# Investing a matrix c++ code

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Now, a basic implementation would have a brute force search, while a more sofisticated one would have a binary search. I wanted to try both even if my profiler didn't spot that as an issue and, guess what: for the number of non zero columns I typically have, it didn't change at all the performances.

If I had to bet on something, if an issue is there is that you manipulate such a fundamental thing like a 2D matrix trough OOP and stuff. They should be your first-class citizens natively not in the OOP meaning of the term. Hey sbaffini, Thanks for the help. Currently I don't need to do any complex matrix operations like inversion, cross multiplication etc. In the previous version I used Eigen3. I liked it. That's why the mathematical operations are in fortran. The multiplication operations will definitely make it slower.

I don't want this very basic access to be slow. In the first for loop, we are taking the values of the different elements of the array from the user one by one. In the second for loop, we are printing the values of the elements of the array.

Let's go to the first for loop. In the first iteration, the value of i is 0, so 'n[i]' is 'n[0]'. Similary in the second iteration, the value of 'i' will be 1 and 'n[i]' will be 'n[1]'. Array allocates contiguous memory. This means that the memories of all elements of an array are allocated together and are continuous.

Pointer to Arrays Till now, you have seen how to declare and assign values to an array. Now, you will see how we can have pointers to arrays too. But before starting, we are assuming that you have gone through Pointers.

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I wondered which method is the fastest, or the one with the best performance, and trying to found that answer I found nothing. I know that for some cases a pseudo-inverse can be computed using SVD, cholevsky, It is easy to find an specific answer for an specific problem but not a general intuition for this big HUGE! So my question is: What method is best in performance for small matrices? And in precision? What about big matrices?

My personal case is a 6x6 EDIT:symetric matrix that have to be inverted thousands of times yes,yes, with different values and I need high precision, but for sure speed would come really handy. The solution can then be generalized to find the inverse of the NxN matrices.

Then, we applied conditional checks to determine if the matrix is singular or not. If non-singular, then the inverse of the matrix will be calculated by function. Here is the explanation of each function we have created. Here matrix3X3 is the matrix whose values are to be displayed. Here, det is the matrix matrix3X3 determinant. After seeing the determinant, we can conclude whether or not the matrix inverse is possible.

After finding the adjoint matrix, we will multiply it with the reciprocal of the determinant to find the inverse. In this function, each entry of the adjoint matrix is divided by the determinant to find the matrix inverse. Output when the input matrix is non-singular: The output shows that our algorithm calculates the inverse only if the matrix is non-singular.

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