/barrelfish-master/usr/eclipseclp/ecrc_solvers/chr/ |
H A D | data.pl | 19 % No solution for 20/3 with (1) but a solution for 20/3 with (5)
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/barrelfish-master/usr/eclipseclp/Kernel/lib/ |
H A D | branch_and_bound.pl | 39 that instantiates a cost variable when a solution is found. The cost 53 Then we can find a minimum-calorie solution as follows: 56 Found a solution with cost 500 57 Found a solution with cost 400 58 Found a solution with cost 200 59 Found no solution with cost -1.0Inf .. 199 71 the minimum-cost solution with Cal=200. 76 Found a solution with cost 1.5 77 Found a solution with cost 0.99 78 Found no solution wit [all...] |
H A D | fd.pl | 104 of the form VarA/VarB must have an integer value in the solution. 464 % Min-Max: Branch & Bound by restarting each time a new solution is found. 497 % the s/1 wrapper makes it fail safely if no solution 507 xget(Index, 1, Solution), % fail here if no solution 527 % In case the solution still contains variables, 585 % the s/1 wrapper makes it fail safely if no solution 600 xget(Index, 1, Solution), % fail here if no solution 2190 whenever a better solution is found, but this tightening does not 2214 summary:"Find the solution of Goal that minimizes the maximum of elements of C. 2218 desc:html(" If C is a linear term, a solution o [all...] |
H A D | test_util.pl | 161 first solution of Goal is committed to, and CheckGoal executed with 162 the variable instantiations of this solution. 170 ALL solutions to Goal will be generated. For each solution, SolutionCheck 171 will be executed with the variable instantiations of this solution, and 172 with SolCountVar instantiated to the number of this solution (starting 488 fail % next solution
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/barrelfish-master/usr/eclipseclp/icparc_solvers/ |
H A D | ic_probing_tests.pl | 39 Found a solution with cost 9 60 Found a solution with cost 9 70 Found a solution with cost 9 79 Found a solution with cost 4 89 Found a solution with cost 4 121 Found a solution with cost 12 122 Found a solution with cost 11 123 Found a solution with cost 9 130 Found a solution with cost 8 145 Found a solution wit [all...] |
H A D | probing_tests.pl | 38 Found a solution with cost 9 59 Found a solution with cost 9 69 Found a solution with cost 9 78 Found a solution with cost 4 88 Found a solution with cost 4 119 Found a solution with cost 12 120 Found a solution with cost 11 121 Found a solution with cost 9 128 Found a solution with cost 8 143 Found a solution wit [all...] |
H A D | ic_probe.pl | 156 eplex:lp_var_get(Handle, Var, solution, Sol0), 189 (lp_var_get(Handle, X, solution, Sol) -> 199 true % no solution yet 210 whose optimal solution is assigned to the problem variables as tentative
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H A D | probe.pl | 155 eplex:lp_var_get(Handle, Var, solution, Sol0), 187 (lp_var_get(Handle, X, solution, Sol) -> 198 true % no solution yet 209 whose optimal solution is assigned to the problem variables as tentative
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/barrelfish-master/usr/eclipseclp/Eplex/ |
H A D | eplex.h | 304 _get_xpress_sol(lp, solution) 306 void *solution; variable 308 struct lp_sol *sol = (struct lp_sol *) solution; 310 printf("Getting solution....\n"); 312 printf("Gotten solution....\n");
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/barrelfish-master/usr/eclipseclp/documents/tutorial/ |
H A D | eplex.tex | 296 In this case, there is an optimal solution of 710.0: 305 instantiated by the solver. However, the `solution' values, i.e.\ the 309 solution value for the variable (e.g., \verb'A2' has the solution value of 315 One reason the problem variables are not assigned their solution values is 320 \subsection{Getting more solution information from the solver} 322 The solution values of the problem variables can be obtained by 324 previous section can be modified to return the solution values: 332 % f. set the problem variables to their solution values 338 In line \verb'f', \verb'eplex_var_get/3' is used to obtain the solution [all...] |
H A D | search.tex | 48 the solution to the problem. Also, there may be multiple solutions 56 \item A total assignment is a {\em solution} if it satisfies all the 89 This is necessary when the optimal solution is needed (one has to prove 90 that no better solution exists). Incomplete search may be sufficient when 91 just some solution or a relatively good solution is needed. 181 not just interested in some solution or all solutions, but in 182 the best solution. 184 Fortunately, there is a general method to find the optimal solution 189 \item Find a first solution [all...] |
H A D | colgen.tex | 29 colgen} library by comparing the solution of a simple 44 solution can begin this approach may be impractical. We could instead 50 optimal solution, we do have an upper bound on this number 168 The cutting stock problem can be decomposed into a master problem in which an optimum combination of existing cuttings is found and a subproblem in which new cuttings are generated which could improve upon the current combination. For clarity we denote by $Q$ the set of feasible cuttings and index variables $\lambda_{\mathbf{q}}$ by the column of master problem constraint coefficients $\mathbf{q}\in Q$ corresponding to the equivalent subproblem solution: 230 where we first create a {\tt colgen} instance {\tt cut\_stock}, set up the variable domains of the subproblem and the demand constraints of the master problem, set up the initial master problem bound constraints and subproblem knapsack constraint, then solve and return the variables with non-zero values in the optimal solution. The definition of cutting cost as waste has been combined with the knapsack constraint, while the bounds placed on this cost exclude cuttings with sufficient waste to produce further boards, thus limiting the amount of search in subproblem solution. The chosen method of subproblem solution is: 255 % create solution structure and post to problem instance
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H A D | repairtut.tex | 44 but subsequently external changes to the problem render the solution 216 \item \verb0tent_init0 to find an initial solution 300 The optimal solution is obtained simply by posting the temporal 356 \item Set the tentative values to an optimal solution (of this 613 track of the best solution encountered. 620 \item Checking whether the move lead to a solution and whether this 621 solution is better than the best one so far. 628 and the current tentative values form a solution to the problem. 639 ( conflict_constraints(cap,[]) -> % it's a solution! 644 printf("Found solution wit [all...] |
/barrelfish-master/usr/eclipseclp/documents/search/ |
H A D | search.tex | 78 the solution to the problem. Also, there may be multiple solutions 85 \item A total assignment is a {\em solution} if it satisfies all the 115 This is necessary when the optimal solution is needed (one has to prove 116 that no better solution exists). Incomplete search may be sufficient when 117 just some solution or a relatively good solution is needed. 201 not just interested in some solution or all solutions, but in 202 the best solution. 204 Fortunately, there is a general method to find the optimal solution 209 \item Find a first solution [all...] |
/barrelfish-master/usr/eclipseclp/documents/libman/ |
H A D | eplex.tex | 91 %solution information. 214 Restrict the external solver to assign solution values for the eplex 276 optimal solution to the eplex problem, then the predicate succeeds and Cost is 278 solution), then the predicate fails (by default). If the problem is 280 succeeds without producing any solution values for the variables. 370 instance for the variable {\tt Var}. If {\tt What} is {\tt solution} or 372 solver state to obtain the optimal solution is returned in {\tt 373 Value}. {\tt solution} returns the value as a float, and {\tt 468 the solver returns an optimal solution, it may actually not be the exact 470 will terminate when the solution foun [all...] |
H A D | extfd.tex | 113 an integer value in the solution. 232 a solution of the goal {\it Goal} is found that minimises the 234 If {\it C} is a list of linear terms, the returned solution 236 The solution is found using the {\it branch and bound} method; 237 as soon as a partial solution is found that is worse than a previously 238 found solution, failure is forced and a new solution is searched for. 239 When a new better solution is found, the bound is updated and 241 Each time a new better solution is found, the event 280 is raised. 242 If a solution doe [all...] |
H A D | repair.tex | 45 applied until a correct solution is found. 91 or initially inconsistent solution. These values may be changed 269 To obtain a tentative assignment which is a solution to the given problem, 275 When all conflict sets are empty, a solution is found. 283 Note that all these variables must be reassigned in any solution 310 to get a repaired solution. 432 [X,Y,Z] tent_get [NewX,NewY,NewZ], % get repaired solution 489 true % a solution is found. 496 solution has been found and a set of tenable variables are still 508 the solution i [all...] |
/barrelfish-master/usr/eclipseclp/ecrc_solvers/ |
H A D | chr_doc.pl | 95 no (more) solution. 118 no (more) solution. 129 no (more) solution. % or/3 - constraint fails 189 no (more) solution. 232 no (more) solution. 235 no (more) solution. 243 no (more) solution. 438 no (more) solution.
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H A D | propia.pl | 72 solution (or the first solution of Goal subsumes all the 75 solution. 91 fail_if:"Fails if Goal has no solution.", 116 yes. % Prolog + infers: one solution 137 no (more) solution. 275 % simply the generalisation of the first solution. Normally the new 276 % msg is constructed from the previous msg and the new solution. 277 % However on initialisation there is only a new solution, so this is 297 % extend first finds a solution t [all...] |
/barrelfish-master/usr/eclipseclp/icparc_solvers/ech/ |
H A D | diaz_bool.pl | 46 /* The solution is a list [ [Int11,Int12,Int13],..., [IntN1,IntN2,IntN3] ] */ 54 /* [1,0,0],[0,0,1],[0,1,0],[0,1,0],[0,0,1]] (first solution) */ 124 /* The solution is a list [ [Pig11,...,Pig1m], ... ,[Pign1,...,Pignm] ] */ 178 /* The solution is a list [ [Que11,...,Que1N], ... ,[QueN1,...,QueNN] ] */ 187 /* N=8 [[0,0,0,0,0,0,0,1], (first solution) */
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/barrelfish-master/usr/eclipseclp/Opium/demo/ |
H A D | digin.pl | 28 % 1) solution of the book and some tests.
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/barrelfish-master/usr/eclipseclp/Contrib/ |
H A D | arrays.pl | 34 solution (with cost O(lgN) rather O(1)) see Trees.Pl.
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/barrelfish-master/doc/015-disk-driver-arch/ |
H A D | conclusion.tex | 30 prefetcher of some sort is necessary. A possible solution would be to have a
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/barrelfish-master/usr/eclipseclp/icparc_solvers/ilog/ |
H A D | fd_min_max.pl | 113 % Min-Max: Branch & Bound by restarting each time a new solution is found. 145 % the s/1 wrapper makes it fail safely if no solution 157 arr_get(Index, 1, Solution), % fail here if no solution 176 % In case the solution still contains variables, 231 % the s/1 wrapper makes it fail safely if no solution 248 arr_get(Index, 1, Solution), % fail here if no solution 417 printf("Found a solution with cost %d\n%b", Cost).
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/barrelfish-master/usr/eclipseclp/documents/intro_paper/ |
H A D | eclipse.tex | 86 locally repairing an original solution, and repeating the process 481 Naturally the implemented solution to an industrial problem must be 483 It is sometimes argued that this is only possible if the solution is 504 the implemented solution has proved not to solve the actual industrial 507 The solution cannot then be modified to meet the actual, or new, 765 the best feasible solution to the problem is one which minimises the 881 and 99 is eight coins! One solution is: 939 solution. 1120 * Found a solution with cost 6 1242 * no (more) solution [all...] |