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From: kot23 <kot23_at_...97...>

Date: Tue, 17 Jun 2008 22:55:06 +0300

Date: Tue, 17 Jun 2008 22:55:06 +0300

I beleive that scheduling is an NP-complete problem, and for such problems it should be obvious from the beginning that complete search is out of the question: it would take ages. What you need to do is find which (incomplete) search stragery is better suited for your problem. I just finished my dissertation (CLP for timetabling) and spent many hours testing the various heuristics available from ic:search/6. So I can assure you that your points (1,2) are correct: variable selection and value ordering are both important. About your third point: I think that the ordering of constraints is not really important, at least in most situations (i.e. when you have not tampered with the ic_kernel itself, priorities, etc). During the search procedure the constraint propagation takes care of selecting which constraints are applicable at each state. The order they were posted is not relevant: they all go into a constraint pool. Perhaps you are using redundant constraints that increase the time needed by the solver to process them. By all means you should use search/6 and leave labeling/1 for simpler tasks. In your case you would probably be needing smaller start times, so indomain_min might be a good choice for value ordering, as it starts by giving each variable the smaller value from its domain. You are also correct about the bb_min/3, you might lose some solutions in favor of finding one faster. >From my own experience, adding a lot of precedence constraints really affects the time required for good solutions. It is a delicate balance between speed and quality of solution. I don't know if there are any rules of thumb here, but what I understood for sure is that the modeling of the problem is also very important. Try to keep the values of the variables quantitive: i.e. smaller (or larger) values would mean something to the problem. In this way you could use value ordering heuristics like indomain_min (or _max) and enjoy a really fast solution. For variable selection, I found "most_constrained" and "first_fail" to be the most efficient. They label in such a way that the search tree does not get very wide. This results in reaching a solution faster (fewer backtracks). For value ordering, it really depends on the problem. indomain_min would be suitable in your case I beleive, since smaller values mean smaller start times, and better costs. Finally for the search strategy: If your heuristics are suitable and efficient, good solutions will be found near the left-hand of the search tree. In this case, i would avoid credit-based search because its searching moves quickly towards the middle and right-hand side of the tree (i.e. away from the good solutions). bbs stays to the left-hand side, but who knows how many backtracks would be required for a good solution? So, what i used myself is a combination of dbs and lds: search is limited to the left-side (where the good solutions are found) and many alternatives are considered while searching for better solutions. I might sound too general in my response but the topics you are concerned with are the major points of CLP! I am sure that other responses will follow, more detailed and elaborate, but I tried to give you some insight from my own experiences with ECLiPSe. Good Luck with it! Christoforidis Nikos On Tue, 17 Jun 2008 17:43:43 +0200 Soheil Samii <sohsa_at_...107...> wrote: > Hi! > > I have some general questions related to optimization with ECLiPSe (or CLP in > general). The problem that I try to solve with ECLiPSe is a task scheduling > problem for multiprocessor system with a cost function that is a sum of a > linear function in the optimization variables and the square-root of a > quadratic function. > > The CLP code is basically organized as follows: > * Declaring the domains of the variables (integer intervals). > * Linear constraints (precedence constraints, etc.). > * Resource constraints (using cumulative/4, which have three different > versions in the libraries ic_cumulative, ic_edge_finder, and > ic_edge_finder3). > * Declaring the cost function 'Cost'. > * Minimization using bb_min/3 in the library branch_and_bound, i.e., > bb_min(labeling(StartTimes), Cost, Options). > > For a reasonably average-size problem (40 variables) it takes incredibly long > time to solve (after many hours it still hasn't found the optimum). I guess > that it is because I use complete search and because I use the labeling/1 > predicate for the variable and value ordering. Is this true? > > In my case, I want to use ECLiPSe as a heuristic to find a suboptimal > schedule, because the ECLiPSe solver will be used inside another optimization > loop. Therefore, I want the CLP solving process to be fast, and I have been > reading and thinking about different ways to use ECLiPSe as a heuristic. > These are the things that I have thought of: > > 1. The variable ordering is important. > 2. The value ordering is important (as we use _incomplete_ search). > These two issues can be solved by using search/6 instead of labeling/1 above. > There you can choose different orderings and different types of incomplete > search. > > 3. The order of the constraints is important. > > 4. bb_min/3 has options that, for example, lets the branch-and-bound cut away > some solutions from the search tree. For example, there is an > option 'factor': if 'factor'=0.9 it means that when the search engine tries > to find a better solution than the current one, it tries to find a 10% better > solution; thus, the obtained solution is guaranteed to be at most 10% from > the optimum. > > What are your experiences with heuristic optimization in ECLiPSe? Which of the > abovementioned points affect the runtime the most, and is any of them not > important for the runtime? Are there any other issues that I have forgot to > look at to obtain an efficient heuristic with ECLiPSe? > > Regarding the cumulative/4 constraint: Is it better to use the version in > ic_cumulative or is it better to use the one in ic_edge_finder3? Which one is > best from the point of view of runtime? > > Is it in general good for the performance to add constraints? > > How much does the cost function affect the performance? I'm a little bit > worried that my cost function (which I described in the beginning of this > email) makes the runtime to grow, but I don't know how to check that. > > Has anyone used ECLiPSe for heuristic optimization to obtain results within > fractions of a second, or is it totally unrealistic to achieve this? > > Thankful for comments, > > Regards, > /Soheil > > ------------------------------------------------------------------------- > Check out the new SourceForge.net Marketplace. > It's the best place to buy or sell services for > just about anything Open Source. > http://sourceforge.net/services/buy/index.php > _______________________________________________ > ECLiPSe-CLP-Users mailing list > ECLiPSe-CLP-Users_at_lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/eclipse-clp-users > -- kot23_at_...97...Received on Tue Jun 17 2008 - 12:54:47 CEST

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