javascript,matrix,three.js,matrix-inverse

three.js is Matrix4-based, and hence does not have a method to invert a Matrix3 directly. One solution is to use Matrix4 in your code everywhere. The other solution is to populate a Matrix4 from your Matrix3, and then call getInverse(). var m3 = new THREE.Matrix3(); m3.set( 10,8,3, 15,7,2, 10,6,1 );...

No. It seems as if there is no routine to directly calculate the pseudo-inverse of a matrix (although here you can find a discussion on how one could get it). However, the explicit pseudo-inverse itself is seldom required. Instead, gsl provides the routine int gsl_linalg_SV_solve (const gsl_matrix * U, const...

matlab,kalman-filter,matrix-inverse

As far as I understand it, the R matrix is supposed to be the covariance matrix for the measurement noise. The following lines: R=sigma_2_v*diag(diag(x)); R = diag(R); Change R from a 2x2 diagonal matrix to a 2x1 column vector. Since your observation y is a scalar, the observation noise v...

matlab,time-series,covariance,correlation,matrix-inverse

For p=2, expression for y_tminus1 should be (see expressions after Eq.1 in the paper) y_tminus1 = Y(end-2:end-1).' Your mult is OK. For Eq. 20 you need to take expectation E(mult). For this you need to generate multiple paths and take an average over them. For the RHS of Eq. 25,...

c++,matrix,maya,matrix-inverse

It won't calculate unless you plug the outputs into something usually, but you haven't given it an if statement to have it not compute. My guess is that your doing this: // set value to handle and then set clean hOutTransX.set( transInMatrixX ); hOutTransX.setClean(); hOutTransY.set( transInMatrixY ); hOutTransY.setClean(); hOutTransZ.set( transInMatrixZ...

You didn't tell what your computing environment is, but I believe it is safe to say that it didn't solve a 50000 points kriging problem in a second. In order to understand what it did, please provide more information, e.g. the commands you used, and the output gstat gave.

matlab,correlation,matrix-inverse,determinants,fminsearch

How about sqrt(det(Gamma)) for the sqrt-determinant and inv(Gamma) for inverse? But if you do not want to implement it yourself you can look at yulewalkerarestimator UPD: For estimation of autocovariance matrix use xcov also, this topic is a bit more explained here...

math,matrix,3d,projection,matrix-inverse

To define the ray you need a start point (which is the camera/eye position) and a direction vector, which can be calculated using any point on the ray. For a given pixel in the image, you have a projected X and Y (zeroed at the center of the image) but...

The Moore–Penrose pseudo inverse, which is the basis for Matab and octave's pinv, is implemented via completely different algorithm than the inv function. More specifically, singular value decomposition is used, which require's finite-valued matrices (they also can't be sparse). You didn't say if your matrices are square or not. The...

A <- matrix(c(3529861.470,8785861.47,6920.344,17120.34, 8785861.470,26209861.47,17120.344,51920.34, 6920.344,17120.34,14.000,34.00, 17120.344,51920.34,34.000,104.00), nrow=4,byrow=TRUE) solve(A) ## works on my system ## [,1] [,2] [,3] [,4] ## [1,] -1.2515442 0.7617239 535.4871 -349.3141 ## [2,] 0.7617072 -0.4635922 -325.9051 212.5957 ## [3,] 535.4884664 -325.9130639 -229114.3516 149458.2734 ## [4,] -349.3061387 212.5955306 149454.4973 -97492.7335 eigen(A)$values ## [1] 2.921525e+07 5.245875e+05 1.440703e+00 -3.061760e-06 rcond(A)...

I tried to fix your logic (and the resulting code is very inefficient and can be much better written). Some issues I think I found are: Variable addOn was not initialized Your b was incorrect (bad subtraction) given the definition of B=I-A in your preface. Your accumulation of addOn =...

python,numpy,matrix,matrix-inverse,determinants

The numerical inversion of matrices does not involve computing the determinant. (Cramer's formula for the inverse is not practical for large matrices.) So, the fact that determinant evaluates to 0 (due to insufficient precision of floats) is not an obstacle for the matrix inversion routine. Following up on the comments...

I think I got this! To get the other (wrong) answer, I think you did: >> K=[6,31,221;31,221,1801;221,1801,15665]; >> inv(K) ans = 1.5647 -0.6276 0.0501 -0.6276 0.3235 -0.0283 0.0501 -0.0283 0.0026 >> iK=[ 1.5647 -0.6276 0.0501; -0.6276 0.3235 -0.0283; 0.0501 -0.0283 0.0026]; >> f=[31;197;1543]; >> iK*f ans = 2.1728 0.6070 -0.0102...

matlab,function,matrix,matrix-multiplication,matrix-inverse

Since (A*B)' = B'*A', you probably just need to call matok(inv(D) * Ps) ...

python,numpy,vectorization,matrix-inverse

NumPy 1.8 included linear algebra gufuncs, which do exactly what you are after. While np.linalg.pinv is not gufunc-ed, np.linalg.svd is, and behind the scenes that is the function that gets called. So you can define your own gupinv function, based on the source code of the original function, as follows:...

If you compute the determinant of the matrix, it is 0: det(x) [1] 0 By definition, your matrix is not invertible. But before trying to invert a squared matrix, the first instinct should be to study analytically if the matrix can be invertible. The singular error you get just reflects...

matlab,time-series,correlation,matrix-inverse

You might be confusing the Correlation matrix of a random vector (multivariate random variable), and the autocorrelation matrix of a random process (stochastic process)... So if your serie is a vector autoregressive model of order 1 (which it seems to be, so h' is your coefficient matrix), then indeed E[y(t-1)*y(t-1)']...