Matlab mutiply vector by 3d matrix3/11/2024 Often I find that I also have a bunch of matrices stored in a 3d-array, say, T. And be sure that your list of row vectors is s by n, in V, where s is the number of matrices and vectors. The size requirement for the operands is that for each dimension, the arrays must either have the same size or one of them is 1. So that S(i:(i+m-1),:) = Mi and so forth. Even though A is a 7-by-3 matrix and mean(A) is a 1-by-3 vector, MATLAB implicitly expands the vector as if it had the same size as the matrix, and the operation executes as a normal element-wise minus operation. Resulting in a list of m-length row vectors:įirst of all, make your vertical stack of matrices S. Here's a code snippet to take a stacked list of m by n matrices, S, and multiply each m by n matrix by a corresponding n-length row vector in a list of row vectors, V. The first page of the 3D Matrix should be equal to the product of the 2D Matrix times the first element of the V. Hey everyone :) I would like to create a 3D Matrix out of a 2D Matrix and a Vector. Learn more about vectorization, multidimensional array, elementwise MATLAB. This is equivalent to a bsxfun capability for matrix multiplication."Element-wise" matrix vector multiplication in matlab Alec Jacobson Februweblog/ or Multiply each matrix in a list of matrices against corresponding vector in list of vectors Elementwise Matrix multiplication with a Vector. If a dimension is singleton then it is virtually expanded to the required size (i.e., equivalent to a repmat operation to get it to a conforming size but without the actual data copy). The first two dimensions must conform using the standard matrix multiply rules taking the transa and transb pre-operations into account, and dimensions 3:end must match exactly or be singleton (equal to 1). Which would be equivalent to the MATLAB m-code: i.e., MTIMESX treats these cases as arrays of 2D matrices and performs the operation on the associated parings. The remaining dimensions are duplicated and specify the number of individual matrix multiplies to perform for the result. Mtimesx('SPEEDOMP','OMP_SET_NUM_THREADS(4)') % sets SPEEDOMP mode with number of threads = 4įor nD cases, the first two dimensions specify the matrix multiply involved. 'N' or 'n' = No pre-operation (the default if trans_ is missing)ĭirective = One of the modes listed above, or other directivesĬ = mtimesx(A,B) % performs the calculation C = A * BĬ = mtimesx(A,'T',B) % performs the calculation C = A.' * BĬ = mtimesx(A,B,'g') % performs the calculation C = A * conj(B)Ĭ = mtimesx(A,'c',B,'C') % performs the calculation C = A' * B' Transb = A character indicating a pre-operation on B: Transa = A character indicating a pre-operation on A: Tensors are very relevant to your question, as they can be represented as multi-dimensional arrays. The resulting map is a map V V R, which can be thought of as an n × n matrix. So if you think of the 3D array as a map from V V V, then you can compose it with the map V R. (If p p happened to be 1, then B B would be an n × 1 n × 1 column vector. An n × 1 matrix can represent a map from V to R. In math terms, we say we can multiply an m × n m × n matrix A A by an n × p n × p matrix B B. Where transa, transb, and directive are the optional inputs: Just like for the matrix-vector product, the product AB A B between matrices A A and B B is defined only if the number of columns in A A equals the number of rows in B B. The general syntax is (arguments in brackets are optional): SPEEDOMP: Fastest BLAS, LOOPS, or LOOPOMP method even if it doesn't match MATLAB exactly SPEED: Fastest BLAS or LOOPS method even if it doesn't match MATLAB exactly MATLAB: Fastest BLAS or LOOPS method that matches MATLAB exactly (default) LOOPSOMP: Always uses OpenMP multi-threaded C loops if available LOOPS: Always uses C loops if available Can meet or beat MATLAB for speed in most cases Can match MATLAB results exactly or approximately as desired Utilizes BLAS calls, custom C loop code, or OpenMP multi-threaded C loop code Supports Transpose, Conjugate Transpose, and Conjugate pre-operations Supports multi-dimensional (nD, n>2) arrays directly MTIMESX is a fast general purpose matrix and scalar multiply routine that has the following features:
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |