The colon operator used on its own specifies all elements when using linear indexing, and it returns a single column vector with the entire array contents. So the element at row 1 column 2 is, in fact, the fifth element stored. In other words, each column in the array is stored one after another. This is possible because MATLAB arrays are stored column wise in memory. For example, the element at row 1 column 2 can be accessed through one linear index, 5. Sometimes it is convenient to treat two dimensional arrays such as these as a one dimensional array as though all the columns were stacked together into a single column and specify single index. You can delete one or more rows of an array such as rows 1 to 2, all the columns, by assigning them to the empty matrix denoted by square brackets. You can assign values to specific elements by specifying indexing on the left hand side of the equation such as rows 1, columns 2 to the end minus 1 equals 10 10. You can also use the end keyword such as row 1, columns 2 to the end or 2 the end minus 1. You can specify all rows or columns by using the colon operator, in this case specifying all columns. The elements do not have to be contiguous, such as row 1, columns 1 and 3. You can specify a range of rows and columns to access sections of an array such as row 1, columns 1 through 2. Here is the element of A in the first row second column. You can specify elements of an array by simple row and column indexing. Let's now look at how you can access and change the values of array elements with different forms of indexing. It can be more convenient to inspect the contents of an array by opening it into the variable editor. You can also call a number of functions that generate elementary matrices with different contents such as ones, zeros, or random numbers. You can change the rows to columns with the transpose operator. The linspace space function is similar to the colon operator, letting you specify a start and end value but gives control over the number of points such as 7. You can create equally spaced one dimensional arrays with a column operator such as A equals 1 to 10, A equals 1 to 10 in steps of 2, or A equals 10 to 1 in steps of negative 2. You can create an array by specifying specific values using square brackets and commas or spaces to separate columns in a row such as A equals 1, 2, 3, 4 and semicolons to separate rows. With the MATLAB language, you can create arrays, access and assign values to array elements using a number of indexing methods, and perform many other operations to manipulate the array's contents. So working with arrays is fundamental to working with MATLAB. This includes not only numeric data, but data of other types such as strings or even complex objects. I’d guess that either some versions are being vectorized (SIMD) more efficiently, or it has to do with memory accesses and caching.MATLAB stores all types of data in arrays. There’s quite a difference still in performance between the three versions IMO (the fastest being twice as fast as the slowest), I haven’t looked into what’s causing that. For performance comparison, I wrote this small test: julia> L = M = N = Int(5e2) I have to deal with 3 dimensional structures, I was hesitating between vectors of vectors of vectors, vectors of matrices or tridimensional arrays.
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