linear-algebra

问题 I wrote a Conjugate-gradient solver (for linear system of equations) with LU preconditioning, I used Dr. Maxim Naumov's papers on nvidia's research community as a guideline, the residuals update step, which requires solving a lower triangular matrix system and then solving an upper triangular matrix system is divided into two phases: analysis phase (which exploits the sparsity pattern and decides the parallelization level). the solution phase itself. according to all posts related to this

问题 I need to solve a system of linear equations Lx=b, where x is always a vector (3x1 array), L is an Nx3 array, and b is an Nx1 vector. N usually ranges from 4 to something like 10. I have no problems solving this using scipy.linalg.lstsq(L,b) However, I need to do this many times (something like 200x200=40000 times) as x is actually something associated with each pixel in an image. So x is actually stored in an PxQx3 array where P and Q is something like 200-300, and the last number '3' refers

问题 I have an array A whose shape is (N, N, K) and I would like to compute another array B with the same shape where B[:, :, i] = np.linalg.inv(A[:, :, i]) . As solutions, I see map and for loops but I am wondering if numpy provides a function to do this (I have tried np.apply_over_axes but it seems that it can only handle 1D array). with a for loop: B = np.zeros(shape=A.shape) for i in range(A.shape[2]): B[:, :, i] = np.linalg.inv(A[:, :, i]) with map : B = np.asarray(map(np.linalg.inv, np

问题 I have an array A whose shape is (N, N, K) and I would like to compute another array B with the same shape where B[:, :, i] = np.linalg.inv(A[:, :, i]) . As solutions, I see map and for loops but I am wondering if numpy provides a function to do this (I have tried np.apply_over_axes but it seems that it can only handle 1D array). with a for loop: B = np.zeros(shape=A.shape) for i in range(A.shape[2]): B[:, :, i] = np.linalg.inv(A[:, :, i]) with map : B = np.asarray(map(np.linalg.inv, np

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 years ago . I'm trying to port some Python code to Scala. It makes heavy use of Numpy and Scipy. While I've found a number of dense matrix / linear algebra libraries that will do as an adequate (but not superb) replacement for NumPy, I've not really found anything that provides the functionality I use in SciPy. In

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 years ago . I don't need anything fancy; basically, if it will let me do some basic Matrix*Matrix stuff I'll be happy. 回答1: You could use numericjs.com. It's a javascript numerical analysis library that I'm working on that has several standard matrix algorithms for arbitrary sized dense matrices. 回答2: Numericjs.com is good

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 years ago . I don't need anything fancy; basically, if it will let me do some basic Matrix*Matrix stuff I'll be happy. 回答1: You could use numericjs.com. It's a javascript numerical analysis library that I'm working on that has several standard matrix algorithms for arbitrary sized dense matrices. 回答2: Numericjs.com is good

问题 in the example below, there is a 3d numpy matrix of size (4, 3, 3)+ a solution about how to calculate pinv of each of 4 of those 3*3 matrices in numpy. I also tried to use the same function worked in numpy, in theano hoping that it is implemented the same, but it failed. Any idea how to do it in theano? dt = np.dtype(np.float32) a=[[[12,3,1], [2,4,1], [2,4,2],], [[12,3,3], [2,4,4], [2,4,5],], [[12,3,6], [2,4,5], [2,4,4],], [[12,3,3], [2,4,5], [2,4,6]]] a=np.asarray(a,dtype=dt) print(a.shape)

问题 Let's say I've got a matrix like this: mat1 = np.array([1,0,1], [1,1,0], [0,0,0]); And I've got another one like this: mat2 = np.array([0,1,0], [0,0,1], [1,1,1]); I want to detect if something like np.add(mat1, mat2); has only 1's or 0's, namely some 1's and some 0's, all 0's, or all 1's. n.b. - Comment your code. 回答1: How about this: >>> def check(matrix): ... # flatten up the matrix into one single list ... # and set on the list it should be [0,1] if it ... # contains only 0 and 1. Then do

问题 Let's say I've got a matrix like this: mat1 = np.array([1,0,1], [1,1,0], [0,0,0]); And I've got another one like this: mat2 = np.array([0,1,0], [0,0,1], [1,1,1]); I want to detect if something like np.add(mat1, mat2); has only 1's or 0's, namely some 1's and some 0's, all 0's, or all 1's. n.b. - Comment your code. 回答1: How about this: >>> def check(matrix): ... # flatten up the matrix into one single list ... # and set on the list it should be [0,1] if it ... # contains only 0 and 1. Then do