45

What's the easiest way to compute a 3x3 matrix inverse?

I'm just looking for a short code snippet that'll do the trick for non-singular matrices, possibly using Cramer's rule. It doesn't need to be highly optimized. I'd prefer simplicity over speed. I'd rather not link in additional libraries.

6
  • You give no details and your question is very generic, so my answer is to use BLAS.
    – Not Sure
    Commented Jun 11, 2009 at 22:12
  • When it comes to matrix inversion, 3x3, and cramer's rule are pretty detailed. Commented Jun 12, 2009 at 0:00
  • 1
    In fairness, I added the additional detail after he complained. ;-)
    – batty
    Commented Jun 12, 2009 at 0:17
  • 3
    "I'd prefer simplicity over speed" The problem you will be facing is numerical errors. Are you sure you wouldn't want to include a dependable library? Commented Jun 12, 2009 at 11:25
  • dgetri will do the trick
    – nlucaroni
    Commented Jun 12, 2009 at 13:51

12 Answers 12

60

Here's a version of batty's answer, but this computes the correct inverse. batty's version computes the transpose of the inverse.

// computes the inverse of a matrix m
double det = m(0, 0) * (m(1, 1) * m(2, 2) - m(2, 1) * m(1, 2)) -
             m(0, 1) * (m(1, 0) * m(2, 2) - m(1, 2) * m(2, 0)) +
             m(0, 2) * (m(1, 0) * m(2, 1) - m(1, 1) * m(2, 0));

double invdet = 1 / det;

Matrix33d minv; // inverse of matrix m
minv(0, 0) = (m(1, 1) * m(2, 2) - m(2, 1) * m(1, 2)) * invdet;
minv(0, 1) = (m(0, 2) * m(2, 1) - m(0, 1) * m(2, 2)) * invdet;
minv(0, 2) = (m(0, 1) * m(1, 2) - m(0, 2) * m(1, 1)) * invdet;
minv(1, 0) = (m(1, 2) * m(2, 0) - m(1, 0) * m(2, 2)) * invdet;
minv(1, 1) = (m(0, 0) * m(2, 2) - m(0, 2) * m(2, 0)) * invdet;
minv(1, 2) = (m(1, 0) * m(0, 2) - m(0, 0) * m(1, 2)) * invdet;
minv(2, 0) = (m(1, 0) * m(2, 1) - m(2, 0) * m(1, 1)) * invdet;
minv(2, 1) = (m(2, 0) * m(0, 1) - m(0, 0) * m(2, 1)) * invdet;
minv(2, 2) = (m(0, 0) * m(1, 1) - m(1, 0) * m(0, 1)) * invdet;
5
  • 4
    This is the best answer imo, but it calculates some things 2 times, like m(1, 1) * m(2, 2) - m(2, 1) * m(1, 2). Please store the 2nd part of the 3 determinant calculations in 3 temporary doubles. They're going to be used in the last part of the calculation again. If you flip m(1, 0) * m(2, 2) - m(1, 2) * m(2, 0), you can even re-use it without the minus sign.
    – Luc Bloom
    Commented Feb 23, 2015 at 11:07
  • 2
    I wrote this primarily for clarity, not necessarily speed. But you're absolutely right that some operations are repeated and can be avoided by temporarily caching the result and reusing it.
    – Cornstalks
    Commented Feb 23, 2015 at 16:57
  • 6
    The compiler should be able to optimize that. No worries. Commented Aug 16, 2016 at 21:33
  • 1
    This is the simplest implementation of the algorithm. However, beware that the determinants of the minors might be affected by round-off errors. This site provides a good explanation of the problem, and a nice solution: pharr.org/matt/blog/2019/11/03/difference-of-floats.html Commented Oct 21, 2020 at 7:41
  • The computations are correct, but result become unstable when determinant is close to 0. I found this by comparing it with with the numpy.linalg.inv method which provides much better result. Just multiply the inverted and the original matrices and compare it with the identity matrix. Commented Jan 26, 2022 at 3:55
33

This piece of code computes the transposed inverse of the matrix A:

double determinant =    +A(0,0)*(A(1,1)*A(2,2)-A(2,1)*A(1,2))
                        -A(0,1)*(A(1,0)*A(2,2)-A(1,2)*A(2,0))
                        +A(0,2)*(A(1,0)*A(2,1)-A(1,1)*A(2,0));
double invdet = 1/determinant;
result(0,0) =  (A(1,1)*A(2,2)-A(2,1)*A(1,2))*invdet;
result(1,0) = -(A(0,1)*A(2,2)-A(0,2)*A(2,1))*invdet;
result(2,0) =  (A(0,1)*A(1,2)-A(0,2)*A(1,1))*invdet;
result(0,1) = -(A(1,0)*A(2,2)-A(1,2)*A(2,0))*invdet;
result(1,1) =  (A(0,0)*A(2,2)-A(0,2)*A(2,0))*invdet;
result(2,1) = -(A(0,0)*A(1,2)-A(1,0)*A(0,2))*invdet;
result(0,2) =  (A(1,0)*A(2,1)-A(2,0)*A(1,1))*invdet;
result(1,2) = -(A(0,0)*A(2,1)-A(2,0)*A(0,1))*invdet;
result(2,2) =  (A(0,0)*A(1,1)-A(1,0)*A(0,1))*invdet;

Though the question stipulated non-singular matrices, you might still want to check if determinant equals zero (or very near zero) and flag it in some way to be safe.

4
  • 2
    Don't forget to check if determinant is zero :) Commented Jun 11, 2009 at 23:24
  • 1
    Now try doing that without so much... mess. :) (Ideally, without using any number other than "0" and "3" in the code.) Commented Jun 11, 2009 at 23:24
  • 3
    This code actually gives you the TRANSPOSE of the inverse matrix. Check out the formula at the other answer
    – shoosh
    Commented Nov 29, 2009 at 13:10
  • 4
    Concise, elegant, and fast (even if it is the transpose) - nicely done! A more general solution that uses loops and multiple function calls is both overkill and needless overhead for such a fundamental operation.
    – AbePralle
    Commented Feb 24, 2012 at 7:32
27

Why don't you try to code it yourself? Take it as a challenge. :)

For a 3×3 matrix

alt text
(source: wolfram.com)

the matrix inverse is

alt text
(source: wolfram.com)

I'm assuming you know what the determinant of a matrix |A| is.

Images (c) Wolfram|Alpha and mathworld.wolfram (06-11-09, 22.06)

13
  • 2
    Nice typesetting as well. LaTeX?
    – duffymo
    Commented Jun 11, 2009 at 22:47
  • 7
    As big a fan as I am of LaTeX, no. They are JPEGs generated by Wolfram|Alpha. :)
    – S.P
    Commented Jun 11, 2009 at 22:58
  • 1
    Uh, is this actually right? All the off-diagonal entries look wrong to me, but maybe I'm not aware of some interesting matrix identity. E.g., shouldn't the cofactor of a12 be -|a21 a23; a31 a33|? Commented Jun 11, 2009 at 23:53
  • 5
    Nice if you have the time to take a challenge but frankly it doesn't answer the question: "I'm just looking for a short code snippet that'll do the trick for non-singular matrices"
    – user234736
    Commented May 19, 2014 at 7:42
  • 10
    I'm going to be that guy. Downvoted because this doesn't actually answer the question - the C++ code is not here.
    – Alan Wolfe
    Commented Dec 21, 2016 at 0:36
10

With all due respect to our unknown (yahoo) poster, I look at code like that and just die a little inside. Alphabet soup is just so insanely difficult to debug. A single typo anywhere in there can really ruin your whole day. Sadly, this particular example lacked variables with underscores. It's so much more fun when we have a_b-c_d*e_f-g_h. Especially when using a font where _ and - have the same pixel length.

Taking up Suvesh Pratapa on his suggestion, I note:

Given 3x3 matrix:
       y0x0  y0x1  y0x2
       y1x0  y1x1  y1x2
       y2x0  y2x1  y2x2
Declared as double matrix [/*Y=*/3] [/*X=*/3];

(A) When taking a minor of a 3x3 array, we have 4 values of interest. The lower X/Y index is always 0 or 1. The higher X/Y index is always 1 or 2. Always! Therefore:

double determinantOfMinor( int          theRowHeightY,
                           int          theColumnWidthX,
                           const double theMatrix [/*Y=*/3] [/*X=*/3] )
{
  int x1 = theColumnWidthX == 0 ? 1 : 0;  /* always either 0 or 1 */
  int x2 = theColumnWidthX == 2 ? 1 : 2;  /* always either 1 or 2 */
  int y1 = theRowHeightY   == 0 ? 1 : 0;  /* always either 0 or 1 */
  int y2 = theRowHeightY   == 2 ? 1 : 2;  /* always either 1 or 2 */

  return ( theMatrix [y1] [x1]  *  theMatrix [y2] [x2] )
      -  ( theMatrix [y1] [x2]  *  theMatrix [y2] [x1] );
}

(B) Determinant is now: (Note the minus sign!)

double determinant( const double theMatrix [/*Y=*/3] [/*X=*/3] )
{
  return ( theMatrix [0] [0]  *  determinantOfMinor( 0, 0, theMatrix ) )
      -  ( theMatrix [0] [1]  *  determinantOfMinor( 0, 1, theMatrix ) )
      +  ( theMatrix [0] [2]  *  determinantOfMinor( 0, 2, theMatrix ) );
}

(C) And the inverse is now:

bool inverse( const double theMatrix [/*Y=*/3] [/*X=*/3],
                    double theOutput [/*Y=*/3] [/*X=*/3] )
{
  double det = determinant( theMatrix );

    /* Arbitrary for now.  This should be something nicer... */
  if ( ABS(det) < 1e-2 )
  {
    memset( theOutput, 0, sizeof theOutput );
    return false;
  }

  double oneOverDeterminant = 1.0 / det;

  for (   int y = 0;  y < 3;  y ++ )
    for ( int x = 0;  x < 3;  x ++   )
    {
        /* Rule is inverse = 1/det * minor of the TRANSPOSE matrix.  *
         * Note (y,x) becomes (x,y) INTENTIONALLY here!              */
      theOutput [y] [x]
        = determinantOfMinor( x, y, theMatrix ) * oneOverDeterminant;

        /* (y0,x1)  (y1,x0)  (y1,x2)  and (y2,x1)  all need to be negated. */
      if( 1 == ((x + y) % 2) )
        theOutput [y] [x] = - theOutput [y] [x];
    }

  return true;
}

And round it out with a little lower-quality testing code:

void printMatrix( const double theMatrix [/*Y=*/3] [/*X=*/3] )
{
  for ( int y = 0;  y < 3;  y ++ )
  {
    cout << "[  ";
    for ( int x = 0;  x < 3;  x ++   )
      cout << theMatrix [y] [x] << "  ";
    cout << "]" << endl;
  }
  cout << endl;
}

void matrixMultiply(  const double theMatrixA [/*Y=*/3] [/*X=*/3],
                      const double theMatrixB [/*Y=*/3] [/*X=*/3],
                            double theOutput  [/*Y=*/3] [/*X=*/3]  )
{
  for (   int y = 0;  y < 3;  y ++ )
    for ( int x = 0;  x < 3;  x ++   )
    {
      theOutput [y] [x] = 0;
      for ( int i = 0;  i < 3;  i ++ )
        theOutput [y] [x] +=  theMatrixA [y] [i] * theMatrixB [i] [x];
    }
}

int
main(int argc, char **argv)
{
  if ( argc > 1 )
    SRANDOM( atoi( argv[1] ) );

  double m[3][3] = { { RANDOM_D(0,1e3), RANDOM_D(0,1e3), RANDOM_D(0,1e3) },
                     { RANDOM_D(0,1e3), RANDOM_D(0,1e3), RANDOM_D(0,1e3) },
                     { RANDOM_D(0,1e3), RANDOM_D(0,1e3), RANDOM_D(0,1e3) } };
  double o[3][3], mm[3][3];

  if ( argc <= 2 )
    cout << fixed << setprecision(3);

  printMatrix(m);
  cout << endl << endl;

  SHOW( determinant(m) );
  cout << endl << endl;

  BOUT( inverse(m, o) );
  printMatrix(m);
  printMatrix(o);
  cout << endl << endl;

  matrixMultiply (m, o, mm );
  printMatrix(m);
  printMatrix(o);
  printMatrix(mm);  
  cout << endl << endl;
}

Afterthought:

You may also want to detect very large determinants as round-off errors will affect your accuracy!

3
  • "With all due respect" my code is crap? Nice. ;-) Maybe you could do the forward error analysis to determine some bounds on the round-off error instead of just picking an arbitrary constant cutoff for the determinant? 1e-2 is probably rather too conservative of a cutoff.
    – batty
    Commented Jun 12, 2009 at 23:06
  • If the determinant is very large, 1/det (which we multiply by) is close to zero. If the determinant is very small, 1/det has divide by zero issues and becomes extremely large. Where to set the thresholds is somewhat application dependent, and perhaps best decided stochastically (probabilistically) depending on your data. Alternatively, you may want to consider libgmp/libgmpxx for improved precision.
    – Mr.Ree
    Commented Jun 13, 2009 at 1:24
  • 13
    The interesting thing here is that Mr Ree's code is a few more orders of magnitude more difficult to read and understand than the one given above.
    – Robinson
    Commented Nov 11, 2012 at 18:07
4

Don't try to do this yourself if you're serious about getting edge cases right. So while they many naive/simple methods are theoretically exact, they can have nasty numerical behavior for nearly singular matrices. In particular you can get cancelation/round-off errors that cause you to get arbitrarily bad results.

A "correct" way is Gaussian elimination with row and column pivoting so that you're always dividing by the largest remaining numerical value. (This is also stable for NxN matrices.). Note that row pivoting alone doesn't catch all the bad cases.

However IMO implementing this right and fast is not worth your time - use a well tested library and there are a heap of header only ones.

3

I have just created a QMatrix class. It uses the built in vector > container. QMatrix.h It uses the Jordan-Gauss method to compute the inverse of a square matrix.

You can use it as follows:

#include "QMatrix.h"
#include <iostream>

int main(){
QMatrix<double> A(3,3,true);
QMatrix<double> Result = A.inverse()*A; //should give the idendity matrix

std::cout<<A.inverse()<<std::endl;
std::cout<<Result<<std::endl; // for checking
return 0;
}

The inverse function is implemented as follows:

Given a class with the following fields:

template<class T> class QMatrix{
public:
int rows, cols;
std::vector<std::vector<T> > A;

the inverse() function:

template<class T> 
QMatrix<T> QMatrix<T>:: inverse(){
Identity<T> Id(rows); //the Identity Matrix as a subclass of QMatrix.
QMatrix<T> Result = *this; // making a copy and transforming it to the Identity matrix
T epsilon = 0.000001;
for(int i=0;i<rows;++i){
    //check if Result(i,i)==0, if true, switch the row with another

    for(int j=i;j<rows;++j){
        if(std::abs(Result(j,j))<epsilon) { //uses Overloading()(int int) to extract element from Result Matrix
            Result.replace_rows(i,j+1); //switches rows i with j+1
        }
        else break;
    }
    // main part, making a triangular matrix
    Id(i)=Id(i)*(1.0/Result(i,i));
    Result(i)=Result(i)*(1.0/Result(i,i));  // Using overloading ()(int) to get a row form the matrix
    for(int j=i+1;j<rows;++j){
        T temp = Result(j,i);
        Result(j) = Result(j) - Result(i)*temp;
        Id(j) = Id(j) - Id(i)*temp; //doing the same operations to the identity matrix
        Result(j,i)=0; //not necessary, but looks nicer than 10^-15
    }
}

// solving a triangular matrix 
for(int i=rows-1;i>0;--i){
    for(int j=i-1;j>=0;--j){
        T temp = Result(j,i);
        Id(j) = Id(j) - temp*Id(i);
        Result(j)=Result(j)-temp*Result(i);
    }
}

return Id;
}
2
  • the QMatrix.h link is sadly dead, invalidating this answer. Perhaps the class could be included here as an appendix? Commented Oct 8, 2020 at 6:26
  • thanks for the update Kenn. It was working in 2015 :)
    – moldovean
    Commented Oct 13, 2020 at 15:09
3

A rather nice (I think) header file containing macros for most 2x2, 3x3 and 4x4 matrix operations has been available with most OpenGL toolkits. Not as standard but I've seen it at various places.

You can check it out here. At the end of it you will find both inverse of 2x2, 3x3 and 4x4.

vvector.h

2
  • 1
    Wonderful solution. I strongly prefer it to any solution that uses many function calls.
    – Stefan
    Commented Jul 31, 2013 at 12:36
  • 1
    The link is down.
    – Tim Čas
    Commented Jul 11, 2017 at 1:01
1

I would also recommend Ilmbase, which is part of OpenEXR. It's a good set of templated 2,3,4-vector and matrix routines.

1
  • This seems a very good choice to me - you can use Matrix33::inverse if you don't care about stability, and Matrix33::gjInverse if you do. Commented Aug 8, 2013 at 6:38
1
# include <conio.h>
# include<iostream.h>

const int size = 9;

int main()
{
    char ch;

    do
    {
        clrscr();
        int i, j, x, y, z, det, a[size], b[size];

        cout << "           **** MATRIX OF 3x3 ORDER ****"
             << endl
             << endl
             << endl;

        for (i = 0; i <= size; i++)
            a[i]=0;

        for (i = 0; i < size; i++)
        {
            cout << "Enter "
                 << i + 1
                 << " element of matrix=";

            cin >> a[i]; 

            cout << endl
                 <<endl;
        }

        clrscr();

        cout << "your entered matrix is "
             << endl
             <<endl;

        for (i = 0; i < size; i += 3)
            cout << a[i]
                 << "  "
                 << a[i+1]
                 << "  "
                 << a[i+2]
                 << endl
                 <<endl;

        cout << "Transpose of given matrix is"
             << endl
             << endl;

        for (i = 0; i < 3; i++)
            cout << a[i]
                 << "  "
                 << a[i+3]
                 << "  "
                 << a[i+6]
                 << endl
                 << endl;

        cout << "Determinent of given matrix is = ";

        x = a[0] * (a[4] * a[8] -a [5] * a[7]);
        y = a[1] * (a[3] * a[8] -a [5] * a[6]);
        z = a[2] * (a[3] * a[7] -a [4] * a[6]);
        det = x - y + z;

        cout << det 
             << endl
             << endl
             << endl
             << endl;

        if (det == 0)
        {
            cout << "As Determinent=0 so it is singular matrix and its inverse cannot exist"
                 << endl
                 << endl;

            goto quit;
        }

        b[0] = a[4] * a[8] - a[5] * a[7];
        b[1] = a[5] * a[6] - a[3] * a[8];
        b[2] = a[3] * a[7] - a[4] * a[6];
        b[3] = a[2] * a[7] - a[1] * a[8];
        b[4] = a[0] * a[8] - a[2] * a[6];
        b[5] = a[1] * a[6] - a[0] * a[7];
        b[6] = a[1] * a[5] - a[2] * a[4];
        b[7] = a[2] * a[3] - a[0] * a[5];
        b[8] = a[0] * a[4] - a[1] * a[3];

        cout << "Adjoint of given matrix is"
             << endl
             << endl;

        for (i = 0; i < 3; i++)
        {
            cout << b[i]
                 << "  "
                 << b[i+3]
                 << "  "
                 << b[i+6]
                 << endl
                 <<endl;
        }

        cout << endl
             <<endl;

        cout << "Inverse of given matrix is "
             << endl
             << endl
             << endl;

        for (i = 0; i < 3; i++)
        {
            cout << b[i]
                 << "/"
                 << det
                 << "  "
                 << b[i+3]
                 << "/" 
                 << det
                 << "  "
                 << b[i+6]
                 << "/" 
                 << det
                 << endl
                  <<endl;
        }

        quit:

        cout << endl
             << endl;

        cout << "Do You want to continue this again press (y/yes,n/no)";

        cin >> ch; 

        cout << endl
             << endl;
    } /* end do */

    while (ch == 'y');
    getch ();

    return 0;
}
0
#include <iostream>
using namespace std;

int main()
{
    double A11, A12, A13;
    double A21, A22, A23;
    double A31, A32, A33;

    double B11, B12, B13;
    double B21, B22, B23;
    double B31, B32, B33;

    cout << "Enter all number from left to right, from top to bottom, and press enter after every number: ";
    cin  >> A11;
    cin  >> A12;
    cin  >> A13;
    cin  >> A21;
    cin  >> A22;
    cin  >> A23;
    cin  >> A31;
    cin  >> A32;
    cin  >> A33;

    B11 = 1 / ((A22 * A33) - (A23 * A32));
    B12 = 1 / ((A13 * A32) - (A12 * A33));
    B13 = 1 / ((A12 * A23) - (A13 * A22));
    B21 = 1 / ((A23 * A31) - (A21 * A33));
    B22 = 1 / ((A11 * A33) - (A13 * A31));
    B23 = 1 / ((A13 * A21) - (A11 * A23));
    B31 = 1 / ((A21 * A32) - (A22 * A31));
    B32 = 1 / ((A12 * A31) - (A11 * A32));
    B33 = 1 / ((A11 * A22) - (A12 * A21));

    cout << B11 << "\t" << B12 << "\t" << B13 << endl;
    cout << B21 << "\t" << B22 << "\t" << B23 << endl;
    cout << B31 << "\t" << B32 << "\t" << B33 << endl;

    return 0;
}
2
  • 2
    This was my try... i guess it doesn't work right... sorry. lol. I didn't try it before hand. :/
    – Matthew
    Commented Feb 19, 2013 at 2:45
  • 1
    It's bad mojo to have a code answer which is known to be defective. @Matthew, if you can't confirm that this works, can you remove the answer? Commented Oct 8, 2020 at 6:27
0
//Title: Matrix Header File
//Writer: Say OL
//This is a beginner code not an expert one
//No responsibilty for any errors
//Use for your own risk
using namespace std;
int row,col,Row,Col;
double Coefficient;
//Input Matrix
void Input(double Matrix[9][9],int Row,int Col)
{
    for(row=1;row<=Row;row++)
        for(col=1;col<=Col;col++)
        {
            cout<<"e["<<row<<"]["<<col<<"]=";
            cin>>Matrix[row][col];
        }
}
//Output Matrix
void Output(double Matrix[9][9],int Row,int Col)
{
    for(row=1;row<=Row;row++)
    {
        for(col=1;col<=Col;col++)
            cout<<Matrix[row][col]<<"\t";
        cout<<endl;
    }
}
//Copy Pointer to Matrix
void CopyPointer(double (*Pointer)[9],double Matrix[9][9],int Row,int Col)
{
    for(row=1;row<=Row;row++)
        for(col=1;col<=Col;col++)
            Matrix[row][col]=Pointer[row][col];
}
//Copy Matrix to Matrix
void CopyMatrix(double MatrixInput[9][9],double MatrixTarget[9][9],int Row,int Col)
{
    for(row=1;row<=Row;row++)
        for(col=1;col<=Col;col++)
            MatrixTarget[row][col]=MatrixInput[row][col];
}
//Transpose of Matrix
double MatrixTran[9][9];
double (*(Transpose)(double MatrixInput[9][9],int Row,int Col))[9]
{
    for(row=1;row<=Row;row++)
        for(col=1;col<=Col;col++)
            MatrixTran[col][row]=MatrixInput[row][col];
    return MatrixTran;
}
//Matrix Addition
double MatrixAdd[9][9];
double (*(Addition)(double MatrixA[9][9],double MatrixB[9][9],int Row,int Col))[9]
{
    for(row=1;row<=Row;row++)
        for(col=1;col<=Col;col++)
            MatrixAdd[row][col]=MatrixA[row][col]+MatrixB[row][col];
    return MatrixAdd;
}
//Matrix Subtraction
double MatrixSub[9][9];
double (*(Subtraction)(double MatrixA[9][9],double MatrixB[9][9],int Row,int Col))[9]
{
    for(row=1;row<=Row;row++)
        for(col=1;col<=Col;col++)
            MatrixSub[row][col]=MatrixA[row][col]-MatrixB[row][col];
    return MatrixSub;
}
//Matrix Multiplication
int mRow,nCol,pCol,kcol;
double MatrixMult[9][9];
double (*(Multiplication)(double MatrixA[9][9],double MatrixB[9][9],int mRow,int nCol,int pCol))[9]
{
    for(row=1;row<=mRow;row++)
        for(col=1;col<=pCol;col++)
        {
            MatrixMult[row][col]=0.0;
            for(kcol=1;kcol<=nCol;kcol++)
                MatrixMult[row][col]+=MatrixA[row][kcol]*MatrixB[kcol][col];
        }
    return MatrixMult;
}
//Interchange Two Rows
double RowTemp[9][9];
double MatrixInter[9][9];
double (*(InterchangeRow)(double MatrixInput[9][9],int Row,int Col,int iRow,int jRow))[9]
{
    CopyMatrix(MatrixInput,MatrixInter,Row,Col);
    for(col=1;col<=Col;col++)
    {
        RowTemp[iRow][col]=MatrixInter[iRow][col];
        MatrixInter[iRow][col]=MatrixInter[jRow][col];
        MatrixInter[jRow][col]=RowTemp[iRow][col];
    }
    return MatrixInter;
}
//Pivote Downward
double MatrixDown[9][9];
double (*(PivoteDown)(double MatrixInput[9][9],int Row,int Col,int tRow,int tCol))[9]
{
    CopyMatrix(MatrixInput,MatrixDown,Row,Col);
    Coefficient=MatrixDown[tRow][tCol];
    if(Coefficient!=1.0)
        for(col=1;col<=Col;col++)
            MatrixDown[tRow][col]/=Coefficient;
    if(tRow<Row)
        for(row=tRow+1;row<=Row;row++)
        {
            Coefficient=MatrixDown[row][tCol];
            for(col=1;col<=Col;col++)
                MatrixDown[row][col]-=Coefficient*MatrixDown[tRow][col];
        }
return MatrixDown;
}
//Pivote Upward
double MatrixUp[9][9];
double (*(PivoteUp)(double MatrixInput[9][9],int Row,int Col,int tRow,int tCol))[9]
{
    CopyMatrix(MatrixInput,MatrixUp,Row,Col);
    Coefficient=MatrixUp[tRow][tCol];
    if(Coefficient!=1.0)
        for(col=1;col<=Col;col++)
            MatrixUp[tRow][col]/=Coefficient;
    if(tRow>1)
        for(row=tRow-1;row>=1;row--)
        {
            Coefficient=MatrixUp[row][tCol];
            for(col=1;col<=Col;col++)
                MatrixUp[row][col]-=Coefficient*MatrixUp[tRow][col];
        }
    return MatrixUp;
}
//Pivote in Determinant
double MatrixPiv[9][9];
double (*(Pivote)(double MatrixInput[9][9],int Dim,int pTarget))[9]
{
    CopyMatrix(MatrixInput,MatrixPiv,Dim,Dim);
    for(row=pTarget+1;row<=Dim;row++)
    {
        Coefficient=MatrixPiv[row][pTarget]/MatrixPiv[pTarget][pTarget];
        for(col=1;col<=Dim;col++)
        {
            MatrixPiv[row][col]-=Coefficient*MatrixPiv[pTarget][col];
        }
    }
    return MatrixPiv;
}
//Determinant of Square Matrix
int dCounter,dRow;
double Det;
double MatrixDet[9][9];
double Determinant(double MatrixInput[9][9],int Dim)
{
    CopyMatrix(MatrixInput,MatrixDet,Dim,Dim);
    Det=1.0;
    if(Dim>1)
    {
        for(dRow=1;dRow<Dim;dRow++)
        {
            dCounter=dRow;
            while((MatrixDet[dRow][dRow]==0.0)&(dCounter<=Dim))
            {
                dCounter++;
                Det*=-1.0;
                CopyPointer(InterchangeRow(MatrixDet,Dim,Dim,dRow,dCounter),MatrixDet,Dim,Dim);
            }
            if(MatrixDet[dRow][dRow]==0)
            {
                Det=0.0;
                break;
            }
            else
            {
                Det*=MatrixDet[dRow][dRow];
                CopyPointer(Pivote(MatrixDet,Dim,dRow),MatrixDet,Dim,Dim);
            }
        }
        Det*=MatrixDet[Dim][Dim];
    }
    else Det=MatrixDet[1][1];
    return Det;
}
//Matrix Identity
double MatrixIdent[9][9];
double (*(Identity)(int Dim))[9]
{
    for(row=1;row<=Dim;row++)
        for(col=1;col<=Dim;col++)
            if(row==col)
                MatrixIdent[row][col]=1.0;
            else
                MatrixIdent[row][col]=0.0;
    return MatrixIdent;
}
//Join Matrix to be Augmented Matrix
double MatrixJoin[9][9];
double (*(JoinMatrix)(double MatrixA[9][9],double MatrixB[9][9],int Row,int ColA,int ColB))[9]
{
    Col=ColA+ColB;
    for(row=1;row<=Row;row++)
        for(col=1;col<=Col;col++)
            if(col<=ColA)
                MatrixJoin[row][col]=MatrixA[row][col];
            else
                MatrixJoin[row][col]=MatrixB[row][col-ColA];
    return MatrixJoin;
}
//Inverse of Matrix
double (*Pointer)[9];
double IdentMatrix[9][9];
int Counter;
double MatrixAug[9][9];
double MatrixInv[9][9];
double (*(Inverse)(double MatrixInput[9][9],int Dim))[9]
{
    Row=Dim;
    Col=Dim+Dim;
    Pointer=Identity(Dim);
    CopyPointer(Pointer,IdentMatrix,Dim,Dim);
    Pointer=JoinMatrix(MatrixInput,IdentMatrix,Dim,Dim,Dim);
    CopyPointer(Pointer,MatrixAug,Row,Col);
    for(Counter=1;Counter<=Dim;Counter++)   
    {
        Pointer=PivoteDown(MatrixAug,Row,Col,Counter,Counter);
        CopyPointer(Pointer,MatrixAug,Row,Col);
    }
    for(Counter=Dim;Counter>1;Counter--)
    {
        Pointer=PivoteUp(MatrixAug,Row,Col,Counter,Counter);
        CopyPointer(Pointer,MatrixAug,Row,Col);
    }
    for(row=1;row<=Dim;row++)
        for(col=1;col<=Dim;col++)
            MatrixInv[row][col]=MatrixAug[row][col+Dim];
    return MatrixInv;
}
//Gauss-Jordan Elemination
double MatrixGJ[9][9];
double VectorGJ[9][9];
double (*(GaussJordan)(double MatrixInput[9][9],double VectorInput[9][9],int Dim))[9]
{
    Row=Dim;
    Col=Dim+1;
    Pointer=JoinMatrix(MatrixInput,VectorInput,Dim,Dim,1);
    CopyPointer(Pointer,MatrixGJ,Row,Col);
    for(Counter=1;Counter<=Dim;Counter++)   
    {
        Pointer=PivoteDown(MatrixGJ,Row,Col,Counter,Counter);
        CopyPointer(Pointer,MatrixGJ,Row,Col);
    }
    for(Counter=Dim;Counter>1;Counter--)
    {
        Pointer=PivoteUp(MatrixGJ,Row,Col,Counter,Counter);
        CopyPointer(Pointer,MatrixGJ,Row,Col);
    }
    for(row=1;row<=Dim;row++)
        for(col=1;col<=1;col++)
            VectorGJ[row][col]=MatrixGJ[row][col+Dim];
    return VectorGJ;
}
//Generalized Gauss-Jordan Elemination
double MatrixGGJ[9][9];
double VectorGGJ[9][9];
double (*(GeneralizedGaussJordan)(double MatrixInput[9][9],double VectorInput[9][9],int Dim,int vCol))[9]
{
    Row=Dim;
    Col=Dim+vCol;
    Pointer=JoinMatrix(MatrixInput,VectorInput,Dim,Dim,vCol);
    CopyPointer(Pointer,MatrixGGJ,Row,Col);
    for(Counter=1;Counter<=Dim;Counter++)   
    {
        Pointer=PivoteDown(MatrixGGJ,Row,Col,Counter,Counter);
        CopyPointer(Pointer,MatrixGGJ,Row,Col);
    }
    for(Counter=Dim;Counter>1;Counter--)
    {
        Pointer=PivoteUp(MatrixGGJ,Row,Col,Counter,Counter);
        CopyPointer(Pointer,MatrixGGJ,Row,Col);
    }
    for(row=1;row<=Row;row++)
        for(col=1;col<=vCol;col++)
            VectorGGJ[row][col]=MatrixGGJ[row][col+Dim];
    return VectorGGJ;
}
//Matrix Sparse, Three Diagonal Non-Zero Elements
double MatrixSpa[9][9];
double (*(Sparse)(int Dimension,double FirstElement,double SecondElement,double ThirdElement))[9]
{
    MatrixSpa[1][1]=SecondElement;
    MatrixSpa[1][2]=ThirdElement;
    MatrixSpa[Dimension][Dimension-1]=FirstElement;
    MatrixSpa[Dimension][Dimension]=SecondElement;
    for(int Counter=2;Counter<Dimension;Counter++)
    {
        MatrixSpa[Counter][Counter-1]=FirstElement;
        MatrixSpa[Counter][Counter]=SecondElement;
        MatrixSpa[Counter][Counter+1]=ThirdElement;
    }
    return MatrixSpa;
}

Copy and save the above code as Matrix.h then try the following code:

#include<iostream>
#include<conio.h>
#include"Matrix.h"
int Dim;
double Matrix[9][9];
int main()
{
    cout<<"Enter your matrix dimension: ";
    cin>>Dim;
    Input(Matrix,Dim,Dim);
    cout<<"Your matrix:"<<endl;
    Output(Matrix,Dim,Dim);
    cout<<"The inverse:"<<endl;
    Output(Inverse(Matrix,Dim),Dim,Dim);
    getch();
}
1
  • what method did you use to compute the inverse, Say OL?
    – moldovean
    Commented Apr 16, 2015 at 21:56
0
//Function for inverse of the input square matrix 'J' of dimension 'dim':

vector<vector<double > > inverseVec33(vector<vector<double > > J, int dim)
{
//Matrix of Minors
 vector<vector<double > > invJ(dim,vector<double > (dim));
for(int i=0; i<dim; i++)
{
    for(int j=0; j<dim; j++)
    {
        invJ[i][j] = (J[(i+1)%dim][(j+1)%dim]*J[(i+2)%dim][(j+2)%dim] -
                      J[(i+2)%dim][(j+1)%dim]*J[(i+1)%dim][(j+2)%dim]);
    }
}

//determinant of the matrix:
double detJ = 0.0;
for(int j=0; j<dim; j++)
{ detJ += J[0][j]*invJ[0][j];}

//Inverse of the given matrix.
 vector<vector<double > > invJT(dim,vector<double > (dim));
 for(int i=0; i<dim; i++)
{
    for(int j=0; j<dim; j++)
    {
        invJT[i][j] = invJ[j][i]/detJ;
    }
}

return invJT;
}

void main()
{
    //given matrix:
vector<vector<double > > Jac(3,vector<double > (3));
Jac[0][0] = 1; Jac[0][1] = 2;  Jac[0][2] = 6;
Jac[1][0] = -3; Jac[1][1] = 4;  Jac[1][2] = 3;
Jac[2][0] = 5; Jac[2][1] = 1;  Jac[2][2] = -4;`

//Inverse of the matrix Jac:
vector<vector<double > > JacI(3,vector<double > (3));
    //call function and store inverse of J as JacI:
JacI = inverseVec33(Jac,3);
}
1
  • you don't really need dim if you use the vector library because you can use the property .size()
    – moldovean
    Commented Apr 16, 2015 at 21:59

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