# Element-wise operations

Element-wise operations
You are encouraged to solve this task according to the task description, using any language you may know.

Implement basic element-wise matrix-matrix and scalar-matrix operations, which can be referred to in other, higher-order tasks.

Implement:

•   subtraction
•   multiplication
•   division
•   exponentiation

Extend the task if necessary to include additional basic operations, which should not require their own specialised task.

Using Generics, the task is quite trivial in Ada. Here is the main program:

`with Ada.Text_IO, Matrix_Scalar; procedure Scalar_Ops is    subtype T is Integer range 1 .. 3;    package M is new Matrix_Scalar(T, T, Integer);    -- the functions to solve the task        function "+" is new M.Func("+");        function "-" is new M.Func("-");        function "*" is new M.Func("*");        function "/" is new M.Func("/");        function "**" is new M.Func("**");        function "mod" is new M.Func("mod");    -- for output purposes, we need a Matrix->String conversion        function Image is new M.Image(Integer'Image);    A: M.Matrix := ((1,2,3),(4,5,6),(7,8,9)); -- something to begin with begin   Ada.Text_IO.Put_Line("  Initial M=" & Image(A));   Ada.Text_IO.Put_Line("        M+2=" & Image(A+2));   Ada.Text_IO.Put_Line("        M-2=" & Image(A-2));   Ada.Text_IO.Put_Line("        M*2=" & Image(A*2));   Ada.Text_IO.Put_Line("        M/2=" & Image(A/2));   Ada.Text_IO.Put_Line("  square(M)=" & Image(A ** 2));   Ada.Text_IO.Put_Line("    M mod 2=" & Image(A mod 2));   Ada.Text_IO.Put_Line("(M*2) mod 3=" & Image((A*2) mod 3));end Scalar_Ops;`
Output:
```  Initial M=((1,2,3),(4,5,6),(7,8,9))
M+2=((3,4,5),(6,7,8),(9,10,11))
M-2=((-1,0,1),(2,3,4),(5,6,7))
M*2=((2,4,6),(8,10,12),(14,16,18))
M/2=((0,1,1),(2,2,3),(3,4,4))
square(M)=((1,4,9),(16,25,36),(49,64,81))
M mod 2=((1,0,1),(0,1,0),(1,0,1))
(M*2) mod 3=((2,1,0),(2,1,0),(2,1,0))```

Our main program uses a generic package Matrix_Scalar. Here is the specification:

`generic   type Rows is (<>);   type Cols is (<>);   type Num is private;package Matrix_Scalar is   type Matrix is array(Rows, Cols) of Num;    generic      with function F(L, R: Num) return Num;   function Func(Left: Matrix; Right: Num) return Matrix;    generic      with function Image(N: Num) return String;   function Image(M: Matrix) return String; end Matrix_Scalar;`

And here is the corresponding implementation. Note that the function Image (which we just use to output the results) takes much more lines than the function Func we need for actually solving the task:

`package body Matrix_Scalar is    function Func(Left: Matrix; Right: Num) return Matrix is      Result: Matrix;   begin      for R in Rows loop         for C in Cols loop            Result(R,C) := F(Left(R,C), Right);         end loop;      end loop;      return Result;   end Func;    function Image(M: Matrix) return String is       function Img(R: Rows) return String is          function I(C: Cols) return String is            S: String := Image(M(R,C));            L: Positive := S'First;         begin            while S(L) = ' ' loop               L := L + 1;            end loop;            if C=Cols'Last then               return S(L .. S'Last);            else               return S(L .. S'Last) & "," & I(Cols'Succ(C));            end if;         end I;          Column: String := I(Cols'First);      begin         if R=Rows'Last then            return "(" & Column & ")";         else            return "(" & Column & ")," & Img(Rows'Succ(R));         end if;      end Img;    begin      return("(" & Img(Rows'First) & ")");   end Image; end Matrix_Scalar;`

## ALGOL 68

Translation of: D
Note: This specimen retains the original D coding style.
Works with: ALGOL 68 version Revision 1 - no extensions to language used.
Works with: ALGOL 68G version Any - tested with release 1.18.0-9h.tiny.
`#!/usr/local/bin/a68g --script # MODE SCALAR = REAL;FORMAT scalar fmt = \$g(0, 2)\$; MODE MATRIX = [3, 3]SCALAR;FORMAT vector fmt = \$"("n(2 UPB LOC MATRIX - 2 LWB LOC MATRIX)(f(scalar fmt)", ")f(scalar fmt)")"\$;FORMAT matrix fmt = \$"("n(1 UPB LOC MATRIX - 1 LWB LOC MATRIX)(f(vector fmt)","l" ")f(vector fmt)")"\$; PROC elementwise op = (PROC(SCALAR, SCALAR)SCALAR op, MATRIX a, UNION(SCALAR, MATRIX) b)MATRIX: (  [LWB a:UPB a, 2 LWB a:2 UPB a]SCALAR out;  CASE b IN  (SCALAR b):    FOR i FROM LWB out TO UPB out DO      FOR j FROM 2 LWB out TO 2 UPB out DO        out[i, j]:=op(a[i, j], b)      OD    OD,  (MATRIX b):    FOR i FROM LWB out TO UPB out DO      FOR j FROM 2 LWB out TO 2 UPB out DO        out[i, j]:=op(a[i, j], b[i, j])      OD    OD  ESAC;  out); PROC plus  = (SCALAR a, b)SCALAR: a+b,     minus = (SCALAR a, b)SCALAR: a-b,     times = (SCALAR a, b)SCALAR: a*b,     div   = (SCALAR a, b)SCALAR: a/b,     pow   = (SCALAR a, b)SCALAR: a**b; main:(    SCALAR scalar := 10;    MATRIX matrix = (( 7, 11, 13),                     (17, 19, 23),                     (29, 31, 37));     printf((\$f(matrix fmt)";"l\$,      elementwise op(plus,  matrix, scalar),      elementwise op(minus, matrix, scalar),      elementwise op(times, matrix, scalar),      elementwise op(div,   matrix, scalar),      elementwise op(pow,   matrix, scalar),       elementwise op(plus,  matrix, matrix),      elementwise op(minus, matrix, matrix),      elementwise op(times, matrix, matrix),      elementwise op(div,   matrix, matrix),      elementwise op(pow,   matrix, matrix)  )))`
Output:
```((17.00, 21.00, 23.00),
(27.00, 29.00, 33.00),
(39.00, 41.00, 47.00));
((-3.00, 1.00, 3.00),
(7.00, 9.00, 13.00),
(19.00, 21.00, 27.00));
((70.00, 110.00, 130.00),
(170.00, 190.00, 230.00),
(290.00, 310.00, 370.00));
((.70, 1.10, 1.30),
(1.70, 1.90, 2.30),
(2.90, 3.10, 3.70));
((282475249.00, 25937424601.00, 137858491849.00),
(2015993900449.00, 6131066257800.99, 41426511213648.90),
(420707233300200.00, 819628286980799.00, 4808584372417840.00));
((14.00, 22.00, 26.00),
(34.00, 38.00, 46.00),
(58.00, 62.00, 74.00));
((.00, .00, .00),
(.00, .00, .00),
(.00, .00, .00));
((49.00, 121.00, 169.00),
(289.00, 361.00, 529.00),
(841.00, 961.00, 1369.00));
((1.00, 1.00, 1.00),
(1.00, 1.00, 1.00),
(1.00, 1.00, 1.00));
((823543.00, 285311670611.00, 302875106592253.00),
(827240261886340000000.00, 1978419655660300000000000.00, 20880467999847700000000000000000.00),
(2567686153161210000000000000000000000000000.00, 17069174130723200000000000000000000000000000000.00, 10555134955777600000000000000000000000000000000000000000000.00));
```

## BBC BASIC

All except exponentiation (^) are native operations in BBC BASIC.

`      DIM a(1,2), b(1,2), c(1,2)      a() = 7, 8, 7, 4, 0, 9 : b() = 4, 5, 1, 6, 2, 1       REM Matrix-Matrix:      c() = a() + b() : PRINT FNshowmm(a(), "+", b(), c())      c() = a() - b() : PRINT FNshowmm(a(), "-", b(), c())      c() = a() * b() : PRINT FNshowmm(a(), "*", b(), c())      c() = a() / b() : PRINT FNshowmm(a(), "/", b(), c())      PROCpowmm(a(), b(), c()) : PRINT FNshowmm(a(), "^", b(), c()) '       REM Matrix-Scalar:      c() = a() + 3 : PRINT FNshowms(a(), "+", 3, c())      c() = a() - 3 : PRINT FNshowms(a(), "-", 3, c())      c() = a() * 3 : PRINT FNshowms(a(), "*", 3, c())      c() = a() / 3 : PRINT FNshowms(a(), "/", 3, c())      PROCpowms(a(), 3, c()) : PRINT FNshowms(a(), "^", 3, c())      END       DEF PROCpowmm(a(), b(), c())      LOCAL i%, j%      FOR i% = 0 TO DIM(a(),1)        FOR j% = 0 TO DIM(a(),2)          c(i%,j%) = a(i%,j%) ^ b(i%,j%)        NEXT      NEXT      ENDPROC       DEF PROCpowms(a(), b, c())      LOCAL i%, j%      FOR i% = 0 TO DIM(a(),1)        FOR j% = 0 TO DIM(a(),2)          c(i%,j%) = a(i%,j%) ^ b        NEXT      NEXT      ENDPROC       DEF FNshowmm(a(), op\$, b(), c())      = FNlist(a()) + " " + op\$ + " " + FNlist(b()) + " = " + FNlist(c())       DEF FNshowms(a(), op\$, b, c())      = FNlist(a()) + " " + op\$ + " " + STR\$(b) + " = " + FNlist(c())       DEF FNlist(a())      LOCAL i%, j%, a\$      a\$ = "["      FOR i% = 0 TO DIM(a(),1)        a\$ += "["        FOR j% = 0 TO DIM(a(),2)          a\$ += STR\$(a(i%,j%)) + ", "        NEXT        a\$ = LEFT\$(LEFT\$(a\$)) + "]"      NEXT      = a\$ + "]"`
Output:
```[[7, 8, 7][4, 0, 9]] + [[4, 5, 1][6, 2, 1]] = [[11, 13, 8][10, 2, 10]]
[[7, 8, 7][4, 0, 9]] - [[4, 5, 1][6, 2, 1]] = [[3, 3, 6][-2, -2, 8]]
[[7, 8, 7][4, 0, 9]] * [[4, 5, 1][6, 2, 1]] = [[28, 40, 7][24, 0, 9]]
[[7, 8, 7][4, 0, 9]] / [[4, 5, 1][6, 2, 1]] = [[1.75, 1.6, 7][0.666666667, 0, 9]]
[[7, 8, 7][4, 0, 9]] ^ [[4, 5, 1][6, 2, 1]] = [[2401, 32768, 7][4096, 0, 9]]

[[7, 8, 7][4, 0, 9]] + 3 = [[10, 11, 10][7, 3, 12]]
[[7, 8, 7][4, 0, 9]] - 3 = [[4, 5, 4][1, -3, 6]]
[[7, 8, 7][4, 0, 9]] * 3 = [[21, 24, 21][12, 0, 27]]
[[7, 8, 7][4, 0, 9]] / 3 = [[2.33333333, 2.66666667, 2.33333333][1.33333333, 0, 3]]
[[7, 8, 7][4, 0, 9]] ^ 3 = [[343, 512, 343][64, 0, 729]]
```

## C

Matrices are 2D double arrays.

`#include <math.h> #define for_i for(i = 0; i < h; i++)#define for_j for(j = 0; j < w; j++)#define _M double**#define OPM(name, _op_) \	void eop_##name(_M a, _M b, _M c, int w, int h){int i,j;\		for_i for_j c[i][j] = a[i][j] _op_ b[i][j];}OPM(add, +);OPM(sub, -);OPM(mul, *);OPM(div, /); #define OPS(name, res) \	void eop_s_##name(_M a, double s, _M b, int w, int h) {double x;int i,j;\		for_i for_j {x = a[i][j]; b[i][j] = res;}}OPS(mul, x*s);OPS(div, x/s);OPS(add, x+s);OPS(sub, x-s);OPS(pow, pow(x, s));`

## C#

`using System;using System.Collections.Generic;using System.Linq; public static class ElementWiseOperations{    private static readonly Dictionary<string, Func<double, double, double>> operations =        new Dictionary<string, Func<double, double, double>> {            { "add", (a, b) => a + b },            { "sub", (a, b) => a - b },            { "mul", (a, b) => a * b },            { "div", (a, b) => a / b },            { "pow", (a, b) => Math.Pow(a, b) }        };     private static readonly Func<double, double, double> nothing = (a, b) => a;     public static double[,] DoOperation(this double[,] m, string name, double[,] other) =>        DoOperation(m, operations.TryGetValue(name, out var operation) ? operation : nothing, other);     public static double[,] DoOperation(this double[,] m, Func<double, double, double> operation, double[,] other) {        if (m == null || other == null) throw new ArgumentNullException();        int rows = m.GetLength(0), columns = m.GetLength(1);        if (rows != other.GetLength(0) || columns != other.GetLength(1)) {            throw new ArgumentException("Matrices have different dimensions.");        }         double[,] result = new double[rows, columns];        for (int r = 0; r < rows; r++) {            for (int c = 0; c < columns; c++) {                result[r, c] = operation(m[r, c], other[r, c]);            }        }        return result;    }     public static double[,] DoOperation(this double[,] m, string name, double number) =>        DoOperation(m, operations.TryGetValue(name, out var operation) ? operation : nothing, number);     public static double[,] DoOperation(this double[,] m, Func<double, double, double> operation, double number) {        if (m == null) throw new ArgumentNullException();        int rows = m.GetLength(0), columns = m.GetLength(1);        double[,] result = new double[rows, columns];        for (int r = 0; r < rows; r++) {            for (int c = 0; c < columns; c++) {                result[r, c] = operation(m[r, c], number);            }        }        return result;    }     public static void Print(this double[,] m) {        if (m == null) throw new ArgumentNullException();        int rows = m.GetLength(0), columns = m.GetLength(1);        for (int r = 0; r < rows; r++) {            Console.WriteLine("[ " + string.Join(", ", Enumerable.Range(0, columns).Select(c => m[r, c])) + " ]");        }    } } public class Program{    public static void Main() {        double[,] matrix = {            { 1, 2, 3, 4 },            { 5, 6, 7, 8 },            { 9, 10, 11, 12 }        };         double[,] tens = {            { 10, 10, 10, 10 },            { 20, 20, 20, 20 },            { 30, 30, 30, 30 }        };         matrix.Print();        WriteLine();         (matrix = matrix.DoOperation("add", tens)).Print();        WriteLine();         matrix.DoOperation((a, b) => b - a, 100).Print();    }}`
Output:
```[ 1, 2, 3, 4 ]
[ 5, 6, 7, 8 ]
[ 9, 10, 11, 12 ]

[ 11, 12, 13, 14 ]
[ 25, 26, 27, 28 ]
[ 39, 40, 41, 42 ]

[ 89, 88, 87, 86 ]
[ 75, 74, 73, 72 ]
[ 61, 60, 59, 58 ]```

## Clojure

This function is for vector matrices; for list matrices, change the (vector?) function to the (list?) function and remove all the (vec) functions.

`(defn initial-mtx [i1 i2 value]  (vec (repeat i1 (vec (repeat i2 value))))) (defn operation [f mtx1 mtx2]  (if (vector? mtx1)    (vec (map #(vec (map f %1 %2)) mtx1 mtx2)))    (recur f (initial-mtx (count mtx2) (count (first mtx2)) mtx1) mtx2)   ))`

The mtx1 argument can either be a matrix or scalar; the function will sort the difference.

## Common Lisp

Element-wise matrix-matrix operations. Matrices are represented as 2D-arrays.

`(defun element-wise-matrix (fn A B)  (let* ((len (array-total-size A))         (m   (car (array-dimensions A)))         (n   (cadr (array-dimensions A)))         (C   (make-array `(,m ,n) :initial-element 0.0d0)))     (loop for i from 0 to (1- len) do         (setf (row-major-aref C i)                (funcall fn                        (row-major-aref A i)                        (row-major-aref B i))))    C)) ;; A.+B, A.-B, A.*B, A./B, A.^B.(defun m+ (A B) (element-wise-matrix #'+    A B))(defun m- (A B) (element-wise-matrix #'-    A B))(defun m* (A B) (element-wise-matrix #'*    A B))(defun m/ (A B) (element-wise-matrix #'/    A B))(defun m^ (A B) (element-wise-matrix #'expt A B))`

Elementwise scalar-matrix operations.

`(defun element-wise-scalar (fn A c)  (let* ((len (array-total-size A))         (m   (car (array-dimensions A)))         (n   (cadr (array-dimensions A)))         (B   (make-array `(,m ,n) :initial-element 0.0d0)))     (loop for i from 0 to (1- len) do         (setf (row-major-aref B i)                (funcall fn                        (row-major-aref A i)                        c)))    B)) ;; c.+A, A.-c, c.*A, A./c, A.^c.(defun .+ (c A) (element-wise-scalar #'+    A c))(defun .- (A c) (element-wise-scalar #'-    A c))(defun .* (c A) (element-wise-scalar #'*    A c))(defun ./ (A c) (element-wise-scalar #'/    A c))(defun .^ (A c) (element-wise-scalar #'expt A c))`

## D

`import std.stdio, std.typetuple, std.traits; T[][] elementwise(string op, T, U)(in T[][] A, in U B) {  auto R = new typeof(return)(A.length, A[0].length);  foreach (r, row; A)    R[r][] = mixin("row[] " ~ op ~ (isNumeric!U ? "B" : "B[r][]"));  return R;} void main() {  const M = [[3, 5, 7], [1, 2, 3], [2, 4, 6]];  foreach (op; TypeTuple!("+", "-", "*", "/", "^^"))    writefln("%s:\n[%([%(%d, %)],\n %)]]\n\n[%([%(%d, %)],\n %)]]\n",             op, elementwise!op(M, 2), elementwise!op(M, M));}`
Output:
```+:
[[5, 7, 9],
[3, 4, 5],
[4, 6, 8]]

[[6, 10, 14],
[2, 4, 6],
[4, 8, 12]]

-:
[[1, 3, 5],
[-1, 0, 1],
[0, 2, 4]]

[[0, 0, 0],
[0, 0, 0],
[0, 0, 0]]

*:
[[6, 10, 14],
[2, 4, 6],
[4, 8, 12]]

[[9, 25, 49],
[1, 4, 9],
[4, 16, 36]]

/:
[[1, 2, 3],
[0, 1, 1],
[1, 2, 3]]

[[1, 1, 1],
[1, 1, 1],
[1, 1, 1]]

^^:
[[9, 25, 49],
[1, 4, 9],
[4, 16, 36]]

[[27, 3125, 823543],
[1, 4, 27],
[4, 256, 46656]]```

This alternative version offers more guarantees, same output:

`import std.stdio, std.typetuple, std.traits; T[][] elementwise(string op, T, U)(in T[][] A, in U B)@safe pure nothrowif (isNumeric!U || (isArray!U && isArray!(ForeachType!U) &&    isNumeric!(ForeachType!(ForeachType!U)))) {    static if (!isNumeric!U)        assert(A.length == B.length);    if (!A.length)        return null;    auto R = new typeof(return)(A.length, A[0].length);     foreach (immutable r, const row; A)        static if (isNumeric!U) {            R[r][] = mixin("row[] " ~ op ~ "B");        } else {            assert(row.length == B[r].length);            R[r][] = mixin("row[] " ~ op ~ "B[r][]");        }     return R;} void main() {    enum scalar = 2;    enum matFormat = "[%([%(%d, %)],\n %)]]\n";    immutable matrix = [[3, 5, 7],                        [1, 2, 3],                        [2, 4, 6]];     foreach (immutable op; TypeTuple!("+", "-", "*", "/", "^^")) {        writeln(op, ":");        writefln(matFormat, elementwise!op(matrix, scalar));        writefln(matFormat, elementwise!op(matrix, matrix));    }}`

## Factor

The `math.matrices` vocabulary provides matrix-matrix and matrix-scalar arithmetic words. I wasn't able to find any for exponentiation, so I wrote them.

`USING: combinators.extras formatting kernel math.functionsmath.matrices math.vectors prettyprint sequences ; : show ( a b words -- )    [        3dup execute( x x -- x ) [ unparse ] dip        "%u %u %s = %u\n" printf    ] 2with each ; inline : m^n ( m n -- m ) [ ^ ] curry matrix-map ;: m^  ( m m -- m ) [ v^ ] 2map ; { { 1 2 } { 3 4 } } { { 5 6 } { 7 8 } } { m+ m- m* m/ m^ }{ { -1 9 4 } { 5 -13 0 } } 3 { m+n m-n m*n m/n m^n }[ show ] [email protected]`
Output:
```{ { 1 2 } { 3 4 } } { { 5 6 } { 7 8 } } m+ = { { 6 8 } { 10 12 } }
{ { 1 2 } { 3 4 } } { { 5 6 } { 7 8 } } m- = { { -4 -4 } { -4 -4 } }
{ { 1 2 } { 3 4 } } { { 5 6 } { 7 8 } } m* = { { 5 12 } { 21 32 } }
{ { 1 2 } { 3 4 } } { { 5 6 } { 7 8 } } m/ = { { 1/5 1/3 } { 3/7 1/2 } }
{ { 1 2 } { 3 4 } } { { 5 6 } { 7 8 } } m^ = { { 1 64 } { 2187 65536 } }
{ { -1 9 4 } { 5 -13 0 } } 3 m+n = { { 2 12 7 } { 8 -10 3 } }
{ { -1 9 4 } { 5 -13 0 } } 3 m-n = { { -4 6 1 } { 2 -16 -3 } }
{ { -1 9 4 } { 5 -13 0 } } 3 m*n = { { -3 27 12 } { 15 -39 0 } }
{ { -1 9 4 } { 5 -13 0 } } 3 m/n = { { -1/3 3 1+1/3 } { 1+2/3 -4-1/3 0 } }
{ { -1 9 4 } { 5 -13 0 } } 3 m^n = { { -1 729 64 } { 125 -2197 0 } }
```

## Fortran

All element based operations are suported by default in Fortran(90+)

` program element_operations  implicit none   real(kind=4), dimension(3,3) :: a,b  integer :: i   a=reshape([(i,i=1,9)],shape(a))   print*,'addition'  b=a+a  call print_arr(b)   print*,'multiplication'  b=a*a  call print_arr(b)   print*,'division'  b=a/b  call print_arr(b)   print*,'exponentiation'  b=a**a  call print_arr(b)   print*,'trignometric'  b=cos(a)  call print_arr(b)   print*,'mod'  b=mod(int(a),3)  call print_arr(b)   print*,'element selection'  b=0  where(a>3) b=1  call print_arr(b)     print*,'elemental functions can be applied to single values:'  print*,square(3.0)  print*,'or element wise to arrays:'  b=square(a)  call print_arr(b)  contains   elemental real function square(a)    real, intent(in) :: a    square=a*a  end function square   subroutine print_arr(arr)    real, intent(in) :: arr(:,:)    integer :: i    do i=1,size(arr,dim=2)       print*,arr(:,i)    end do  end subroutine print_arr  end program element_operations `
Output:
``` addition
2.00000000       4.00000000       6.00000000
8.00000000       10.0000000       12.0000000
14.0000000       16.0000000       18.0000000
multiplication
1.00000000       4.00000000       9.00000000
16.0000000       25.0000000       36.0000000
49.0000000       64.0000000       81.0000000
division
1.00000000      0.500000000      0.333333343
0.250000000      0.200000003      0.166666672
0.142857149      0.125000000      0.111111112
exponentiation
1.00000000       4.00000000       27.0000000
256.000000       3125.00000       46656.0000
823543.000       16777216.0       387420480.
trignometric
0.540302277     -0.416146845     -0.989992499
-0.653643608      0.283662200      0.960170269
0.753902256     -0.145500034     -0.911130250
mod
1.00000000       2.00000000       0.00000000
1.00000000       2.00000000       0.00000000
1.00000000       2.00000000       0.00000000
element selection
0.00000000       0.00000000       0.00000000
1.00000000       1.00000000       1.00000000
1.00000000       1.00000000       1.00000000
elemental functions can be applied to single values:
9.00000000
or element wise to arrays:
1.00000000       4.00000000       9.00000000
16.0000000       25.0000000       36.0000000
49.0000000       64.0000000       81.0000000

```

## Go

A package, which can be referred to in other, higher-order tasks.

`package element import (    "fmt"    "math") 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 like(m Matrix) Matrix {    return Matrix{make([]float64, len(m.ele)), m.stride}} func (m Matrix) String() string {    s := ""    for e := 0; e < len(m.ele); e += m.stride {        s += fmt.Sprintf("%6.3f \n", m.ele[e:e+m.stride])    }    return s} type binaryFunc64 func(float64, float64) float64 func elementWiseMM(m1, m2 Matrix, f binaryFunc64) Matrix {    z := like(m1)    for i, m1e := range m1.ele {        z.ele[i] = f(m1e, m2.ele[i])    }    return z} func elementWiseMS(m Matrix, s float64, f binaryFunc64) Matrix {    z := like(m)    for i, e := range m.ele {        z.ele[i] = f(e, s)    }    return z} func add(a, b float64) float64 { return a + b }func sub(a, b float64) float64 { return a - b }func mul(a, b float64) float64 { return a * b }func div(a, b float64) float64 { return a / b }func exp(a, b float64) float64 { return math.Pow(a, b) } func AddMatrix(m1, m2 Matrix) Matrix { return elementWiseMM(m1, m2, add) }func SubMatrix(m1, m2 Matrix) Matrix { return elementWiseMM(m1, m2, sub) }func MulMatrix(m1, m2 Matrix) Matrix { return elementWiseMM(m1, m2, mul) }func DivMatrix(m1, m2 Matrix) Matrix { return elementWiseMM(m1, m2, div) }func ExpMatrix(m1, m2 Matrix) Matrix { return elementWiseMM(m1, m2, exp) } func AddScalar(m Matrix, s float64) Matrix { return elementWiseMS(m, s, add) }func SubScalar(m Matrix, s float64) Matrix { return elementWiseMS(m, s, sub) }func MulScalar(m Matrix, s float64) Matrix { return elementWiseMS(m, s, mul) }func DivScalar(m Matrix, s float64) Matrix { return elementWiseMS(m, s, div) }func ExpScalar(m Matrix, s float64) Matrix { return elementWiseMS(m, s, exp) }`

Package use:

`package main import (    "fmt"     "element") func h(heading string, m element.Matrix) {    fmt.Println(heading)    fmt.Print(m)} func main() {    m1 := element.MatrixFromRows([][]float64{{3, 1, 4}, {1, 5, 9}})    m2 := element.MatrixFromRows([][]float64{{2, 7, 1}, {8, 2, 8}})    h("m1:", m1)    h("m2:", m2)    fmt.Println()    h("m1 + m2:", element.AddMatrix(m1, m2))    h("m1 - m2:", element.SubMatrix(m1, m2))    h("m1 * m2:", element.MulMatrix(m1, m2))    h("m1 / m2:", element.DivMatrix(m1, m2))    h("m1 ^ m2:", element.ExpMatrix(m1, m2))    fmt.Println()    s := .5    fmt.Println("s:", s)    h("m1 + s:", element.AddScalar(m1, s))    h("m1 - s:", element.SubScalar(m1, s))    h("m1 * s:", element.MulScalar(m1, s))    h("m1 / s:", element.DivScalar(m1, s))    h("m1 ^ s:", element.ExpScalar(m1, s))}`
Output:
```m1:
[ 3.000  1.000  4.000]
[ 1.000  5.000  9.000]
m2:
[ 2.000  7.000  1.000]
[ 8.000  2.000  8.000]

m1 + m2:
[ 5.000  8.000  5.000]
[ 9.000  7.000 17.000]
m1 - m2:
[ 1.000 -6.000  3.000]
[-7.000  3.000  1.000]
m1 * m2:
[ 6.000  7.000  4.000]
[ 8.000 10.000 72.000]
m1 / m2:
[ 1.500  0.143  4.000]
[ 0.125  2.500  1.125]
m1 ^ m2:
[ 9.000  1.000  4.000]
[ 1.000 25.000 43046721.000]

s: 0.5
m1 + s:
[ 3.500  1.500  4.500]
[ 1.500  5.500  9.500]
m1 - s:
[ 2.500  0.500  3.500]
[ 0.500  4.500  8.500]
m1 * s:
[ 1.500  0.500  2.000]
[ 0.500  2.500  4.500]
m1 / s:
[ 6.000  2.000  8.000]
[ 2.000 10.000 18.000]
m1 ^ s:
[ 1.732  1.000  2.000]
[ 1.000  2.236  3.000]
```

Matrices are represented here as Immutable Arrays.

`{-# OPTIONS_GHC -fno-warn-duplicate-constraints #-}{-# LANGUAGE RankNTypes #-} import Data.Array (Array, Ix)import Data.Array.Base -- | Element-wise combine the values of two arrays 'a' and 'b' with 'f'.-- 'a' and 'b' must have the same bounds.zipWithA :: (IArray arr a, IArray arr b, IArray arr c, Ix i) =>            (a -> b -> c) -> arr i a -> arr i b -> arr i czipWithA f a b =  case bounds a of    ba ->      if ba /= bounds b      then error "elemwise: bounds mismatch"      else        let n = numElements a        in unsafeArray ba [ (i, f (unsafeAt a i) (unsafeAt b i))                          | i <- [0 .. n - 1]] -- Convenient aliases for matrix-matrix element-wise operations.type ElemOp a b c = (IArray arr a, IArray arr b, IArray arr c, Ix i) =>                    arr i a -> arr i b -> arr i ctype ElemOp1 a = ElemOp a a a infixl 6 +:, -:infixl 7 *:, /:, `divE` (+:), (-:), (*:) :: (Num a) => ElemOp1 a(+:) = zipWithA (+)(-:) = zipWithA (-)(*:) = zipWithA (*) divE :: (Integral a) => ElemOp1 adivE = zipWithA div (/:) :: (Fractional a) => ElemOp1 a(/:) = zipWithA (/) infixr 8 ^:, **:, ^^: (^:) :: (Num a, Integral b) => ElemOp a b a(^:) = zipWithA (^) (**:) :: (Floating a) => ElemOp1 a(**:) = zipWithA (**) (^^:) :: (Fractional a, Integral b) => ElemOp a b a(^^:) = zipWithA (^^) -- Convenient aliases for matrix-scalar element-wise operations.type ScalarOp a b c = (IArray arr a, IArray arr c, Ix i) =>                      arr i a -> b -> arr i ctype ScalarOp1 a = ScalarOp a a a samap :: (IArray arr a, IArray arr c, Ix i) =>         (a -> b -> c) -> arr i a -> b -> arr i csamap f a s = amap (`f` s) a infixl 6 +., -.infixl 7 *., /., `divS` (+.), (-.), (*.) :: (Num a) => ScalarOp1 a(+.) = samap (+)(-.) = samap (-)(*.) = samap (*) divS :: (Integral a) => ScalarOp1 adivS = samap div (/.) :: (Fractional a) => ScalarOp1 a(/.) = samap (/) infixr 8 ^., **., ^^. (^.) :: (Num a, Integral b) => ScalarOp a b a(^.) = samap (^) (**.) :: (Floating a) => ScalarOp1 a(**.) = samap (**) (^^.) :: (Fractional a, Integral b) => ScalarOp a b a(^^.) = samap (^^) main :: IO ()main = do  let m1, m2 :: (forall a. (Enum a, Num a) => Array (Int, Int) a)      m1 = listArray ((0, 0), (2, 3)) [1..]      m2 = listArray ((0, 0), (2, 3)) [10..]      s :: (forall a. Num a => a)      s = 99  putStrLn "m1"  print m1  putStrLn "m2"  print m2  putStrLn "s"  print s  putStrLn "m1 + m2"  print \$ m1 +: m2  putStrLn "m1 - m2"  print \$ m1 -: m2  putStrLn "m1 * m2"  print \$ m1 *: m2  putStrLn "m1 `div` m2"  print \$ m1 `divE` m2  putStrLn "m1 / m2"  print \$ m1 /: m2  putStrLn "m1 ^ m2"  print \$ m1 ^: m2  putStrLn "m1 ** m2"  print \$ m1 **: m2  putStrLn "m1 ^^ m2"  print \$ m1 ^^: m2  putStrLn "m1 + s"  print \$ m1 +. s  putStrLn "m1 - s"  print \$ m1 -. s  putStrLn "m1 * s"  print \$ m1 *. s  putStrLn "m1 `div` s"  print \$ m1 `divS` s  putStrLn "m1 / s"  print \$ m1 /. s  putStrLn "m1 ^ s"  print \$ m1 ^. s  putStrLn "m1 ** s"  print \$ m1 **. s  putStrLn "m1 ^^ s"  print \$ m1 ^^. s`
Output:
```m1
array ((0,0),(2,3)) [((0,0),1),((0,1),2),((0,2),3),((0,3),4),((1,0),5),((1,1),6),((1,2),7),((1,3),8),((2,0),9),((2,1),10),((2,2),11),((2,3),12)]
m2
array ((0,0),(2,3)) [((0,0),10),((0,1),11),((0,2),12),((0,3),13),((1,0),14),((1,1),15),((1,2),16),((1,3),17),((2,0),18),((2,1),19),((2,2),20),((2,3),21)]
s
99
m1 + m2
array ((0,0),(2,3)) [((0,0),11),((0,1),13),((0,2),15),((0,3),17),((1,0),19),((1,1),21),((1,2),23),((1,3),25),((2,0),27),((2,1),29),((2,2),31),((2,3),33)]
m1 - m2
array ((0,0),(2,3)) [((0,0),-9),((0,1),-9),((0,2),-9),((0,3),-9),((1,0),-9),((1,1),-9),((1,2),-9),((1,3),-9),((2,0),-9),((2,1),-9),((2,2),-9),((2,3),-9)]
m1 * m2
array ((0,0),(2,3)) [((0,0),10),((0,1),22),((0,2),36),((0,3),52),((1,0),70),((1,1),90),((1,2),112),((1,3),136),((2,0),162),((2,1),190),((2,2),220),((2,3),252)]
m1 `div` m2
array ((0,0),(2,3)) [((0,0),0),((0,1),0),((0,2),0),((0,3),0),((1,0),0),((1,1),0),((1,2),0),((1,3),0),((2,0),0),((2,1),0),((2,2),0),((2,3),0)]
m1 / m2
array ((0,0),(2,3)) [((0,0),0.1),((0,1),0.18181818181818182),((0,2),0.25),((0,3),0.3076923076923077),((1,0),0.35714285714285715),((1,1),0.4),((1,2),0.4375),((1,3),0.47058823529411764),((2,0),0.5),((2,1),0.5263157894736842),((2,2),0.55),((2,3),0.5714285714285714)]
m1 ^ m2
array ((0,0),(2,3)) [((0,0),1),((0,1),2048),((0,2),531441),((0,3),67108864),((1,0),6103515625),((1,1),470184984576),((1,2),33232930569601),((1,3),2251799813685248),((2,0),150094635296999121),((2,1),10000000000000000000),((2,2),672749994932560009201),((2,3),46005119909369701466112)]
m1 ** m2
array ((0,0),(2,3)) [((0,0),1.0),((0,1),2048.0),((0,2),531441.0),((0,3),6.7108864e7),((1,0),6.103515625e9),((1,1),4.70184984576e11),((1,2),3.3232930569601e13),((1,3),2.251799813685248e15),((2,0),1.5009463529699914e17),((2,1),1.0e19),((2,2),6.727499949325601e20),((2,3),4.60051199093697e22)]
m1 ^^ m2
array ((0,0),(2,3)) [((0,0),1.0),((0,1),2048.0),((0,2),531441.0),((0,3),6.7108864e7),((1,0),6.103515625e9),((1,1),4.70184984576e11),((1,2),3.3232930569601e13),((1,3),2.251799813685248e15),((2,0),1.5009463529699914e17),((2,1),1.0e19),((2,2),6.7274999493256e20),((2,3),4.60051199093697e22)]
m1 + s
array ((0,0),(2,3)) [((0,0),100),((0,1),101),((0,2),102),((0,3),103),((1,0),104),((1,1),105),((1,2),106),((1,3),107),((2,0),108),((2,1),109),((2,2),110),((2,3),111)]
m1 - s
array ((0,0),(2,3)) [((0,0),-98),((0,1),-97),((0,2),-96),((0,3),-95),((1,0),-94),((1,1),-93),((1,2),-92),((1,3),-91),((2,0),-90),((2,1),-89),((2,2),-88),((2,3),-87)]
m1 * s
array ((0,0),(2,3)) [((0,0),99),((0,1),198),((0,2),297),((0,3),396),((1,0),495),((1,1),594),((1,2),693),((1,3),792),((2,0),891),((2,1),990),((2,2),1089),((2,3),1188)]
m1 `div` s
array ((0,0),(2,3)) [((0,0),0),((0,1),0),((0,2),0),((0,3),0),((1,0),0),((1,1),0),((1,2),0),((1,3),0),((2,0),0),((2,1),0),((2,2),0),((2,3),0)]
m1 / s
array ((0,0),(2,3)) [((0,0),1.0101010101010102e-2),((0,1),2.0202020202020204e-2),((0,2),3.0303030303030304e-2),((0,3),4.040404040404041e-2),((1,0),5.0505050505050504e-2),((1,1),6.060606060606061e-2),((1,2),7.07070707070707e-2),((1,3),8.080808080808081e-2),((2,0),9.090909090909091e-2),((2,1),0.10101010101010101),((2,2),0.1111111111111111),((2,3),0.12121212121212122)]
m1 ^ s
array ((0,0),(2,3)) [((0,0),1),((0,1),633825300114114700748351602688),((0,2),171792506910670443678820376588540424234035840667),((0,3),401734511064747568885490523085290650630550748445698208825344),((1,0),1577721810442023610823457130565572459346412870218046009540557861328125),((1,1),108886437250011817682781711193009636756190618412159145257178661061582856912896),((1,2),462068072803536855906378252728602401551029028414946485847699333055955922805275437143),((1,3),254629497041810760783555711051172270131433549208242031329517556169297662470417088272924672),((2,0),29512665430652752148753480226197736314359272517043832886063884637676943433478020332709411004889),((2,1),1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000),((2,2),12527829399838427440107579247354215251149392000034969484678615956504532008683916069945559954314411495091),((2,3),69014978768345458548673686329780708168010234321157869622016822008604576610843435253147523608071501615464448)]
m1 ** s
array ((0,0),(2,3)) [((0,0),1.0),((0,1),6.338253001141147e29),((0,2),1.7179250691067045e47),((0,3),4.017345110647476e59),((1,0),1.5777218104420236e69),((1,1),1.0888643725001182e77),((1,2),4.620680728035369e83),((1,3),2.5462949704181076e89),((2,0),2.9512665430652752e94),((2,1),1.0e99),((2,2),1.2527829399838427e103),((2,3),6.901497876834546e106)]
m1 ^^ s
array ((0,0),(2,3)) [((0,0),1.0),((0,1),6.338253001141147e29),((0,2),1.7179250691067043e47),((0,3),4.017345110647476e59),((1,0),1.5777218104420238e69),((1,1),1.0888643725001181e77),((1,2),4.620680728035369e83),((1,3),2.5462949704181076e89),((2,0),2.9512665430652752e94),((2,1),1.0000000000000001e99),((2,2),1.2527829399838425e103),((2,3),6.901497876834545e106)]
```

## Icon and Unicon

This is a Unicon-specific solution solely because of the use of the [: ... :] operator. It would be easy to replace this with another construct to produce a version that works in both languages. The output flattens each displayed matrix onto a single line to save space here.

`procedure main()   a := [[1,2,3],[4,5,6],[7,8,9]]   b := [[9,8,7],[6,5,4],[3,2,1]]   showMat("  a: ",a)   showMat("  b: ",b)   showMat("a+b: ",mmop("+",a,b))   showMat("a-b: ",mmop("-",a,b))   showMat("a*b: ",mmop("*",a,b))   showMat("a/b: ",mmop("/",a,b))   showMat("a^b: ",mmop("^",a,b))   showMat("a+2: ",msop("+",a,2))   showMat("a-2: ",msop("-",a,2))   showMat("a*2: ",msop("*",a,2))   showMat("a/2: ",msop("/",a,2))   showMat("a^2: ",msop("^",a,2))end procedure mmop(op,A,B)    if (*A = *B) & (*A[1] = *B[1]) then {        C := [: |list(*A[1])\*A[1] :]        a1 := create !!A        b1 := create !!B        every (!!C) := op(@a1,@b1)        return C        }end procedure msop(op,A,s)    C := [: |list(*A[1])\*A[1] :]    a1 := create !!A    every (!!C) := op(@a1,s)    return Cend procedure showMat(label, m)    every writes(label | right(!!m,5) | "\n")end`
Output:
```->ewo
a:     1    2    3    4    5    6    7    8    9
b:     9    8    7    6    5    4    3    2    1
a+b:    10   10   10   10   10   10   10   10   10
a-b:    -8   -6   -4   -2    0    2    4    6    8
a*b:     9   16   21   24   25   24   21   16    9
a/b:     0    0    0    0    1    1    2    4    9
a^b:     1  256 2187 4096 3125 1296  343   64    9
a+2:     3    4    5    6    7    8    9   10   11
a-2:    -1    0    1    2    3    4    5    6    7
a*2:     2    4    6    8   10   12   14   16   18
a/2:     0    1    1    2    2    3    3    4    4
a^2:     1    4    9   16   25   36   49   64   81
->
```

## J

Solution: J's arithmetical primitives act elementwise by default (in J parlance, such operations are known as "scalar" or "rank zero", which means they generalize to high-order arrays transparently, operating elementwise). Thus:
`   scalar =: 10   vector =: 2 3 5   matrix =: 3 3 \$    7 11 13  17 19 23  29 31 37    scalar * scalar100   scalar * vector20 30 50   scalar * matrix 70 110 130170 190 230290 310 370    vector * vector4 9 25   vector * matrix 14  22  26 51  57  69145 155 185    matrix * matrix 49 121  169289 361  529841 961 1369`
And similarly for +, -, % (division), and ^ .

Note that in some branches of mathematics, it has been traditional to define multiplication such that it is not performed element-wise. This can introduce some complications (wp:Einstein notation is arguably the best approach for resolving those complexities in latex, when they occur frequently enough that mentioning and using the notation is not more complicated than explicitly describing the multiply-and-sum) and makes expressing element-wise multiplication complicated. J deals with this conflict by making its multiplication primitive be elementwise (consistent with the rest of the language) and by using a different verb (typically +/ .*) to represent the traditional non-element-wise multiply and sum operation.

## Java

` import java.util.Arrays;import java.util.HashMap;import java.util.Map;import java.util.function.BiFunction;import java.util.stream.Stream; @SuppressWarnings("serial")public class ElementWiseOp {	static final Map<String, BiFunction<Double, Double, Double>> OPERATIONS = new HashMap<String, BiFunction<Double, Double, Double>>() {		{			put("add", (a, b) -> a + b);			put("sub", (a, b) -> a - b);			put("mul", (a, b) -> a * b);			put("div", (a, b) -> a / b);			put("pow", (a, b) -> Math.pow(a, b));			put("mod", (a, b) -> a % b);		}	};	public static Double[][] scalarOp(String op, Double[][] matr, Double scalar) {		BiFunction<Double, Double, Double> operation = OPERATIONS.getOrDefault(op, (a, b) -> a);		Double[][] result = new Double[matr.length][matr[0].length];		for (int i = 0; i < matr.length; i++) {			for (int j = 0; j < matr[i].length; j++) {				result[i][j] = operation.apply(matr[i][j], scalar);			}		}		return result;	}	public static Double[][] matrOp(String op, Double[][] matr, Double[][] scalar) {		BiFunction<Double, Double, Double> operation = OPERATIONS.getOrDefault(op, (a, b) -> a);		Double[][] result = new Double[matr.length][Stream.of(matr).mapToInt(a -> a.length).max().getAsInt()];		for (int i = 0; i < matr.length; i++) {			for (int j = 0; j < matr[i].length; j++) {				result[i][j] = operation.apply(matr[i][j], scalar[i % scalar.length][j						% scalar[i % scalar.length].length]);			}		}		return result;	}	public static void printMatrix(Double[][] matr) {		Stream.of(matr).map(Arrays::toString).forEach(System.out::println);	}	public static void main(String[] args) {		printMatrix(scalarOp("mul", new Double[][] {				{ 1.0, 2.0, 3.0 }, 				{ 4.0, 5.0, 6.0 }, 				{ 7.0, 8.0, 9.0 }		}, 3.0)); 		printMatrix(matrOp("div", new Double[][] {				{ 1.0, 2.0, 3.0 }, 				{ 4.0, 5.0, 6.0 }, 				{ 7.0, 8.0, 9.0 }		}, new Double[][] {				{ 1.0, 2.0}, 				{ 3.0, 4.0} 		}));	}} `

## jq

The following definition of elementwise allows matrices of any type to be processed, e.g. the matrices could be string or object-valued, and they can be of mixed type.

The matrices also need not be rectangular or conformant, but the resultant matrix will be rectangular, with the same number of rows as self, and if that number is greater than 0, then the number of columns in the result will be the length of the first row of self.

In jq, it is idiomatic to specify an operation by using a jq filter. This means that composite and user-defined operations can be specified. In the following definition of "elementwise", the "operator" argument for addition, for example, would be given as (.[0] + .[1]) rather than the string "+".

In Part 2 below, a variation of "elementwise" is presented that does accept string specifications of common operators, e.g. "+" for addition. However this is done mainly for illustration and is not recommended, primarily because it introduces certain complexities.

Part 1

`# Occurrences of .[0] in "operator" will refer to an element in self,# and occurrences of .[1] will refer to the corresponding element in other.def elementwise( operator; other ):  length as \$rows  | if \$rows == 0 then .    else . as \$self    | other as \$other    | (\$self[0]|length) as \$cols    | reduce range(0; \$rows) as \$i        ([]; reduce range(0; \$cols) as \$j          (.; .[\$i][\$j] = ([\$self[\$i][\$j], \$other[\$i][\$j]] | operator) ) )    end ;`

Example:

`[[3,1,4],[1,5,9]] as \$m1 | [[2,7,1],[8,2,2]] as \$m2| ( (\$m1|elementwise(.[0] + .[1]; \$m2) ),    (\$m1|elementwise(.[0] + 2 * .[1]; \$m2) ),    (\$m1|elementwise(.[0] < .[1]; \$m2) ) ) `
Output:
`[[5,8,5],[9,7,11]][[7,15,6],[17,9,13]][[false,true,false],[true,false,false]] `

Part 2

In elementwise2, the operator can be any jq filter e.g. (.[0] < .[1]), where .[0] refers to an element in self and .[1] to the corresponding element in other, but if it is one of the strings "+", "-", "*", "/", "%", "//", "**", "^" or "pow", then the corresponding operator will be applied. Note that in jq, operators are in general polymorphic. For example, + is defined on strings and other types besides numbers.

`def elementwise2( operator; other ):  def pow(i): . as \$in | reduce range(0;i) as \$i (1; .*\$in);  def operation(x; op; y):    [x,y] | op as \$op    | if \$op == "+" then x+y      elif \$op == "-" then x-y      elif \$op == "*" then x*y      elif \$op == "/" then x/y      elif \$op == "%" then x%y      elif \$op == "//" then x/y|floor      elif \$op == "**" or \$op == "^" or \$op == "pow" then x|pow(y)      else \$op      end;   length as \$rows  | if \$rows == 0 then .    else . as \$self    | other as \$other    | (\$self[0]|length) as \$cols    | reduce range(0; \$rows) as \$i        ([]; reduce range(0; \$cols) as \$j          (.; .[\$i][\$j] = operation(\$self[\$i][\$j]; operator; \$other[\$i][\$j] ) ) )    end;`

Example:

`[[3,1,4],[1,5,9]] as \$m1 | [[2,7,1],[8,2,2]] as \$m2  | ( (\$m1|elementwise2("+"; \$m2) ),      (\$m1|elementwise2("//"; \$m2)),      (\$m1|elementwise2(.[0] < .[1]; \$m2) ) )`
Output:
`[[5,8,5],[9,7,11]][[1,0,4],[0,2,4]][[false,true,false],[true,false,false]] `

## Julia

In Julia operations with `.` before are for convention Element-wise:

`@show [1 2 3; 3 2 1] .+ [2 1 2; 0 2 1]@show [1 2 3; 2 1 2] .+ 1@show [1 2 3; 2 2 1] .- [1 1 1; 2 1 0]@show [1 2 1; 1 2 3] .* [3 2 1; 1 0 1]@show [1 2 3; 3 2 1] .* 2@show [9 8 6; 3 2 3] ./ [3 1 2; 2 1 2]@show [3 2 2; 1 2 3] .^ [1 2 3; 2 1 2]`
Output:
```[1 2 3; 3 2 1] .+ [2 1 2; 0 2 1] = [3 3 5; 3 4 2]
[1 2 3; 2 1 2] .+ 1 = [2 3 4; 3 2 3]
[1 2 3; 2 2 1] .- [1 1 1; 2 1 0] = [0 1 2; 0 1 1]
[1 2 1; 1 2 3] .* [3 2 1; 1 0 1] = [3 4 1; 1 0 3]
[1 2 3; 3 2 1] .* 2 = [2 4 6; 6 4 2]
[9 8 6; 3 2 3] ./ [3 1 2; 2 1 2] = [3.0 8.0 3.0; 1.5 2.0 1.5]
[3 2 2; 1 2 3] .^ [1 2 3; 2 1 2] = [3 4 8; 1 2 9]```

## K

Translation of: J
`   scalar: 10   vector: 2 3 5   matrix: 3 3 # 7 11 13  17 19 23  29 31 37    scalar * scalar100   scalar * vector20 30 50   scalar * matrix(70 110 130 170 190 230 290 310 370)    vector * vector4 9 25   vector * matrix(14 22 26 51 57 69 145 155 185)    matrix * matrix(49 121 169 289 361 529 841 961 1369) `
And similarly for +, -, % (division), and ^ .

## Kotlin

`// version 1.1.51 typealias Matrix = Array<DoubleArray>typealias Op = Double.(Double) -> Double fun Double.dPow(exp: Double) = Math.pow(this, exp) fun Matrix.elementwiseOp(other: Matrix, op: Op): Matrix {    require(this.size == other.size && this[0].size == other[0].size)    val result = Array(this.size) { DoubleArray(this[0].size) }    for (i in 0 until this.size) {        for (j in 0 until this[0].size) result[i][j] = this[i][j].op(other[i][j])    }    return result  } fun Matrix.elementwiseOp(d: Double, op: Op): Matrix {    val result = Array(this.size) { DoubleArray(this[0].size) }    for (i in 0 until this.size) {        for (j in 0 until this[0].size) result[i][j] = this[i][j].op(d)    }    return result  } fun Matrix.print(name: Char?, scalar: Boolean? = false) {    println(when (scalar) {        true  -> "m \$name s"         false -> "m \$name m"        else  -> "m"    } + ":")    for (i in 0 until this.size) println(this[i].asList())    println()} fun main(args: Array<String>) {    val ops = listOf(Double::plus, Double::minus, Double::times, Double::div, Double::dPow)    val names = "+-*/^"    val m = arrayOf(        doubleArrayOf(3.0, 5.0, 7.0),         doubleArrayOf(1.0, 2.0, 3.0),        doubleArrayOf(2.0, 4.0, 6.0)    )    m.print(null, null)     for ((i, op) in ops.withIndex()) m.elementwiseOp(m, op).print(names[i])    val s = 2.0    println("s = \$s:\n")    for ((i, op) in ops.withIndex()) m.elementwiseOp(s, op).print(names[i], true)   }`
Output:
```m:
[3.0, 5.0, 7.0]
[1.0, 2.0, 3.0]
[2.0, 4.0, 6.0]

m + m:
[6.0, 10.0, 14.0]
[2.0, 4.0, 6.0]
[4.0, 8.0, 12.0]

m - m:
[0.0, 0.0, 0.0]
[0.0, 0.0, 0.0]
[0.0, 0.0, 0.0]

m * m:
[9.0, 25.0, 49.0]
[1.0, 4.0, 9.0]
[4.0, 16.0, 36.0]

m / m:
[1.0, 1.0, 1.0]
[1.0, 1.0, 1.0]
[1.0, 1.0, 1.0]

m ^ m:
[27.0, 3125.0, 823543.0]
[1.0, 4.0, 27.0]
[4.0, 256.0, 46656.0]

s = 2.0:

m + s:
[5.0, 7.0, 9.0]
[3.0, 4.0, 5.0]
[4.0, 6.0, 8.0]

m - s:
[1.0, 3.0, 5.0]
[-1.0, 0.0, 1.0]
[0.0, 2.0, 4.0]

m * s:
[6.0, 10.0, 14.0]
[2.0, 4.0, 6.0]
[4.0, 8.0, 12.0]

m / s:
[1.5, 2.5, 3.5]
[0.5, 1.0, 1.5]
[1.0, 2.0, 3.0]

m ^ s:
[9.0, 25.0, 49.0]
[1.0, 4.0, 9.0]
[4.0, 16.0, 36.0]
```

## Maple

`# Built-in element-wise operator ~ #addition<1,2,3;4,5,6> +~ 2; #subtraction<2,3,1,4;0,-2,-2,1> -~ 4; #multiplication<2,3,1,4;0,-2,-2,1> *~ 4; #division<2,3,7,9;6,8,4,5;7,0,10,11> /~ 2; #exponentiation<1,2,0; 7,2,7; 6,11,3>^~5;`
Output:
```Matrix(2, 3, [[3, 4, 5], [6, 7, 8]])
Matrix(2, 4, [[-2, -1, -3, 0], [-4, -6, -6, -3]])
Matrix(2, 4, [[8, 12, 4, 16], [0, -8, -8, 4]])
Matrix(3, 4, [[1, 3/2, 7/2, 9/2], [3, 4, 2, 5/2], [7/2, 0, 5, 11/2]])
Matrix(3, 3, [[1, 32, 0], [16807, 32, 16807], [7776, 161051, 243]])
```

## Mathematica / Wolfram Language

`S = 10 ; M = {{7, 11, 13}, {17 , 19, 23} , {29, 31, 37}};M + SM - SM * SM / SM ^ S M + MM - MM * MM / MM ^ M Gives: ->{{17, 21, 23}, {27, 29, 33}, {39, 41, 47}}->{{-3, 1, 3}, {7, 9, 13}, {19, 21, 27}}->{{70, 110, 130}, {170, 190, 230}, {290, 310, 370}}->{{7/10, 11/10, 13/10}, {17/10, 19/10, 23/10}, {29/10, 31/10, 37/10}}->{{282475249, 25937424601, 137858491849}, {2015993900449,   6131066257801, 41426511213649}, {420707233300201, 819628286980801,   4808584372417849}} ->{{14, 22, 26}, {34, 38, 46}, {58, 62, 74}}->{{0, 0, 0}, {0, 0, 0}, {0, 0, 0}}->{{49, 121, 169}, {289, 361, 529}, {841, 961, 1369}}->{{1, 1, 1}, {1, 1, 1}, {1, 1, 1}}->{{823543, 285311670611, 302875106592253}, {827240261886336764177,   1978419655660313589123979,   20880467999847912034355032910567}, {2567686153161211134561828214731016126483469,   17069174130723235958610643029059314756044734431,   10555134955777783414078330085995832946127396083370199442517}}`

## MATLAB

`a = rand;b = rand(10,10);scalar_matrix = a * b;component_wise = b .* b;`

## Maxima

`a: matrix([1, 2], [3, 4]);b: matrix([2, 4], [3, 1]); a * b;a / b;a + b;a - b;a^3;a^b;  /* won't work */fullmapl("^", a, b);sin(a);`

## PARI/GP

GP already implements element-wise matrix-matrix addition and subtraction and element-wise scalar-matrix multiplication and division. Other element-wise matrix-matrix functions:

`multMM(A,B)=matrix(#A[,1],#A,i,j,A[i,j]*B[i,j]);divMM(A,B)=matrix(#A[,1],#A,i,j,A[i,j]/B[i,j]);powMM(A,B)=matrix(#A[,1],#A,i,j,A[i,j]^B[i,j]);`

Other element-wise scalar-matrix functions:

`addMs(A,s)=A+matrix(#A[,1],#A,i,j,s);subMs(A,s)=A-matrix(#A[,1],#A,i,j,s);powMs(A,s)=matrix(#A[,1],#A,i,j,A[i,j]^s);`

PARI implements convenience functions `vecmul` (element-wise matrix-matrix multiplication), `vecdiv` (element-wise matrix-matrix division), and `vecpow` (element-wise matrix-scalar exponentiation), as well as `vecmodii` and `vecinv`. These operate on vectors, but a `t_MAT` is simply an array of vectors in PARI so it applies fairly easily.

## Perl

There's no need to use real multi-dimentional arrays to represent matrix. Since matrices have fixed row length, they can be represented by flat array.

This example demonstrates Perl's operator overload ability and bulk list operations using map.

File Elementwise.pm:

`package Elementwise; use Exporter 'import'; use overload'='  => sub { \$_[0]->clone() },'+'  => sub { \$_[0]->add(\$_[1]) },'-'  => sub { \$_[0]->sub(\$_[1]) },'*'  => sub { \$_[0]->mul(\$_[1]) },'/'  => sub { \$_[0]->div(\$_[1]) },'**'  => sub { \$_[0]->exp(\$_[1]) },; sub new{	my (\$class, \$v) = @_;	return bless \$v, \$class;} sub clone{	my @ret = @{\$_[0]};	return bless \@ret, ref(\$_[0]);} sub add { new Elementwise ref(\$_[1]) ? [map { \$_[0][\$_]  + \$_[1][\$_] } 0 .. \$#{\$_[0]} ] : [map { \$_[0][\$_]  + \$_[1] } 0 .. \$#{\$_[0]} ] }sub sub { new Elementwise ref(\$_[1]) ? [map { \$_[0][\$_]  - \$_[1][\$_] } 0 .. \$#{\$_[0]} ] : [map { \$_[0][\$_]  - \$_[1] } 0 .. \$#{\$_[0]} ] }sub mul { new Elementwise ref(\$_[1]) ? [map { \$_[0][\$_]  * \$_[1][\$_] } 0 .. \$#{\$_[0]} ] : [map { \$_[0][\$_]  * \$_[1] } 0 .. \$#{\$_[0]} ] }sub div { new Elementwise ref(\$_[1]) ? [map { \$_[0][\$_]  / \$_[1][\$_] } 0 .. \$#{\$_[0]} ] : [map { \$_[0][\$_]  / \$_[1] } 0 .. \$#{\$_[0]} ] }sub exp { new Elementwise ref(\$_[1]) ? [map { \$_[0][\$_] ** \$_[1][\$_] } 0 .. \$#{\$_[0]} ] : [map { \$_[0][\$_] ** \$_[1] } 0 .. \$#{\$_[0]} ] } 1;`

File test.pl:

`use Elementwise; \$a = new Elementwise [	1,2,3,	4,5,6,	7,8,9]; print << "_E";a  @\$aa OP a+  @{\$a+\$a}-  @{\$a-\$a}*  @{\$a*\$a}/  @{\$a/\$a}^  @{\$a**\$a}a OP 5+  @{\$a+5}-  @{\$a-5}*  @{\$a*5}/  @{\$a/5}^  @{\$a**5}_E`
Output:
```a  1 2 3 4 5 6 7 8 9
a OP a
+  2 4 6 8 10 12 14 16 18
-  0 0 0 0 0 0 0 0 0
*  1 4 9 16 25 36 49 64 81
/  1 1 1 1 1 1 1 1 1
^  1 4 27 256 3125 46656 823543 16777216 387420489
a OP 5
+  6 7 8 9 10 11 12 13 14
-  -4 -3 -2 -1 0 1 2 3 4
*  5 10 15 20 25 30 35 40 45
/  0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
^  1 32 243 1024 3125 7776 16807 32768 59049```

## Perl 6

Works with: rakudo version 2016.05
Perl 6 already implements this and other metaoperators as higher-order functions (cross, zip, reduce, triangle, etc.) that are usually accessed through a meta-operator syntactic sugar that is productive over all appropriate operators, including user-defined ones. In this case, a dwimmy element-wise operator (generically known as a "hyper") is indicated by surrounding the operator with double angle quotes. Hypers dwim on the pointy end with cyclic APL semantics as necessary. You can turn the quote the other way to suppress dwimmery on that end. In this case we could have used »op» instead of «op» since the short side is always on the right.
`my @a =    [1,2,3],    [4,5,6],    [7,8,9]; sub msay(@x) {    for @x -> @row {        print ' ', \$_%1 ?? \$_.nude.join('/') !! \$_ for @row;        say '';    }    say '';} msay @a «+» @a;msay @a «-» @a;msay @a «*» @a;msay @a «/» @a;msay @a «+» [1,2,3];msay @a «-» [1,2,3];msay @a «*» [1,2,3];msay @a «/» [1,2,3];msay @a «+» 2;msay @a «-» 2;msay @a «*» 2;msay @a «/» 2; # In addition to calling the underlying higher-order functions directly, it's possible to name a function. sub infix:<M+> (\l,\r) { l <<+>> r } msay @a M+ @a;msay @a M+ [1,2,3];msay @a M+ 2; `
Output:
``` 2 4 6
8 10 12
14 16 18

0 0 0
0 0 0
0 0 0

1 4 9
16 25 36
49 64 81

1 1 1
1 1 1
1 1 1

2 3 4
6 7 8
10 11 12

0 1 2
2 3 4
4 5 6

1 2 3
8 10 12
21 24 27

1 2 3
2 5/2 3
7/3 8/3 3

3 4 5
6 7 8
9 10 11

-1 0 1
2 3 4
5 6 7

2 4 6
8 10 12
14 16 18

1/2 1 3/2
2 5/2 3
7/2 4 9/2

2 4 6
8 10 12
14 16 18

2 3 4
6 7 8
10 11 12

3 4 5
6 7 8
9 10 11```

## Phix

Phix has builtin sequence ops, which work fine with a multi-dimensional array / matrix:

`constant m = {{7, 8, 7},{4, 0, 9}},         m2 = {{4, 5, 1},{6, 2, 1}}?{m,"+",m2,"=",sq_add(m,m2)}?{m,"-",m2,"=",sq_sub(m,m2)}?{m,"*",m2,"=",sq_mul(m,m2)}?{m,"/",m2,"=",sq_div(m,m2)}?{m,"^",m2,"=",sq_power(m,m2)}?{m,"+ 3 =",sq_add(m,3)}?{m,"- 3 =",sq_sub(m,3)}?{m,"* 3 =",sq_mul(m,3)}?{m,"/ 3 =",sq_div(m,3)}?{m,"^ 3 =",sq_power(m,3)}`
Output:
```{{{7,8,7},{4,0,9}},"+",{{4,5,1},{6,2,1}},"=",{{11,13,8},{10,2,10}}}
{{{7,8,7},{4,0,9}},"-",{{4,5,1},{6,2,1}},"=",{{3,3,6},{-2,-2,8}}}
{{{7,8,7},{4,0,9}},"*",{{4,5,1},{6,2,1}},"=",{{28,40,7},{24,0,9}}}
{{{7,8,7},{4,0,9}},"/",{{4,5,1},{6,2,1}},"=",{{1.75,1.6,7},{0.6666666667,0,9}}}
{{{7,8,7},{4,0,9}},"^",{{4,5,1},{6,2,1}},"=",{{2401,32768,7},{4096,0,9}}}
{{{7,8,7},{4,0,9}},"+ 3 =",{{10,11,10},{7,3,12}}}
{{{7,8,7},{4,0,9}},"- 3 =",{{4,5,4},{1,-3,6}}}
{{{7,8,7},{4,0,9}},"* 3 =",{{21,24,21},{12,0,27}}}
{{{7,8,7},{4,0,9}},"/ 3 =",{{2.333333333,2.666666667,2.333333333},{1.333333333,0,3}}}
{{{7,8,7},{4,0,9}},"^ 3 =",{{343,512,343},{64,0,729}}}
```

## PicoLisp

`(de elementWiseMatrix (Fun Mat1 Mat2)   (mapcar '((L1 L2) (mapcar Fun L1 L2)) Mat1 Mat2) ) (de elementWiseScalar (Fun Mat Scalar)   (elementWiseMatrix Fun Mat (circ (circ Scalar))) )`

Test:

```(let (S 10  M '((7 11 13) (17 19 23) (29 31 37)))
(println (elementWiseScalar + M S))
(println (elementWiseScalar - M S))
(println (elementWiseScalar * M S))
(println (elementWiseScalar / M S))
(println (elementWiseScalar ** M S))
(prinl)
(println (elementWiseMatrix + M M))
(println (elementWiseMatrix - M M))
(println (elementWiseMatrix * M M))
(println (elementWiseMatrix / M M))
(println (elementWiseMatrix ** M M)) )```
Output:
```((17 21 23) (27 29 33) (39 41 47))
((-3 1 3) (7 9 13) (19 21 27))
((70 110 130) (170 190 230) (290 310 370))
((0 1 1) (1 1 2) (2 3 3))
((282475249 25937424601 137858491849) (2015993900449 6131066257801 ...

((14 22 26) (34 38 46) (58 62 74))
((0 0 0) (0 0 0) (0 0 0))
((49 121 169) (289 361 529) (841 961 1369))
((1 1 1) (1 1 1) (1 1 1))
((823543 285311670611 302875106592253) (827240261886336764177 ...```

## PL/I

Any arithmetic operation can be applied to elements of arrays. These examples illustrate element-by-element multiplication, but addition, subtraction, division, and exponentiation can also be written.

`declare (matrix(3,3), vector(3), scalar) fixed;declare (m(3,3), v(3) fixed; m = scalar * matrix;m = vector * matrix;m = matrix * matrix; v = scalar * vector;v = vector * vector;`

## Python

`>>> import random>>> from operator import add, sub, mul, floordiv>>> from pprint import pprint as pp>>> >>> def ewise(matrix1, matrix2, op):	return [[op(e1,e2) for e1,e2 in zip(row1, row2)] for row1,row2 in zip(matrix1, matrix2)] >>> m,n = 3,4 	# array dimensions>>> a0 = [[random.randint(1,9) for y in range(n)] for x in range(m)]>>> a1 = [[random.randint(1,9) for y in range(n)] for x in range(m)]>>> pp(a0); pp(a1)[[7, 8, 7, 4], [4, 9, 4, 1], [2, 3, 6, 4]][[4, 5, 1, 6], [6, 8, 3, 4], [2, 2, 6, 3]]>>> pp(ewise(a0, a1, add))[[11, 13, 8, 10], [10, 17, 7, 5], [4, 5, 12, 7]]>>> pp(ewise(a0, a1, sub))[[3, 3, 6, -2], [-2, 1, 1, -3], [0, 1, 0, 1]]>>> pp(ewise(a0, a1, mul))[[28, 40, 7, 24], [24, 72, 12, 4], [4, 6, 36, 12]]>>> pp(ewise(a0, a1, floordiv))[[1, 1, 7, 0], [0, 1, 1, 0], [1, 1, 1, 1]]>>> pp(ewise(a0, a1, pow))[[2401, 32768, 7, 4096], [4096, 43046721, 64, 1], [4, 9, 46656, 64]]>>> pp(ewise(a0, a1, lambda x, y:2*x - y))[[10, 11, 13, 2], [2, 10, 5, -2], [2, 4, 6, 5]]>>> >>> def s_ewise(scalar1, matrix1, op):	return [[op(scalar1, e1) for e1 in row1] for row1 in matrix1] >>> scalar = 10>>> a0[[7, 8, 7, 4], [4, 9, 4, 1], [2, 3, 6, 4]]>>> for op in ( add, sub, mul, floordiv, pow, lambda x, y:2*x - y ):	print("%10s :" % op.__name__, s_ewise(scalar, a0, op))         add : [[17, 18, 17, 14], [14, 19, 14, 11], [12, 13, 16, 14]]       sub : [[3, 2, 3, 6], [6, 1, 6, 9], [8, 7, 4, 6]]       mul : [[70, 80, 70, 40], [40, 90, 40, 10], [20, 30, 60, 40]]  floordiv : [[1, 1, 1, 2], [2, 1, 2, 10], [5, 3, 1, 2]]       pow : [[10000000, 100000000, 10000000, 10000], [10000, 1000000000, 10000, 10], [100, 1000, 1000000, 10000]]  <lambda> : [[13, 12, 13, 16], [16, 11, 16, 19], [18, 17, 14, 16]]>>> `

## R

In R most operations work on vectors and matrices:

`# create a 2-times-2 matrixmat <- matrix(1:4, 2, 2) # matrix with scalarmat + 2mat * 2mat ^ 2 # matrix with matrixmat + matmat * matmat ^ mat`
Output:
```> mat <- matrix(1:4, 2, 2)
[,1] [,2]
[1,]    1    3
[2,]    2    4

> mat + 2
[,1] [,2]
[1,]    3    5
[2,]    4    6

> mat * 2
[,1] [,2]
[1,]    2    6
[2,]    4    8

> mat ^ 2
[,1] [,2]
[1,]    1    9
[2,]    4   16

> mat + mat
[,1] [,2]
[1,]    2    6
[2,]    4    8

> mat * mat
[,1] [,2]
[1,]    1    9
[2,]    4   16

> mat ^ mat
[,1] [,2]
[1,]    1   27
[2,]    4  256```

## Racket

Translation of: R
`#lang racket(require math/array) (define mat (list->array #(2 2) '(1 3 2 4))) mat(array+ mat (array 2))(array* mat (array 2))(array-map expt mat (array 2)) (array+ mat mat)(array* mat mat)(array-map expt mat mat) `
Output:
```(array #[#[1 3] #[2 4]])
(array #[#[3 5] #[4 6]])
(array #[#[2 6] #[4 8]])
(array #[#[1 9] #[4 16]])
(array #[#[2 6] #[4 8]])
(array #[#[1 9] #[4 16]])
(array #[#[1 27] #[4 256]])
```

## REXX

### discrete

`/*REXX program  multiplies two matrixes together, displays the  matrixes and the result.*/m=(1 2 3)  (4 5 6)  (7 8 9)w=words(m);               do k=1;  if k*k>=w  then leave;  end  /*k*/;     rows=k;  cols=kcall showMat  M, 'M matrix'answer=matAdd(m, 2  );    call showMat  answer, 'M matrix, added 2'answer=matSub(m, 7  );    call showMat  answer, 'M matrix, subtracted 7'answer=matMul(m, 2.5);    call showMat  answer, 'M matrix, multiplied by 2½'answer=matPow(m, 3  );    call showMat  answer, 'M matrix, cubed'answer=matDiv(m, 4  );    call showMat  answer, 'M matrix, divided by 4'answer=matIdv(m, 2  );    call showMat  answer, 'M matrix, integer halved'answer=matMod(m, 3  );    call showMat  answer, 'M matrix, modulus 3'exit                                             /*stick a fork in it,  we're all done. *//*──────────────────────────────────────────────────────────────────────────────────────*/matAdd:   parse arg @,#;  call mat#;    do j=1  for w; !.j=!.j+#;     end;   return [email protected]()matSub:   parse arg @,#;  call mat#;    do j=1  for w; !.j=!.j-#;     end;   return [email protected]()matMul:   parse arg @,#;  call mat#;    do j=1  for w; !.j=!.j*#;     end;   return [email protected]()matDiv:   parse arg @,#;  call mat#;    do j=1  for w; !.j=!.j/#;     end;   return [email protected]()matIdv:   parse arg @,#;  call mat#;    do j=1  for w; !.j=!.j%#;     end;   return [email protected]()matPow:   parse arg @,#;  call mat#;    do j=1  for w; !.j=!.j**#;    end;   return [email protected]()matMod:   parse arg @,#;  call mat#;    do j=1  for w; !.j=!.j//#;    end;   return [email protected]()mat#:     w=words(@);                   do j=1  for w; !.j=word(@,j); end;   return[email protected]:     @=!.1;                        do j=2   to w; @[email protected] !.j;       end;   return @/*──────────────────────────────────────────────────────────────────────────────────────*/showMat:  parse arg @, hdr; L=0;  say                                        do j=1  for w;  L=max(L,length(word(@,j)));  end          say  center(hdr, max(length(hdr)+4, cols*(L+1)+4), "─")          n=0                    do r    =1 for rows;         _=                        do c=1 for cols; n=n+1;  _=_ right(word(@,n),L); end;    say _                    end          return`

output

```──M matrix──
1 2 3
4 5 6
7 8 9

3  4  5
6  7  8
9 10 11

──M matrix, subtracted 7──
-6 -5 -4
-3 -2 -1
0  1  2

──M matrix, multiplied by 2½──
2.5  5.0  7.5
10.0 12.5 15.0
17.5 20.0 22.5

──M matrix, cubed──
1   8  27
64 125 216
343 512 729

──M matrix, divided by 4──
0.25  0.5 0.75
1 1.25  1.5
1.75    2 2.25

──M matrix, integer halved──
0 1 1
2 2 3
3 4 4

──M matrix, modulus 3──
1 2 0
1 2 0
1 2 0
```

### generalized

`/*REXX program  multiplies two matrixes together, displays the matrixes and the result. */m=(1 2 3)  (4 5 6)  (7 8 9)w=words(m);                  do k=1; if k*k>=w then leave; end  /*k*/;     rows=k;  cols=kcall showMat  M, 'M matrix'ans=matOp(m, '+2'   );   call showMat  ans,  "M matrix, added 2"ans=matOp(m, '-7'   );   call showMat  ans,  "M matrix, subtracted 7"ans=matOp(m, '*2.5' );   call showMat  ans,  "M matrix, multiplied by 2½"ans=matOp(m, '**3'  );   call showMat  ans,  "M matrix, cubed"ans=matOp(m, '/4'   );   call showMat  ans,  "M matrix, divided by 4"ans=matOp(m, '%2'   );   call showMat  ans,  "M matrix, integer halved"ans=matOp(m, '//3'  );   call showMat  ans,  "M matrix, modulus 3"ans=matOp(m, '*3-1' );   call showMat  ans,  "M matrix, tripled, less one"exit                                             /*stick a fork in it,  we"re all done. *//*──────────────────────────────────────────────────────────────────────────────────────*/matOp:  parse arg @,#; call mat#; do j=1 for w; interpret '!.'j"=!."j #;end; return [email protected]()mat#:   w=words(@);               do j=1 for w; !.j=word(@,j);          end; return[email protected]:   @=!.1;                    do j=2  to w; @[email protected] !.j;                end; return @/*──────────────────────────────────────────────────────────────────────────────────────*/showMat:  parse arg @, hdr;  say          L=0;                    do j=1  for w;     L=max(L,length(word(@,j)));   end          say;   say center(hdr,max(length(hdr)+4,cols*(L+1)+4),"─")          n=0                    do r    =1 for rows;         _=                        do c=1 for cols; n=n+1;  _=_ right(word(@,n),L); end;    say _                    end          return`

output

```──M matrix──
1 2 3
4 5 6
7 8 9

3  4  5
6  7  8
9 10 11

──M matrix, subtracted 7──
-6 -5 -4
-3 -2 -1
0  1  2

──M matrix, multiplied by 2½──
2.5  5.0  7.5
10.0 12.5 15.0
17.5 20.0 22.5

──M matrix, cubed──
1   8  27
64 125 216
343 512 729

──M matrix, divided by 4──
0.25  0.5 0.75
1 1.25  1.5
1.75    2 2.25

──M matrix, integer halved──
0 1 1
2 2 3
3 4 4

──M matrix, modulus 3──
1 2 0
1 2 0
1 2 0

──M matrix, tripled, less one──
2  5  8
11 14 17
20 23 26
```

## Ruby

`require 'matrix' class Matrix  def element_wise( operator, other )    Matrix.build(row_size, column_size) do |row, col|      self[row, col].send(operator, other[row, col])    end  endend m1, m2 = Matrix[[3,1,4],[1,5,9]], Matrix[[2,7,1],[8,2,2]]puts "m1: #{m1}\nm2: #{m2}\n\n" [:+, :-, :*, :/, :fdiv, :**, :%].each do |op|  puts "m1 %-4s m2  =  %s" % [op, m1.element_wise(op, m2)]end`
Output:
```m1: Matrix[[3, 1, 4], [1, 5, 9]]
m2: Matrix[[2, 7, 1], [8, 2, 2]]

m1 +    m2  =  Matrix[[5, 8, 5], [9, 7, 11]]
m1 -    m2  =  Matrix[[1, -6, 3], [-7, 3, 7]]
m1 *    m2  =  Matrix[[6, 7, 4], [8, 10, 18]]
m1 /    m2  =  Matrix[[1, 0, 4], [0, 2, 4]]
m1 fdiv m2  =  Matrix[[1.5, 0.14285714285714285, 4.0], [0.125, 2.5, 4.5]]
m1 **   m2  =  Matrix[[9, 1, 4], [1, 25, 81]]
m1 %    m2  =  Matrix[[1, 1, 0], [1, 1, 1]]
```

## Rust

`struct Matrix {    elements: Vec<f32>,    pub height: u32,    pub width: u32,} impl Matrix {    fn new(elements: Vec<f32>, height: u32, width: u32) -> Matrix {        // Should check for dimensions but omitting to be succient        Matrix {            elements: elements,            height: height,            width: width,        }    }     fn get(&self, row: u32, col: u32) -> f32 {        let row = row as usize;        let col = col as usize;        self.elements[col + row * (self.width as usize)]    }     fn set(&mut self, row: u32, col: u32, value: f32) {        let row = row as usize;        let col = col as usize;        self.elements[col + row * (self.width as usize)] = value;    }     fn print(&self) {        for row in 0..self.height {            for col in 0..self.width {                print!("{:3.0}", self.get(row, col));            }            println!("");        }        println!("");    }} // Matrix addition will perform element-wise additionfn matrix_addition(first: &Matrix, second: &Matrix) -> Result<Matrix, String> {    if first.width == second.width && first.height == second.height {        let mut result = Matrix::new(vec![0.0f32; (first.height * first.width) as usize],                                     first.height,                                     first.width);        for row in 0..first.height {            for col in 0..first.width {                let first_value = first.get(row, col);                let second_value = second.get(row, col);                result.set(row, col, first_value + second_value);            }        }        Ok(result)    } else {        Err("Dimensions don't match".to_owned())    }} fn scalar_multiplication(scalar: f32, matrix: &Matrix) -> Matrix {    let mut result = Matrix::new(vec![0.0f32; (matrix.height * matrix.width) as usize],                                 matrix.height,                                 matrix.width);    for row in 0..matrix.height {        for col in 0..matrix.width {            let value = matrix.get(row, col);            result.set(row, col, scalar * value);        }    }    result} // Subtract second from firstfn matrix_subtraction(first: &Matrix, second: &Matrix) -> Result<Matrix, String> {    if first.width == second.width && first.height == second.height {        let negative_matrix = scalar_multiplication(-1.0, second);        let result = matrix_addition(first, &negative_matrix).unwrap();        Ok(result)    } else {        Err("Dimensions don't match".to_owned())    }} // First must be a l x m matrix and second a m x n matrix for this to work.fn matrix_multiplication(first: &Matrix, second: &Matrix) -> Result<Matrix, String> {    if first.width == second.height {        let mut result = Matrix::new(vec![0.0f32; (first.height * second.width) as usize],                                     first.height,                                     second.width);        for row in 0..result.height {            for col in 0..result.width {                let mut value = 0.0;                for it in 0..first.width {                    value += first.get(row, it) * second.get(it, col);                }                result.set(row, col, value);            }        }        Ok(result)    } else {        Err("Dimensions don't match. Width of first must equal height of second".to_owned())    }}  fn main() {    let height = 2;    let width = 3;    // Matrix will look like:    // | 1.0  2.0  3.0  |    // | 4.0  5.0  6.0 |    let matrix1 = Matrix::new(vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0], height, width);     // Matrix will look like:    // | 6.0  5.0  4.0  |    // | 3.0  2.0  1.0 |    let matrix2 = Matrix::new(vec![6.0, 5.0, 4.0, 3.0, 2.0, 1.0], height, width);     // | 7.0  7.0  7.0  |    // | 7.0  7.0  7.0 |    matrix_addition(&matrix1, &matrix2).unwrap().print();    // | 2.0   4.0   6.0  |    // | 8.0  10.0  12.0 |    scalar_multiplication(2.0, &matrix1).print();    // | -5.0  -3.0  -1.0  |    // |  1.0   3.0   5.0 |    matrix_subtraction(&matrix1, &matrix2).unwrap().print();     // | 1.0 |    // | 1.0 |    // | 1.0 |    let matrix3 = Matrix::new(vec![1.0, 1.0, 1.0], width, 1);    // |  6 |    // | 15 |    matrix_multiplication(&matrix1, &matrix3).unwrap().print();}`

## Sidef

The built-in metaoperators `~W<op>`, `~S<op>` and `~RS<op>` are defined for arbitrary nested arrays.

`var m1 = [[3,1,4],[1,5,9]]var m2 = [[2,7,1],[8,2,2]] say ":: Matrix-matrix operations"say (m1 ~W+  m2)say (m1 ~W-  m2)say (m1 ~W*  m2)say (m1 ~W/  m2)say (m1 ~W// m2)say (m1 ~W** m2)say (m1 ~W%  m2) say "\n:: Matrix-scalar operations"say (m1 ~S+  42)say (m1 ~S-  42)say (m1 ~S/  42)say (m1 ~S** 10)# ... say "\n:: Scalar-matrix operations"say (m1 ~RS+  42)say (m1 ~RS-  42)say (m1 ~RS/  42)say (m1 ~RS** 10)# ...`
Output:
```:: Matrix-matrix operations
[[5, 8, 5], [9, 7, 11]]
[[1, -6, 3], [-7, 3, 7]]
[[6, 7, 4], [8, 10, 18]]
[[3/2, 1/7, 4], [1/8, 5/2, 9/2]]
[[1, 0, 4], [0, 2, 4]]
[[9, 1, 4], [1, 25, 81]]
[[1, 1, 0], [1, 1, 1]]

:: Matrix-scalar operations
[[45, 43, 46], [43, 47, 51]]
[[-39, -41, -38], [-41, -37, -33]]
[[1/14, 1/42, 2/21], [1/42, 5/42, 3/14]]
[[59049, 1, 1048576], [1, 9765625, 3486784401]]

:: Scalar-matrix operations
[[45, 43, 46], [43, 47, 51]]
[[39, 41, 38], [41, 37, 33]]
[[14, 42, 21/2], [42, 42/5, 14/3]]
[[1000, 10, 10000], [10, 100000, 1000000000]]
```

## Standard ML

`structure Matrix = structlocal    open Array2    fun mapscalar f (x, scalar) =	tabulate RowMajor (nRows x, nCols x, fn (i,j) => f(sub(x,i,j),scalar))    fun map2 f (x, y) =	tabulate RowMajor (nRows x, nCols x, fn (i,j) => f(sub(x,i,j),sub(y,i,j)))ininfix splus sminus stimesval op splus = mapscalar Int.+ val op sminus = mapscalar Int.- val op stimes = mapscalar Int.* val op + = map2 Int.+val op - = map2 Int.-val op * = map2 Int.*val fromList = fromListfun toList a =    List.tabulate(nRows a, fn i => List.tabulate(nCols a, fn j => sub(a,i,j)))endend; (* example *)let open Matrix    infix splus sminus stimes    val m1 = fromList [[1,2],[3,4]]    val m2 = fromList [[4,3],[2,1]]    val s = 2			     in    List.map toList [m1+m2, m1-m2, m1*m2,		     m1 splus s, m1 sminus s, m1 stimes s]end;`

Output:

`val it =  [[[5,5],[5,5]],[[~3,~1],[1,3]],[[4,6],[6,4]],[[3,4],[5,6]],[[~1,0],[1,2]],   [[2,4],[6,8]]] : int list list list`

## Stata

`mataa = rnormal(5,5,0,1)b = 2a:+ba:-ba:*ba:/ba:^b a = rnormal(5,5,0,1)b = rnormal(5,1,0,1)a:+ba:-ba:*ba:/ba:^bend`

## Tcl

`package require Tcl 8.5proc alias {name args} {uplevel 1 [list interp alias {} \$name {} {*}\$args]} # Engine for elementwise operations between matricesproc elementwiseMatMat {lambda A B} {    set C {}    foreach rA \$A rB \$B {	set rC {}	foreach vA \$rA vB \$rB {	    lappend rC [apply \$lambda \$vA \$vB]	}	lappend C \$rC    }    return \$C}# Lift some basic math opsalias m+  elementwiseMatMat {{a b} {expr {\$a+\$b}}}alias m-  elementwiseMatMat {{a b} {expr {\$a-\$b}}}alias m*  elementwiseMatMat {{a b} {expr {\$a*\$b}}}alias m/  elementwiseMatMat {{a b} {expr {\$a/\$b}}}alias m** elementwiseMatMat {{a b} {expr {\$a**\$b}}} # Engine for elementwise operations between a matrix and a scalarproc elementwiseMatSca {lambda A b} {    set C {}    foreach rA \$A {	set rC {}	foreach vA \$rA {	    lappend rC [apply \$lambda \$vA \$b]	}	lappend C \$rC    }    return \$C}# Lift some basic math opsalias .+  elementwiseMatSca {{a b} {expr {\$a+\$b}}}alias .-  elementwiseMatSca {{a b} {expr {\$a-\$b}}}alias .*  elementwiseMatSca {{a b} {expr {\$a*\$b}}}alias ./  elementwiseMatSca {{a b} {expr {\$a/\$b}}}alias .** elementwiseMatSca {{a b} {expr {\$a**\$b}}}`

## zkl

`var [const] GSL=Import("zklGSL");	// libGSL (GNU Scientific Library)M:=GSL.Matrix(3,3).set(3,5,7, 1,2,3, 2,4,6);x:=2;println("M = \n%s\nx = %s".fmt(M.format(),x));foreach op in (T('+,'-,'*,'/)){   println("M %s x:\n%s\n".fmt(op.toString()[3,1],op(M.copy(),x).format()));}foreach op in (T("addElements","subElements","mulElements","divElements")){   println("M %s M:\n%s\n".fmt(op, M.copy().resolve(op)(M).format()));}mSqrd:=M.pump(0,M.copy(),fcn(x){ x*x });  // M element by elementprintln("M square elements:\n%s\n".fmt(mSqrd.format()));`
Output:
```M =
3.00,      5.00,      7.00
1.00,      2.00,      3.00
2.00,      4.00,      6.00
x = 2
M + x:
5.00,      7.00,      9.00
3.00,      4.00,      5.00
4.00,      6.00,      8.00

M - x:
1.00,      3.00,      5.00
-1.00,      0.00,      1.00
0.00,      2.00,      4.00

M * x:
6.00,     10.00,     14.00
2.00,      4.00,      6.00
4.00,      8.00,     12.00

M / x:
1.50,      2.50,      3.50
0.50,      1.00,      1.50
1.00,      2.00,      3.00

6.00,     10.00,     14.00
2.00,      4.00,      6.00
4.00,      8.00,     12.00

M subElements M:
0.00,      0.00,      0.00
0.00,      0.00,      0.00
0.00,      0.00,      0.00

M mulElements M:
9.00,     25.00,     49.00
1.00,      4.00,      9.00
4.00,     16.00,     36.00

M divElements M:
1.00,      1.00,      1.00
1.00,      1.00,      1.00
1.00,      1.00,      1.00

M square elements:
9.00,     25.00,     49.00
1.00,      4.00,      9.00
4.00,     16.00,     36.00
```