![]() rrandn (100,1) yreshape (r,10,10) In your case, try : Theme. The matrix analysis functions det, rcond, hess, and expm also show significant increase in speed on large double-precision arrays. This is not conversion but you are adding new elements to the original vector : If you have a vector of size Mx1 you can convert it to matrix using function reshape to get a matrix of size PxN such MPN. 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. ![]() I have done logical indexing and therefore I have a column vector (668557x1). The operation is not memory-bound processing time is not dominated by memory access time. Learn more about logical indexing, matrix, geoshow Hello everyone, I am trying to create a map from below code. This is an especially rich set of functions as MAT- LAB was originally. For example, most functions speed up only when the array contains several thousand elements or more. matfun These are various matrix functions. 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. This can be done rowwise indirectly by transpose or reshape. A magic (3) A 8 1 6 3 5 7 4 9 2 columnvector A (:) columnvector 8 3 4 1 5 9 6 7 2. In Matlab by default convert a matrix to column vector is done column-wise. For example, if A (3,2) is 1+2i and B A., then the element B (2,3) is also 1+2i. does not affect the sign of the imaginary parts. ![]() These sections must be able to execute with little communication between processes. convert a matrix to column vector rowwise. returns the nonconjugate transpose of A, that is, interchanges the row and column index for each element. The function performs operations that easily partition into sections that execute concurrently. ![]()
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