static void Main()
{
// Creates the model.
// [START model]
CpModel model = new CpModel();
// [END model]
// Creates the variables.
// [START variables]
int num_vals = 3;
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
// [END variables]
// Adds a different constraint.
// [START constraints]
model.Add(x != y);
// [END constraints]
// Creates a solver and solves the model.
// [START solve]
CpSolver solver = new CpSolver();
VarArraySolutionPrinter cb = new VarArraySolutionPrinter(new IntVar[] { x, y, z });
// Search for all solutions.
solver.StringParameters = "enumerate_all_solutions:true";
// And solve.
solver.Solve(model, cb);
// [END solve]
Console.WriteLine($"Number of solutions found: {cb.SolutionCount()}");
}
static void Main()
{
// Create the CP-SAT model.
CpModel model = new CpModel();
// Declare our primary variable.
IntVar x = model.NewIntVar(0, 20, "x");
// Create the expression variable and implement the step function
// Note it is not defined for var == 2.
//
// - 3
// -- -- --------- 2
// 1
// -- --- 0
// 0 ================ 20
//
IntVar expr = model.NewIntVar(0, 3, "expr");
// expr == 0 on [5, 6] U [8, 10]
ILiteral b0 = model.NewBoolVar("b0");
model.AddLinearExpressionInDomain(x, Domain.FromValues(new long[] { 5, 6, 8, 9, 10 }))
.OnlyEnforceIf(b0);
model.Add(expr == 0).OnlyEnforceIf(b0);
// expr == 2 on [0, 1] U [3, 4] U [11, 20]
ILiteral b2 = model.NewBoolVar("b2");
model
.AddLinearExpressionInDomain(
x, Domain.FromIntervals(new long[][] { new long[] { 0, 1 }, new long[] { 3, 4 },
new long[] { 11, 20 } }))
.OnlyEnforceIf(b2);
model.Add(expr == 2).OnlyEnforceIf(b2);
// expr == 3 when x == 7
ILiteral b3 = model.NewBoolVar("b3");
model.Add(x == 7).OnlyEnforceIf(b3);
model.Add(expr == 3).OnlyEnforceIf(b3);
// At least one bi is true. (we could use a sum == 1).
model.AddBoolOr(new ILiteral[] { b0, b2, b3 });
// Search for x values in increasing order.
model.AddDecisionStrategy(new IntVar[] { x },
DecisionStrategyProto.Types.VariableSelectionStrategy.ChooseFirst,
DecisionStrategyProto.Types.DomainReductionStrategy.SelectMinValue);
// Create the solver.
CpSolver solver = new CpSolver();
// Force solver to follow the decision strategy exactly.
solver.StringParameters = "search_branching:FIXED_SEARCH";
VarArraySolutionPrinter cb = new VarArraySolutionPrinter(new IntVar[] { x, expr });
solver.SearchAllSolutions(model, cb);
}
static void Main()
{
// Creates the model.
// [START model]
CpModel model = new CpModel();
// [END model]
// Creates the variables.
// [START variables]
int num_vals = 3;
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
// [END variables]
// Adds a different constraint.
// [START constraints]
model.Add(x != y);
// [END constraints]
// Creates a solver and solves the model.
// [START solve]
CpSolver solver = new CpSolver();
VarArraySolutionPrinter cb = new VarArraySolutionPrinter(new IntVar[] { x, y, z });
solver.SearchAllSolutions(model, cb);
// [END solve]
Console.WriteLine(String.Format("Number of solutions found: {0}", cb.SolutionCount()));
}
static void Main()
{
long earliness_date = 5;
long earliness_cost = 8;
long lateness_date = 15;
long lateness_cost = 12;
// Create the CP-SAT model.
CpModel model = new CpModel();
// Declare our primary variable.
IntVar x = model.NewIntVar(0, 20, "x");
// Create the expression variable and implement the piecewise linear
// function.
//
// \ /
// \______/
// ed ld
//
long large_constant = 1000;
IntVar expr = model.NewIntVar(0, large_constant, "expr");
// First segment.
IntVar s1 = model.NewIntVar(-large_constant, large_constant, "s1");
model.Add(s1 == earliness_cost * (earliness_date - x));
// Second segment.
IntVar s2 = model.NewConstant(0);
// Third segment.
IntVar s3 = model.NewIntVar(-large_constant, large_constant, "s3");
model.Add(s3 == lateness_cost * (x - lateness_date));
// Link together expr and x through s1, s2, and s3.
model.AddMaxEquality(expr, new IntVar[] { s1, s2, s3 });
// Search for x values in increasing order.
model.AddDecisionStrategy(
new IntVar[] { x },
DecisionStrategyProto.Types.VariableSelectionStrategy.ChooseFirst,
DecisionStrategyProto.Types.DomainReductionStrategy.SelectMinValue);
// Create the solver.
CpSolver solver = new CpSolver();
// Force solver to follow the decision strategy exactly.
solver.StringParameters = "search_branching:FIXED_SEARCH";
VarArraySolutionPrinter cb =
new VarArraySolutionPrinter(new IntVar[] { x, expr });
solver.SearchAllSolutions(model, cb);
}
public void Solve()
{
// Creates the model.
CpModel model = new CpModel();
// Creates the variables.
int num_vals = 9;
//Creat the Sudoku grid
IntVar[][] grid = new IntVar[9][];
for (int k = 0; k < 9; k++)
{
grid[k] = new IntVar[9];
}
//get initial sudoku grid and put the 9 possibilities in Empty cells
for (int i = 0; i < 9; i++)
{
for (int j = 0; j < 9; j++)
{
if (Sudoku.getCaseInitialSudoku(i, j) == 0)
{
grid[i][j] = model.NewIntVar(1, num_vals, "C" + i.ToString() + j.ToString());
}
else
{
grid[i][j] = model.NewIntVar(Sudoku.getCaseInitialSudoku(i, j), Sudoku.getCaseInitialSudoku(i, j), "C" + i.ToString() + j.ToString());
}
}
}
// Adds a different constraint.
for (int k = 0; k < 9; k++)
{
//All the ligne might have a different number in each cells
model.AddAllDifferent(GetRow(grid, k));
//All the columns might have a different number in each cells
model.AddAllDifferent(GetColumn(grid, k));
}
//All 9 Regions might have a different number in each cells
for (int region = 0; region < 9; region++)
{
model.AddAllDifferent(GetRegion(grid, region));
}
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
VarArraySolutionPrinter cb = new VarArraySolutionPrinter(grid);
solver.SearchAllSolutions(model, cb);
int[][] values = cb.getValues();
Sudoku.setSudoku(values);
}
// [END solution_printer]
// Solve the CP+IS+FUN==TRUE cryptarithm.
static void Main()
{
// Constraint programming engine
// [START model]
CpModel model = new CpModel();
// [START model]
// [START variables]
int kBase = 10;
IntVar c = model.NewIntVar(1, kBase - 1, "C");
IntVar p = model.NewIntVar(0, kBase - 1, "P");
IntVar i = model.NewIntVar(1, kBase - 1, "I");
IntVar s = model.NewIntVar(0, kBase - 1, "S");
IntVar f = model.NewIntVar(1, kBase - 1, "F");
IntVar u = model.NewIntVar(0, kBase - 1, "U");
IntVar n = model.NewIntVar(0, kBase - 1, "N");
IntVar t = model.NewIntVar(1, kBase - 1, "T");
IntVar r = model.NewIntVar(0, kBase - 1, "R");
IntVar e = model.NewIntVar(0, kBase - 1, "E");
// We need to group variables in a list to use the constraint AllDifferent.
IntVar[] letters = new IntVar[] { c, p, i, s, f, u, n, t, r, e };
// [END variables]
// [START constraints]
// Define constraints.
model.AddAllDifferent(letters);
// CP + IS + FUN = TRUE
model.Add(c * kBase + p + i * kBase + s + f * kBase * kBase + u * kBase + n ==
t * kBase * kBase * kBase + r * kBase * kBase + u * kBase + e);
// [END constraints]
// [START solve]
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
VarArraySolutionPrinter cb = new VarArraySolutionPrinter(letters);
// Search for all solutions.
solver.StringParameters = "enumerate_all_solutions:true";
// And solve.
solver.Solve(model, cb);
// [END solve]
// [START statistics]
Console.WriteLine("Statistics");
Console.WriteLine($" conflicts : {solver.NumConflicts()}");
Console.WriteLine($" branches : {solver.NumBranches()}");
Console.WriteLine($" wall time : {solver.WallTime()} s");
Console.WriteLine($" number of solutions found: {cb.SolutionCount()}");
// [END statistics]
}
static void Main()
{
// Create the CP-SAT model.
CpModel model = new CpModel();
// Declare our two primary variables.
IntVar x = model.NewIntVar(0, 10, "x");
IntVar y = model.NewIntVar(0, 10, "y");
// Declare our intermediate boolean variable.
IntVar b = model.NewBoolVar("b");
// Implement b == (x >= 5).
model.Add(x >= 5).OnlyEnforceIf(b);
model.Add(x < 5).OnlyEnforceIf(b.Not());
// Create our two half-reified constraints.
// First, b implies (y == 10 - x).
model.Add(y == 10 - x).OnlyEnforceIf(b);
// Second, not(b) implies y == 0.
model.Add(y == 0).OnlyEnforceIf(b.Not());
// Search for x values in increasing order.
model.AddDecisionStrategy(
new IntVar[] { x },
DecisionStrategyProto.Types.VariableSelectionStrategy.ChooseFirst,
DecisionStrategyProto.Types.DomainReductionStrategy.SelectMinValue);
// Create the solver.
CpSolver solver = new CpSolver();
// Force solver to follow the decision strategy exactly.
solver.StringParameters = "search_branching:FIXED_SEARCH";
VarArraySolutionPrinter cb =
new VarArraySolutionPrinter(new IntVar[] { x, y, b });
solver.SearchAllSolutions(model, cb);
}
static void Main()
{
// Creates the model.
// [START model]
CpModel model = new CpModel();
// [END model]
// Creates the variables.
// [START variables]
int num_vals = 3;
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
// [END variables]
// Creates the constraints.
// [START constraints]
model.Add(x != y);
// [END constraints]
// Solution hinting: x <- 1, y <- 2
model.AddHint(x, 1);
model.AddHint(y, 2);
// [START objective]
model.Maximize(LinearExpr.ScalProd(new IntVar[] { x, y, z }, new int[] { 1, 2, 3 }));
// [END objective]
// Creates a solver and solves the model.
// [START solve]
CpSolver solver = new CpSolver();
VarArraySolutionPrinter cb =
new VarArraySolutionPrinter(new IntVar[] { x, y, z });
CpSolverStatus status = solver.SolveWithSolutionCallback(model, cb);
// [END solve]
}
static void MinimalCpSatAllSolutions()
{
// Creates the model.
CpModel model = new CpModel();
// Creates the variables.
int num_vals = 3;
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
// Creates the constraints.
model.Add(x != y);
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
VarArraySolutionPrinter cb =
new VarArraySolutionPrinter(new IntVar[] { x, y, z });
solver.SearchAllSolutions(model, cb);
Console.WriteLine(String.Format("Number of solutions found: {0}",
cb.SolutionCount()));
}
static void Main()
{
// Model.
CpModel model = new CpModel();
// Variables.
IntVar x = model.NewIntVar(0, 10, "x");
IntVar y = model.NewIntVar(0, 10, "y");
IntVar b = model.NewBoolVar("b");
// Implement b == (x >= 5).
model.Add(x >= 5).OnlyEnforceIf(b);
model.Add(x < 5).OnlyEnforceIf(b.Not());
// b implies (y == 10 - x).
model.Add(y == 10 - x).OnlyEnforceIf(b);
// not(b) implies y == 0.
model.Add(y == 0).OnlyEnforceIf(b.Not());
// Search for x values in increasing order.
model.AddDecisionStrategy(
new IntVar[] { x },
DecisionStrategyProto.Types.VariableSelectionStrategy.ChooseFirst,
DecisionStrategyProto.Types.DomainReductionStrategy.SelectMinValue);
// Create a solver and solve with a fixed search.
CpSolver solver = new CpSolver();
// Force solver to follow the decision strategy exactly.
solver.StringParameters = "search_branching:FIXED_SEARCH";
VarArraySolutionPrinter cb =
new VarArraySolutionPrinter(new IntVar[] { x, y, b });
solver.SearchAllSolutions(model, cb);
}
