May 2017
| This page uses content from Wikipedia. The original article was at Rref#Pseudocode. The list of authors can be seen in the page history. As with Rosetta Code, the text of Wikipedia is available under the GNU FDL. (See links for details on variance) |
You are encouraged to solve this task according to the task description, using any language you may know.
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
<lang 360asm>* 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</lang>
- Output:
1 0 0 -8
0 1 0 1
0 0 1 -2
ActionScript
_m being of type Vector.<Vector.<Number>> the following function is a method of Matrix class. Therefore return this statements are returning the Matrix object itself.
<lang Actionscript>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;
}</lang>
Ada
matrices.ads: <lang Ada>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;</lang>
matrices.adb: <lang Ada>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;</lang>
Example use: main.adb: <lang Ada>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;</lang>
- Output:
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
<lang aime>void rref(list l, integer rows, integer columns) {
integer e, i, j, lead, r; list u, v;
lead = 0;
r = 0;
while (r < rows) {
if (columns <= lead) {
break;
}
i = r;
while (!l_q_integer(l_q_list(l, i), lead)) {
i += 1;
if (i == rows) {
i = r;
lead += 1;
if (lead == columns) {
break;
}
}
}
if (lead == columns) {
break;
}
u = l_q_list(l, i);
l_spin(l, i, r);
e = l_q_integer(u, lead);
if (e) {
j = 0;
while (j < columns) {
l_r_integer(u, j, l_q_integer(u, j) / e);
j += 1;
}
}
i = 0;
while (i < rows) {
if (i != r) {
v = l_q_list(l, i);
e = l_q_integer(v, lead);
j = 0;
while (j < columns) {
l_r_integer
(v, j, l_q_integer(v, j) - l_q_integer(u, j) * e);
j += 1;
}
}
i += 1;
}
lead += 1;
r += 1; }
}
void display_2(list l, integer rows, integer columns) {
integer i, j; list u;
i = 0;
while (i < rows) {
u = l_q_list(l, i);
j = 0;
while (j < columns) {
o_winteger(4, l_q_integer(u, j));
j += 1;
}
i += 1;
o_byte('\n');
}
}
integer main(void) {
list l;
l = l_effect(l_effect(1, 2, -1, -4),
l_effect(2, 3, -1, -11),
l_effect(-2, 0, -3, 22));
rref(l, 3, 4);
display_2(l, 3, 4);
return 0;
}</lang>
ALGOL 68
<lang algol68>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, $l$))</lang>
- Output:
(( 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
<lang 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 </lang>
- Output:
[1,0,0,-8] [-0,1,0,1] [-0,-0,1,-2]
BBC BASIC
<lang bbcbasic> 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</lang>
- Output:
1 0 0 -8
0 1 0 1
0 0 1 -2
C
<lang 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;
}</lang>
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.
<lang cpp>#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;
}</lang>
- Output:
1 0 0 -8 -0 1 0 1 -0 -0 1 -2
C#
<lang csharp>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;
}
}
}</lang>
Common Lisp
Direct implementation of the pseudo-code given. <lang lisp>(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)))))</lang>
D
<lang 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);
}</lang>
- Output:
1 0 0 -8 0 1 0 1 0 0 1 -2
Euphoria
<lang 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 } })</lang>
- Output:
{
{1,0,0,-8},
{0,1,0,1},
{0,0,1,-2}
}
Fortran
<lang 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</lang>
<lang fortran>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</lang>
Go
2D representation
From WP pseudocode: <lang go>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++
}
}</lang>
- Output:
(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
<lang go>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:")
}</lang>
- Output:
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. <lang groovy>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
}</lang>
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. <lang groovy>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()</lang>
- Output:
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.
<lang haskell>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</lang>
Icon and Unicon
Works in both languages: <lang unicon>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</lang>
- Output:
->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 linear.ijs script, available as part of the math/misc addon.
<lang j> ]mymatrix=: _4]\ 1 2 _1 _4 2 3 _1 _11 _2 0 _3 22
1 2 _1 _4 2 3 _1 _11
_2 0 _3 22
require 'math/misc/linear' gauss_jordan mymatrix
1 0 0 _8 0 1 0 1 0 0 1 _2</lang>
Additional examples, recommended on talk page:
<lang j>
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</lang>
And:
<lang j>mat=:".;._2]0 :0
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</lang>
Java
This requires Apache Commons 2.2+ <lang java>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"); } }</lang>
JavaScript
for the print() function.
Extends the Matrix class defined at Matrix Transpose#JavaScript <lang 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());</lang>
- Output:
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
Julia comes with a built-in 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
Lua
<lang 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</lang>
- Output:
1 0 0 -8 0 1 0 1 0 0 1 -2
Maple
<lang Maple> with(LinearAlgebra):
ReducedRowEchelonForm(<<1,2,-2>|<2,3,0>|<-1,-1,-3>|<-4,-11,22>>); </lang>
- Output:
[1 0 0 -8]
[ ]
[0 1 0 1]
[ ]
[0 0 1 -2]
Mathematica
<lang Mathematica>RowReduce[{{1, 2, -1, -4}, {2, 3, -1, -11}, {-2, 0, -3, 22}}]</lang> gives back: <lang Mathematica>{{1, 0, 0, -8}, {0, 1, 0, 1}, {0, 0, 1, -2}}</lang>
MATLAB
<lang MATLAB>rref([1, 2, -1, -4; 2, 3, -1, -11; -2, 0, -3, 22])</lang>
Maxima
<lang 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])</lang>
Objeck
<lang 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;
}
} </lang>
OCaml
<lang 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</lang>
Another implementation: <lang OCaml>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</lang>
Octave
<lang octave>A = [ 1, 2, -1, -4; 2, 3, -1, -11; -2, 0, -3, 22]; refA = rref(A); disp(refA);</lang>
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:
<lang parigp>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]))
};</lang> Example: <lang parigp>rref([1,2,-1,-4;2,3,-1,-11;-2,0,-3,22])</lang>
- Output:
%1 = [1 0 0 -8] [0 1 0 1] [0 0 1 -2]
Perl
Note that the function defined here takes an array reference, not an array. <lang perl>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] };}
++$lead;}}</lang>
Perl 6
<lang perl6>sub rref (@m) {
@m or return; my ($lead, $rows, $cols) = 0, +@m, +@m[0];
for ^$rows -> $r {
return @m if $lead >= $cols;
my $i = $r;
until @m[$i][$lead] {
++$i == $rows or next;
$i = $r;
++$lead == $cols and return @m;
}
@m[$i, $r] = @m[$r, $i];
my $lv = @m[$r][$lead];
@m[$r] »/=» $lv;
for ^$rows -> $n {
next if $n == $r;
@m[$n] »-=» @m[$r] »*» @m[$n][$lead];
}
++$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";
}</lang>
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.
- Output:
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: <lang perl6>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;</lang>
And the same code, recast into OO. Also, scale and shear are recast as unscale and unshear, which fit the problem better. <lang perl6>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 @($M);</lang>
Note that both versions can be simplified using Z+=, Z-=, X*=, and X/= to scale and shear. Currently, Rakudo has a bug related to Xop= and Zop=.
Note that the negative zeros in the output are innocuous, and also occur in the Perl 5 version.
Phix
<lang Phix>function ToReducedRowEchelonForm(sequence M) integer lead = 1,
rowCount = length(M),
columnCount = length(M[1]),
i
for r=1 to rowCount do
if lead>=columnCount then exit end if
i = r
while M[i][lead]=0 do
i += 1
if i=rowCount then
i = r
lead += 1
if lead=columnCount then exit end if
end if
end while
-- nb M[i] is assigned before M[r], which matters when i=r:
{M[r],M[i]} = {sq_div(M[i],M[i][lead]),M[r]}
for j=1 to rowCount do
if j!=r then
M[j] = sq_sub(M[j],sq_mul(M[j][lead],M[r]))
end if
end for
lead += 1
end for
return M
end function
? ToReducedRowEchelonForm(
{ { 1, 2, -1, -4 },
{ 2, 3, -1, -11 },
{ -2, 0, -3, 22 } })</lang>
- Output:
{{1,0,0,-8},{0,1,0,1},{0,0,1,-2}}
PHP
<lang php><?php
function rref($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;
} ?></lang>
PicoLisp
<lang 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 )</lang>
- Output:
(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
<lang 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) )</lang>
R
<lang rsplus>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))</lang>
Racket
<lang 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]]))
</lang>
- Output:
(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. <lang rexx>/*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) /*remember max number of cols.*/
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
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' row", column" c'.'; exit 13
end
_=_ right(@.r.c,w)
end /*c*/
say _ /*display a row of the matrix to screen*/
end /*r*/
return</lang>
- output 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
Ruby
<lang ruby># 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)</lang>
- Output:
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
<lang sage>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] </lang>
Scheme
<lang scheme>(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)))</lang>
Example: <lang scheme>(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)</lang>
- Output:
<lang>((1 0 0 -8) (0 1 0 1) (0 0 1 -2))</lang>
Seed7
<lang 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;</lang>
Original source: [1]
Sidef
<lang ruby>func rref (Array m) {
m.is_empty && return; var (lead, rows, cols) = (0, m.len, m[0].len);
rows.range.each { |r|
lead >= cols && return m;
var i = r;
while (!m[i][lead]) {
++i == rows || next;
i = r;
++lead == cols && return m;
}
m[i, r] = m[r, i];
var lv = m[r][lead];
m[r] = (m[r] »/» lv);
rows.range.each { |n|
n == r && next;
m[n] = (m[n] »-« (m[r] «*« m[n][lead]))
}
++lead;
}
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 ;
}</lang>
- Output:
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
<lang Swift> var lead = 0 var rowCount = eCount var columnCount = mCount for (var r = 0; r < rowCount; ++r) { if (columnCount <= lead) {
break }
var i = r while (matrix[i][lead] == 0) {
++i
if (i == rowCount) {
i = r
++lead
if (columnCount == lead) {
--lead break
} }
} for (var j = 0; j < columnCount; ++j) {
var temp = matrix[r][j] matrix[r][j] = matrix[i][j] matrix[i][j] = temp
} var div = matrix[r][lead] if (div != 0) {
for (var j = 0; j < columnCount; ++j) {
matrix[r][j] /= div
}
} for (var j = 0; j < rowCount; ++j) {
if (j != r) {
var sub = matrix[j][lead]
for (var k = 0; k < columnCount; ++k) {
matrix[j][k] -= (sub * matrix[r][k])
} }
} ++lead }</lang>
Tcl
Using utility procs defined at Matrix Transpose#Tcl <lang 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]</lang>
- Output:
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
TI-89 BASIC
<lang ti89b>rref([1,2,–1,–4; 2,3,–1,–11; –2,0,–3,22])</lang>
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.
<lang Ursala>#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.>></lang>
- Output:
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. <lang Ursala>#import lin
rref = @ySzSX msolve; ^plrNCTS\~& ~&iiDlSzyCK9+ :/1.+ 0.!*t</lang>
Visual FoxPro
Translation of Fortran. <lang vfp> 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 </lang>
- Output:
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: <lang zkl>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
}</lang> <lang zkl>A:=GSL.Matrix(3,4).set( 1, 2, -1, -4, 2, 3, -1, -11, -2, 0, -3, 22); toReducedRowEchelonForm(A).format(5,1).println();</lang>
- Output:
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. <lang zkl>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
}</lang> <lang zkl>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()) }</lang>
- Output:
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
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