Yeah we changed the example, so best to take it from the one in the release version...
I removed the dictionary from search() but its now no longer solving all the problems(!) - does the algorithm rely somehow on the way the dictionary is constructed? On Tuesday, July 1, 2014 6:59:02 PM UTC-4, andy hayden wrote: > > I was using Cbc. > > SolveModel is a copy and paste job from JuMP (from the last release rather > than master) so may not work with JuMP from master - I couldn't get the > version from master working since it was incompatible with the JuMP release > I had! It'd be great to just be able to just include the file, but I > couldn't get that working so I just pasted it in (I should probably clean > Bench as I made quite a mess, apologies about that.)... so it may be you > need to update SolveModel from JuMP master/your version of JuMP to get > Bench working. > > It's amazing how some small tweaks like this go so far, there's a few > other bits that are obvious even to me (but I just couldn't get working). > > > On 1 July 2014 15:47, Iain Dunning <[email protected] <javascript:>> > wrote: > >> JuMP won't be getting any faster, its entirely limited by the speed of >> the MIP solver. Which one did you use? >> >> >> On Tuesday, July 1, 2014 6:47:04 PM UTC-4, Iain Dunning wrote: >>> >>> I was unable to run Bench.jl (ERROR: varzm! not defined), but, on my >>> computer just using runtests.jl, a fixed seed, and total time for 100 random >>> >>> *Initial >>> elapsed time: 1.641434988 seconds (282491732 bytes allocated, 5.99% gc >>> time) >>> >>> *Change globals to const >>> elapsed time: 1.563094028 seconds (261818132 bytes allocated, 6.61% gc >>> time) >>> >>> * Changing from using a Dict{Int64, *} for the Grid types to just a >>> Vector{*}, as well as those other globals >>> elapsed time: 1.373703078 seconds (191864592 bytes allocated, 4.91% gc >>> time) >>> >>> >>> >>> >>> >>> >>> >>> >>> On Tuesday, July 1, 2014 6:27:15 PM UTC-4, andy hayden wrote: >>>> >>>> Bench.jl has a bench_compare method which returns a DataFrame of times >>>> (I then divide the median of Python vs Julia columns), I'll add this >>>> output >>>> to the Bench script as it's useful to see (would be nice to add more >>>> stats, >>>> as it's just a DataFrame of all the solved puzzles in seconds). By default >>>> it runs a hundred random sudoku's on Julia, Python, and JuMP (the same on >>>> each)... >>>> >>>> Thanks Steven: Making those const makes a huge difference, Julia wins >>>> (from 20% slower to 10% faster for me with just that change). >>>> I will have a play and see how your other suggestions play out. >>>> >>>> I was also very impressed with JuMP here... and it may be the latest is >>>> even faster (I'm using the version from the last release rather than >>>> master, and it has changed since then). >>>> >>>> >>>> On Tuesday, 1 July 2014 15:11:27 UTC-7, Iain Dunning wrote: >>>>> >>>>> I'm working on improving this, but I'm not sure how you are measuring >>>>> that 20% slower - can you be more specific? >>>>> >>>>> On Tuesday, July 1, 2014 1:37:00 PM UTC-4, andy hayden wrote: >>>>>> >>>>>> I recently ported Norvig's Solve Every Sudoku Puzzle >>>>>> <http://norvig.com/sudoku.html> to Julia: https://github.com/ >>>>>> hayd/Sudoku.jl >>>>>> >>>>>> Some simple benchmarks suggest my Julia implementation solves around >>>>>> 20% slower* than the Python version, and 3 times faster than the >>>>>> implementation on JuMP (vendorized from the latest release), against the >>>>>> random puzzles. I tried to include the solver from >>>>>> attractivechaos/plb >>>>>> <https://github.com/attractivechaos/plb/tree/master/sudoku> but >>>>>> couldn't get it working for comparison... >>>>>> >>>>>> I'm new to Julia so would love to hear people's thoughts / any >>>>>> performance tips! >>>>>> I've not delved too deeply into the Profile, but @time suggests 10% >>>>>> of time is GC. >>>>>> >>>>>> **I'm sure I've lost some performance in translation which could be >>>>>> easily sped up...* >>>>>> >>>>>> Best, >>>>>> Andy >>>>>> >>>>> >
