matrix-multiplication

问题 Hello I am trying to run the example from here: http://rtutorial.altervista.org/lp_solvers.html A snippet and test where it goes wrong: library(CVXR) #create Variable objects that can be manipulated by the solver. x<-Variable(3) #coefficients for objective function C<-c(2,4,3) #problem: C %*% x Error: Error in mul_dims_promote(lh_dim, rh_dim) : Incompatible dimensions > x [1] "Variable((3, 1), nonneg=FALSE, nonpos=FALSE, pos=FALSE, neg=FALSE, complex=FALSE, imag=FALSE, symmetric=FALSE, diag

问题 I've been using GPU for a while without questioning it but now I'm curious. Why can GPU do matrix multiplication much faster than CPU? Is it because of the parallel processing? But I didn't write any parallel processing code. Does it do it automatically by itself? Any intuition / high-level explanation will be appreciated! Thanks. 回答1: How do you parallelize the computations? GPU's are able to do a lot of parallel computations. A Lot more than a CPU could do. Look at this example of vector

问题 I need some help with massive usage of memory by the NbClust function. On my data, memory balloons to 56GB at which point R crashes with a fatal error. Using debug() , I was able to trace the error to these lines: if (any(indice == 23) || (indice == 32)) { res[nc - min_nc + 1, 23] <- Index.sPlussMoins(cl1 = cl1, md = md)$gamma Debugging of Index.sPlussMoins revealed that the crash happens during a for loop. The iteration that it crashes at varies, and during the loop memory usage varies

问题 Is there any difference? If not, what is preferred by convention? The performance seems to be almost the same. a=np.random.rand(1000,1000) b=np.random.rand(1000,1000) %timeit a.dot(b) #14.3 ms ± 374 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) %timeit np.dot(a,b) #14.7 ms ± 315 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) %timeit a @ b #15.1 ms ± 779 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 回答1: They are all basically doing the same thing. In terms of

问题 Is there a difference between import numpy as np np.dot(a,b) and a.dot(b) internally? I wasn't able to find any documentation on the latter method. 回答1: If a is an array, they're equivalent. The docs you couldn't find for the dot method are here, and they boil down to "see numpy.dot". If type(a) is not numpy.ndarray , then numpy.dot will convert a to an array and use the array for the multiplication, while a.dot will do whatever a 's type says it does, or raise an AttributeError if a doesn't

问题 Is there a difference between import numpy as np np.dot(a,b) and a.dot(b) internally? I wasn't able to find any documentation on the latter method. 回答1: If a is an array, they're equivalent. The docs you couldn't find for the dot method are here, and they boil down to "see numpy.dot". If type(a) is not numpy.ndarray , then numpy.dot will convert a to an array and use the array for the multiplication, while a.dot will do whatever a 's type says it does, or raise an AttributeError if a doesn't

问题 Multiplying two multi-dimensional arrays, say, a 1-dimensional with a 3-dimensional array: [1 2] * reshape(1:8,2,2,2) gives me the error message: LoadError: MethodError: `*` has no method matching *(::Array{Int64,2}, ::Array{Int64,3}) Closest candidates are: *(::Any, ::Any, !Matched::Any, !Matched::Any...) *{TA,TB}(::Union{DenseArray{TA,1},DenseArray{TA,2},SubArray{TA,1,A<:DenseArray{T,N},I<:Tuple{Vararg{Union{Colon,Int64,Range{Int64}}}},LD},SubArray{TA,2,A<:DenseArray{T,N},I<:Tuple{Vararg

问题 I have access to a number of matrix libraries, but for this project I am using Eigen, due to its compile time definition and its inclusion of SVD. Now, I am doing the following operation: Eigen::Matrix<double,M,N> A; // populated in the code Eigen::Matrix<double,N,N> B = A.transpose() * A; As I understand, this makes a copy of A and forms the transpose, which is multiplied by A again. This operation is being performed on relatively small matrices (M=20-30,N=3), but many millions of times per

问题 I have a dataset of a thousand 128 dimensional features in the shape of e.g. (1000,128). I want to find the sorted nearest neighbors of a 128 dimensional feature in the shape of (128,1). The distance in calculated via a Matrix Multiplication between dataset (1000,128) and feature (128,1) which would give an array of similarities in the shape of (1000,1) : DATASET (1000,128) x FEATURE (128,1) = SIMILARITIES (1000,1) This is done via: # features.shape=(1000,128) ; feature.shape=(128,1) ;

问题 This question already has an answer here : Unable to execute device kernel in CUDA (1 answer) Closed 4 years ago . What I am attempting to do is Multiply Matrix A & Matrix B and then from the product matrix I get the index of the maximum value per column. But unfortunately, only the first 128*128 values of the matrix multiplication are correct while others are just garbage. I do not quite understand how this works. I request you to kindly guide me with this .. #include<stdio.h> #include "cuda