![]() I’d guess that either some versions are being vectorized (SIMD) more efficiently, or it has to do with memory accesses and caching. The process of revising loop-based, scalar-oriented code to use MATLAB matrix and vector operations is called vectorization. The orientation of a two-dimensional vector is its status as either a row vector or column vector. There’s quite a difference still in performance between the three versions IMO (the fastest being twice as fast as the slowest), I haven’t looked into what’s causing that. n norm (X,p) returns the p -norm of matrix X, where p is 1, 2, or Inf: If p 1, then n is the maximum. n norm (X) returns the 2-norm or maximum singular value of matrix X, which is approximately max (svd (X)). n norm (v,p) returns the generalized vector p -norm. The values of that satisfy the equation are the eigenvalues. The eigenvalue problem is to determine the solution to the equation Av v, where A is an n-by-n matrix, v is a column vector of length n, and is a scalar. The result is a 4-by-4 matrix, also called the outer product of the vectors. This norm is also called the 2-norm, vector magnitude, or Euclidean length. V,D,W eig(A) also returns full matrix W whose columns are the corresponding left eigenvectors, so that WA DW. Matrix operations follow the rules of linear algebra. You can use these arithmetic operations to perform numeric computations, for example, adding two numbers, raising the elements of an array to a given power, or multiplying two matrices. Alternatively, you can calculate the dot product A B with the syntax dot (A,B). MATLAB has two different types of arithmetic operations: array operations and matrix operations. ![]() MATLAB offers a variety of other symbols and line types. To create a GPU array with underlying type datatype, specify the underlying type as an additional argument before typename. Here is an example using stars to mark the points. ![]() Julia> vec_vec_vec = Array(undef, L) Īrr = rand(L, M, for i = 1:L, j = 1:M, k = 1:N vec_vec_vec += 1 for i = 1:L, k = 1:N, j = 1:M vec_mat += 1 for k = 1:N, j = 1:M, i = 1:L arr += 1 end The result is a 1-by-1 scalar, also called the dot product or inner product of the vectors A and B. You can specify typename as gpuArray.If you specify typename as gpuArray, the default underlying type of the array is double. For performance comparison, I wrote this small test: julia> L = M = N = Int(5e2) Use vecnorm to treat a matrix or array as a collection of vectors and calculate the norm along a specified dimension. 2 Lien Traduire Modifié (e) : Stephen23 le Just like in mathematics, the only difference is their size. Matrix multiplication involves the action of multiplying each row vector of one matrix by each column vector of another matrix.I have to deal with 3 dimensional structures, I was hesitating between vectors of vectors of vectors, vectors of matrices or tridimensional arrays.
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