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 matrix analysis functions det, rcond, hess, and expm also show significant increase in speed on large double-precision arrays. I am not sure if I understood exactly what you are asking. skr This is a general solution, and you dont need to specify anything.bsxfun automatically replicates the smaller matrix (in our case x) along all non-singelton dimensions of the larger matrix (in our case A).So if x is a row vector, it will automatically be replicated along the first and the third dimension. Matrix multiplication is not universally commutative for nonscalar inputs. 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). For nonscalar A and B, the number of columns of A must equal the number of rows of B. As a general rule, complicated functions speed up more than simple functions. The function calculates the dot product of corresponding vectors along the first array dimension whose size does not equal 1. In this case, the dot function treats A and B as collections of vectors. If A and B are matrices or multidimensional arrays, then they must have the same size. The operation is not memory-bound processing time is not dominated by memory access time. If A and B are vectors, then they must have the same length. For example, most functions speed up only when the array contains several thousand elements or more. 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. They should require few sequential operations. Product, returned as a scalar, vector, or matrix. These sections must be able to execute with little communication between processes. The function performs operations that easily partition into sections that execute concurrently.
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