Hi Julians, I am trying to apply svd on a very large sparse matrix using svds. When warming up svds function, I used a very small random vector, and the number of singular values (nsv) is set to one. However, this simple set up results in a considerable high cost of time, and as much as 120MB memory is occupied. To me, it doesn't make sense, and one can reproduce it by:
julia> @time svds(sprand(3,3,0.1), nsv = 1) > > elapsed time: 2.640233094 seconds (117 MB allocated, 1.06% gc time in 5 >> pauses with 0 full sweep) > > Regards, Todd
