![]() All of the efficiencies described in this paper are implemented in the Tensor Toolbox for MATLAB. ![]() ![]() Otherwise, linsolve returns the rank of A. If the mathematics are not important and any procedure will do, the easiest approach would likely be something like: Theme. If t is a datetime or duration array having m elements, then datevec returns an m -by-6 matrix where each row corresponds to a value in t. X,R linsolve (A,B) also returns the reciprocal of the condition number of A if A is a square matrix. DateVector datevec (t) converts the datetime or duration value t to a date vector that is, a numeric vector whose six elements represent the year, month, day, hour, minute, and second components of t. We are interested in the case where the storage of the components is less than the storage of the full tensor, and we demonstrate that many elementary operations can be computed using only the components. X linsolve (A,B) solves the matrix equation AX B, where A is a symbolic matrix and B is a symbolic column vector. A matrix is a two-dimensional, rectangular array of data elements arranged in rows and columns. We consider two specific types: a Tucker tensor can be more » expressed as the product of a core tensor (which itself may be dense, sparse, or factored) and a matrix along each mode, and a Kruskal tensor can be expressed as the sum of rank-1 tensors. The most basic MATLAB® data structure is the matrix. First, create the scalar t, the square matrix A, and the column vector b. The size of A is 9000x1 and B is 9000x1000. Find the matrix exponential times a vector: e t A b. Second, we study factored tensors, which have the property that they can be assembled from more basic components. Given a matrix A, I need to multiply with another constant vector B, N times (N > 1 million). We propose storing sparse tensors using coordinate format and describe the computational efficiency of this scheme for various mathematical operations, including those typical to tensor decomposition algorithms. That is Cx where xvec (X) Yet I found the last term (XB) is very difficult to vectorize, it would be very sparsy. The code I am using is the following: Final Time T 0. First, we study sparse tensors, which have the property that the vast majority of the elements are zero. Vectorizing matrix multiplication in matlab Ask Question Asked 10 years, 9 months ago Modified 10 years, 9 months ago Viewed 1k times 1 I would like to transform the matrix product AX-XB into vector form. This is the problem of computing the action of the matrix exponential on a vector. I have a problem multiplying a vector times the inverse of a matrix in Matlab. In this paper, the term tensor refers simply to a multidimensional or N-way array, and we consider how specially structured tensors allow for efficient storage and computation.
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