The matrix analysis functions det, rcond, hess, and expm also show significant increase in speed on large double-precision arrays. returns the nonconjugate transpose of A, that is, interchanges the row and column index for each element. 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. 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 (:). 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. Choose a web site to get translated content where available and see local events and offers. They should require few sequential operations. The Transpose block computes the transpose of an M-by-N matrix. Matrix operations follow the rules of linear algebra. You can use these arithmetic operations to perform numeric computations, for example, adding two numbers, raising the elements of an array to a given power, or multiplying two matrices.

These sections must be able to execute with little communication between processes. MATLAB has two different types of arithmetic operations: array operations and matrix operations. The function performs operations that easily partition into sections that execute concurrently.

Matlab transpose matrix to vector