The matrix analysis functions det, rcond, hess, and expm also show significant increase in speed on large double-precision arrays. 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. The operation is not memory-bound processing time is not dominated by memory access time. 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. Hi, Order vector 1:first dim(row) 2:second dim(col) 3:third dim(Z) so if you want to transpose rows and columns keeping the same Z, your vector would be 2 1 3 Sign in to comment. They should require few sequential operations. 2 Answers Sorted by: 1 There are several ways: Use reshape: B reshape (A.',1, ) Use vec2mat from the Communications Toolbox: B vec2mat (A,numel (A)) Transpose A and then use linear indexing: A A.' B A (:). Hi, I have to convert a matrix in one column/row vector composed of all the rows of the original matrix. Learn more about matlab programming MATLAB. output vector/matrix transpose (input vector/matrix) The function takes an input vector or a matrix as an argument and returns the transpose. Convert matrix in single column/row vector. These sections must be able to execute with little communication between processes. The MATLAB has a built-in transpose function which when applied on any vector or matrix, it directly computes and returns the transpose of a matrix. The function performs operations that easily partition into sections that execute concurrently. returns the nonconjugate transpose of A, that is, interchanges the row and column index for each element.
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