Matrix Division in Eigen vs MATLAB

I am trying to find the equivalent of the opertaor in MATLAB:
 
A = B/C

where here A,B, and C are all square matrices with say N-by-N .

So I have the following C++ code where I tried using Eigen but I am not getting correct results:
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Eigen::Matrix< double, (ny+1), (ny+1)> A; //same initialization for B and C

	for (int i = 0; i < ny+1; i++){
		for (int j = 0; j < ny+1; j++){
                C(j + (ny+1)*i) = cos(acos(ygl[j]) *(i));
		B(0,j) = -1. * pow(-1,(j))*(pow(j,2));//replace 1st row with expression
		B((ny),j) =  1. * pow(j,2);//replace last row
		A(j + (ny+1)*i) = (B(j + (ny+1)*i))/(C(j + (ny+1)*i));
		}
	}
	std::cout << A << "\n";


Is matrix division done just simply by / with Eigen like MATLAB or is there another way? Thanks
Last edited on
there really is no such thing as matrix division.
matlab lets you do it and understands that you are using it as a convoluted way to write ax=b (rewritten improperly as x = b/a or b\a or whatever it is, my matlab is a few years behind me)

so in eigen you need to either do the underlying math by hand or use a solver depending on what you think division means. If you think a*b = c, then it may involve an inverse or pseudoinverse.
if you want the solution to a system, then you need to use whatever they provide for that.

I guess I can give an example in MATLAB.

This, in C++ is equivalent to x = A\b in MATLAB:
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	x = A.colPivHouseholderQr().solve(b);

But, I am looking for something equivalent to x = A/b in MATLAB. For example in MATLAB:
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A =

     4    12
     6     8
b =

     6    12
    14     8

>> A/b

    1.1333   -0.2000
    0.5333    0.2000

which is different from:
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>> A\b

    3.0000         0
   -0.5000    1.0000

So I am trying to calculate x = A/b , the first example.
I just solved my issue and the it's basically,
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X=A\B computes X=inv(A)*B.
Y=A/B computes Y=A*inv(B)


so then in C++ with Eigen:
 
x= A* B.inverse();
So I am trying to calculate x = A/b , the first example.


This duplicates the output in your first MATLAB example:
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#include <Eigen/Core>
#include <Eigen/QR>
#include <iostream>

int main() 
{
  Eigen::Matrix2f A { { 4.f, 12.f }, { 6.f,  8.f } }; 
  Eigen::Matrix2f B { { 6.f, 12.f }, { 14.f, 8.f } }; 
  std::cout << B.transpose().colPivHouseholderQr().solve(A.transpose()).transpose();  
}


Could be faster than computing the inverse matrix, but YMMV
Last edited on
@mbozzi
Thanks! This is pretty helpful actually.
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