Lines Matching defs:with
6 % in compliance with the License. You may obtain a copy of the License
73 (in this context, we only consider domains with a finite number of values).
89 with the problem size:
107 Figure \ref{figsearchspace} shows a search space with N (here 16)
126 Here is table of a selection of search methods together with their properties:
146 partitioning it systematically. This can be done naturally with a
151 completely (with as little as O(N) memory requirements).
187 is only possible with constructive methods which allow to reason about
191 This can only be implemented with
224 Since search space sizes grow exponentially with problem size,
231 This can be done with the help of constraints. This is often referred
250 \section{Complete Tree Search with Heuristics}
261 into play here because we deal with finite search trees.
345 While with 2 variables there are only 2 variable selection strategies,
346 this number grows exponentially with the number of variables. For 5
353 down, variables with small domains should be selected first.
406 We model the problem with one variable per queen, assuming that each queen
436 We start naively, using the pre-defined labeling-predicate that comes with the
455 variables with the smallest domain first. This reduces the branching
467 the first solution is found with only 3 backtracks now.
469 nicely with the naive strategy, but our improvement leads to a
471 This is not uncommmon with heuristics: one has to keep in mind that the
473 results with some problems can be useless or counter-productive with others.
549 performance has become more predictable than with the
609 \item With a perfect heuristic, the first solution is found with zero
620 %\subsection{Pruning with Extra Constraints}
698 starting the search at the tree root with a certain integral amount of
701 be split any further: subtrees provided with only a single credit unit
740 The number of leaf nodes grows exponentially with the number of
749 is not good enough. A possible remedy is to combine credit search with
799 the heuristic. LDS starts searching with a discrepancy of $0$ (which
801 a solution with a given discrepancy, the discrepancy is increased and
805 to apply LDS with small discrepancies. Subsequently, if no solution
814 This reference also suggests that combining LDS with Bounded Backtrack
816 module also supports BBS and its combination with LDS.
837 {\eclipse} installation. You can copy this file and load it with
900 cope with this we use the {\eclipse} ``tentative value'' facility in
902 The heuristic is stored with the variable as its tentative value.
1013 well with the idea of constraint propagation.
1033 a given capacity and a set of items with given weights and profit
1083 In the example, the single capacity constraint has been annotated with
1129 are even simpler and use only a single loop with a single termination
1174 printf("Found solution with profit %w%n", [CurrentProfit]),
1188 The {\tt change_random} predicate changes a randomly selected variable with
1227 printf("Found solution with profit %w%n",
1281 Uphill moves are always accepted, downhill moves with a probability
1282 that decreases with the temperature. The search routine must be invoked
1283 with appropriate start and end temperatures, they should roughly correspond
1304 printf("Found solution with profit %w%n",
1327 search. Moves are selected by an acceptance criterion, with a
1331 added to or removed from the tabu list has to be specified, along with the
1336 the item with the best relative profit into the knapsack, or removing
1355 printf("Found solution with profit %w%n", [CurrentProfit]),
1403 from scratch with an extra `nogood' constraint. This `nogood' constraint
1420 variable (i.e. values which causes no constraint violations with existing
1422 causes the minimum conflict (constraint violation) with the tentative
1476 values to be associated with variables, as specified in the algorithm.
1496 with the tentative values in the unlabelled variables. This is done with
1510 of such constraints using \verb'length/2'. The value with the minimum
1537 \verb'try_one_step/2' tries out one search, with the first argument
1548 The main difficulty with implementing restart is to remember the values of
1557 associated with has also to be remembered. This is done using the
1567 which associates the name \verb'varbindings' with a non-logical value. The
1570 which variable is associated with which value, all the variables being
1665 to select a variable (picking the one with the smallest domain and most