math,matrix,big-o,time-complexity,matrix-decomposition

That statement considers the overall complexity of Cholesky decomposition including (an implementation of) inverse square root, and is what is left of a section that detailed the whole algorithm on a DSP. I do see now that out of context the statement is misleading. So, to compute the term within...

c,stack-smash,matrix-decomposition

Your original Matlab code was (Matlab has 1-based indexing): for k = 1:n - 1 rows = k + 1:n A(rows, k) = A(rows, k) / A(k, k) A(rows, rows) = A(rows, rows) - A(rows, k) * A(k, rows) end What rows = k + 1:n this does is that...

python,lambda,max,xrange,matrix-decomposition

First, isn't the line for ID simply the identity matrix? Yes. Second, I can't really understand the line for row.... See this for a discussion about the max/key/lambda interaction. To answer "what is i?", its the argument to the lambda function, i could equivalently be x for foo. (For...

r,svd,matrix-decomposition,function

Cannot you just wrap your matrix arithmetic in a small function of your own? recover_matrix_from_svd <- function(svd) { score <- 0 for(i in 1:ncol(svd$u)) { score <- score + svd$u[,i] %*% t(svd$v[,i]) * svd$d[i] } score } alternatively, the diag function is very useful for this. Using it results in...