matrix-inverse

问题 i am looking for a sample code implementation on how to invert a 4x4 matrix. i know there is gaussian eleminiation, LU decomposition, etc. but instead of looking at them in detail i am really just looking for the code to do this. language ideally C++, data is available in array of 16 floats in cloumn-major order. thank you! 回答1: here: bool gluInvertMatrix(const double m[16], double invOut[16]) { double inv[16], det; int i; inv[0] = m[5] * m[10] * m[15] - m[5] * m[11] * m[14] - m[9] * m[6] * m

问题 i am looking for a sample code implementation on how to invert a 4x4 matrix. i know there is gaussian eleminiation, LU decomposition, etc. but instead of looking at them in detail i am really just looking for the code to do this. language ideally C++, data is available in array of 16 floats in cloumn-major order. thank you! 回答1: here: bool gluInvertMatrix(const double m[16], double invOut[16]) { double inv[16], det; int i; inv[0] = m[5] * m[10] * m[15] - m[5] * m[11] * m[14] - m[9] * m[6] * m

问题 Following a previous question ( Pycuda - Best way to perform a high number of small matrix inversion - CUBLAS or MAGMA ), considering the inversion of 4x4 matrix, I would like to do the same but with 3x3 matrix. As @Robert Crovella said, this change implies a complete rewrite. Given the code shown below, I tried to test some things like putting zeros instead of values but this method doesn't seem to work. Here is the code working for a high number of 4x4 matrix inversion $ cat t10.py import

问题 As data I get a matrix A but in my algorithm I need to work on its inverse. What I do is: C = inv(A) + B; Then in another line I update A. In the next cycles I also need (updated) A inverse, again for this algorithm. And so on. In the later cycles I get this: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND = 1.425117e-019 or this: Warning: Matrix is singular to working precision. or this: Warning: Matrix is singular, close to singular or badly scaled. Results may

问题 ginv() function from MASS package in R produce totally different values compared to MATLAB pinv() function. They both claim to produce Moore-Penrose generalized inverse of a matrix. I tried to set the same tolerance for the R implementation but the difference persists. MATLAB default tol : max(size(A)) * norm(A) * eps(class(A)) R default tol : sqrt(.Machine$double.eps) Reproduction: R: library(MASS) A <- matrix(c(47,94032,149, 94032, 217179406,313679,149,313679,499),3,3) ginv(A) outputs: [,1]

问题 I am finding it very hard to understand the way the inverse of the matrix is calculated in the Hill Cipher algorithm. I get the idea of it all being done in modulo arithmetic, but somehow things are not adding up. I would really appreciate a simple explanation! Consider the following Hill Cipher key matrix: 5 8 17 3 Please use the above matrix for illustration. 回答1: You must study the Linear congruence theorem and the extended GCD algorithm, which belong to Number Theory, in order to

问题 I wrote a inversion function for an n*n square matrix. void inverseMatrix(int n, float **matrix) { float ratio,a; int i, j, k; for(i = 0; i < n; i++) { for(j = n; j < 2*n; j++) { if(i==(j-n)) matrix[i][j] = 1.0; else matrix[i][j] = 0.0; } } for(i = 0; i < n; i++) { for(j = 0; j < n; j++) { if(i!=j) { ratio = matrix[j][i]/matrix[i][i]; for(k = 0; k < 2*n; k++) { matrix[j][k] -= ratio * matrix[i][k]; } } } } for(i = 0; i < n; i++) { a = matrix[i][i]; for(j = 0; j < 2*n; j++) { matrix[i][j] /= a

问题 I am a newbie to matlab and I am trying to find out the inverse of matrix with very small values. When i try to find the inverse I get an error saying that the matrix is singular. One of the solutions suggested is to try and add some elements to the diagonal elements. I know i have to use eye and diag methods but I am not able come up the correct soltion. Any comments will be helpful. 回答1: If you just want to add the identity matrix or a multiple of it to your square matrix, you can do A_new

问题 I have a sparse matrix W, when I use linalg.pinv(W) , it throws some errors: Traceback (most recent call last): File "/Users/ad9075/PycharmProjects/bednmf/test.py", line 14, in testNmfRun self.factor = factorization(self.V) File "/Users/ad9075/PycharmProjects/bednmf/nmf.py", line 18, in factorization W_trans = linalg.pinv(W) File "/Library/Python/2.7/site-packages/scipy/linalg/basic.py", line 540, in pinv b = np.identity(a.shape[0], dtype=a.dtype) IndexError: tuple index out of range` But

问题 I compared various methods to compute the inverse of a symmetric matrix: solve (from the package LAPCK) solve (but using a higher machine precision) qr.solve (said to be faster) ginv (MASS package, implementation of the Moore-Penrose algo) chol2inv (using the Cholesky decomposition) The inverse-matrix was compared through their eigenvalues: R library(MASS) ## Create the matrix m = replicate(10, runif(n=10)) m[lower.tri(m)] = t(m)[lower.tri(m)] ## Inverse the matrix inv1 = solve(m) inv2 =