It takes a matrix M that used to have x rows and y columns and turns it into a matrix with a rows and b columns. With a matrix, diag pulls out the diagonal elements and makes a vector out of them. See in the snippet below a successful deletion of the fourth element of a vector, and what happens when I try to delete just one element from a 4x3 matrix.Ī null assignment can have only one non-colon index.ĭiag on a vector creates a matrix whose diagonal is the initial vector and whose other elements are zero. Using empty brackets to delete elements from a matrix works if you are going to delete a whole row or a whole column, but not just one element. Deleting is not the same as assigning zero to the value of that element. Use empty brackets to delete an element from a vector or a row/column from a matrix. To append vectors to a matrix you need to make sure the dimensions work out so that all rows have the same number of elements. If it is not the next consecutive position, MATLAB pads the elements in between with zeros. To append an element to a vector just specify a value at the desired position. M(,) addresses the intersection of rows a and b and columns c through d and e. For example v() addresses elements a, b, and c through d. Use a square bracket to address nonconsecutive elements in a vector or matrix. M(:,a) addresses column a, M(a,:) addresses row a, M(:,a:b) addresses columns a through b, M(a:b,:) addresses rows a through b, M(a:b,c:d) addresses the intersection of rows a through b and columns c through d. For example, v(:) addresses all the elements of a vector, v(a:b) addresses elements a through b in vector v. Use the colon operator to address a range of elements in a vector or matrix. It's just like playing Battleship except both the columns and rows are designated by numbers. Then I ask it for the element in the second row and third column. In the example below I make a 3x3 matrix M. M(1,1) addresses the element in the top left corner of the matrix M. For example, v(1) addresses the first element in a vector v. You can also use that technique to address a specific spot in a matrix. We've already practiced using parentheses to address a certain element of a vector. One easy improvement is to broadcast the first line in your loop to avoid allocating a matrix for (sparseR + reshape(q' * sparseS, 199, 199)) and then another one for 0.5 * 0.05 * (sparseR + reshape(q' * sparseS, 199, 199)): tmp = 0.5. Modifying your code to pre-allocate those matrices may help a lot. In particular, you are constructing new matrices to hold a lot of intermediate quantities. You are seeing a lot of allocations because your code really does allocate a lot of memory. Running your code in a function, I see 3.699408 seconds (41.60 k allocations: 3.787 GiB, 5.39% gc time) which is already quite close to what you reported MATLAB as giving. Instead, put the code you’re timing in a function. When benchmarking code, you will not get accurate results when timing in global scope.semicolons at the end of each line are not necessary.Please quote your code so that it’s easy to read: PSA: how to quote code with backticks.I don’t understand why, but hope it can help future readers and hope someone can explain this.įor the same codes, Matlab takes Elapsed time is 4.509614 seconds. I find that my original vector q has type 799 X 1 Array, the speed is much much faster. # Here is how I'm timing the testFun(sparseM,sparseR,sparseS,q,100,799) The size requirement for the operands is that for each dimension, the arrays must either have the same size or one of them is 1. Tmp = 0.5 * 0.05 * (sparseR + reshape(q' * sparseS, numGrids, numGrids)) Even though A is a 7-by-3 matrix and mean(A) is a 1-by-3 vector, MATLAB implicitly expands the vector as if it had the same size as the matrix, and the operation executes as a normal element-wise minus operation. # I want to optimize this testFun functionįunction testFun(sparseM,sparseR,sparseS,q,numIters,numGrids) SparseS = sparse(rows3,cols3,vals3,numGrids,numGrids*numGrids) SparseR = sparse(rows2,cols2,vals2,numGrids,numGrids) SparseM = sparse(rows1,cols1,vals1,numGrids,numGrids) MATLAB supports (and encourages) vectorized operations on vectors and matrices. Vals3 = zeros(numGrids*numGrids*numGrids) Rows3 = zeros(Int64,numGrids*numGrids*numGrids) Ĭols3 = zeros(Int64,numGrids*numGrids*numGrids) I only care about the for loop part inside the testFun function.) (The creation of the matrices is ugly, but it works.
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