![]() ![]() Example: t 1 2 A 1 2 3 4 > Res 11 12 23 24 So far I have tried 4 versions: Theme. I need to multiply t with each column of A element-wise. The result is a 4-by-4 matrix, also called the outer product of the vectors. Alternatively, you can calculate the dot product A B with the syntax dot (A,B). The matrix analysis functions det, rcond, hess, and expm also show significant increase in speed on large double-precision arrays. Hey there, I have a vector t (nx1) and a matrix A (nxm). The result is a 1-by-1 scalar, also called the dot product or inner product of the vectors A and B. The matrix multiply (X*Y) and matrix power (X^p) operators show significant increase in speed on large double-precision arrays (on order of 10,000 elements). As a general rule, complicated functions speed up more than simple functions. row/column of a square matrix A has all zero entries or is the same as another row/column multiplied by a real number, then det (A) 0. The operation is not memory-bound processing time is not dominated by memory access time. Matrix multiplication is not universally commutative for nonscalar inputs. For example, most functions speed up only when the array contains several thousand elements or more. For nonscalar A and B, the number of columns of A must equal the number of rows of B. For example: Here we multiply a 5 by 4 matrix with 3 vectors, each is 4 by 1. But if you mean you have a matrix of vectors, and you want to multiply another matrix by each one of these vectors then one way is to use arrayfun. The data size is large enough so that any advantages of concurrent execution outweigh the time required to partition the data and manage separate execution threads. I am not sure if I understood exactly what you are asking. If you did not store the matrix as sparse, then no, MATLAB cannot possibly bother to look for zero elements inside every matrix multiply. MATLAB IS using an efficient algorithm, as long as your matrix is stored in sparse form. They should require few sequential operations. So the product of a sparse and a full matrix is also faster than the product of two full matrices. ![]() This must be done because the multiplication between the vector and the 3D array is performed along the 3rd dimension, so Matlab need a hint on sizes. ![]() Example: A 1 1 4 3 2 2 2 1 1 4 1 1 Expected output: C 4 12 2 4 Any ideas without for Stack Overflow. twodim twodim + squeeze (sum (bsxfun (times, threedim, reshape (onedim, 1 1 glength)),3)) Here is how it works: reshape makes the vector onedim looking like a 3D array. Instead of using 'for' loop which takes so much time, h. How can I multiply columns of a matrix and obtain a column vector. These sections must be able to execute with little communication between processes. Dear All, I have a simple 33 matrix(A) and large number of 31 vectors(v) that I want to find Av multiplication for all of the v vectors. The function performs operations that easily partition into sections that execute concurrently. ![]()
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