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Constraint Programming Solver
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lib/solver/constraints/constraint_factory.ex
defmodule CPSolver.Constraint.Factory do
alias CPSolver.Constraint.{
Sum,
ElementVar,
Element2D,
Modulo,
Absolute,
LessOrEqual,
Equal,
Reified
}
alias CPSolver.Propagator.Modulo, as: ModuloPropagator
alias CPSolver.IntVariable, as: Variable
alias CPSolver.BooleanVariable
alias CPSolver.Variable.Interface
alias CPSolver.DefaultDomain, as: Domain
import CPSolver.Variable.View.Factory
def element(array, x, opts \\ []) do
y_domain = Enum.reduce(array,
MapSet.new(), fn el, acc ->
Interface.domain(el) |> Domain.to_list() |> MapSet.union(acc)
end)
|> MapSet.to_list()
y = Variable.new(y_domain, name: Keyword.get(opts, :name, make_ref()))
result(y, ElementVar.new(array, x, y))
end
def element2d(array2d, x, y, opts \\ []) do
domain = array2d |> List.flatten()
z = Variable.new(domain, name: Keyword.get(opts, :name, make_ref()))
result(z, Element2D.new([array2d, x, y, z]))
end
def sum(vars, opts \\ []) do
domain =
case opts[:domain] do
nil ->
{domain_min, domain_max} =
Enum.reduce(vars, {0, 0}, fn var, {min_acc, max_acc} ->
domain = Interface.domain(var) |> Domain.to_list()
{min_acc + Enum.min(domain), max_acc + Enum.max(domain)}
end)
domain_min..domain_max
d ->
d
end
sum_var = Variable.new(domain, name: Keyword.get(opts, :name, make_ref()))
result(sum_var, Sum.new(sum_var, vars))
end
def add(var1, var2, opts \\ []) do
sum([var1, var2], opts)
end
def subtract(var1, var2, opts \\ []) do
add(var1, linear(var2, -1, 0), opts)
end
def mod(x, y, opts \\ []) do
domain =
Keyword.get(opts, :domain) ||
(
{lb, ub} = ModuloPropagator.mod_bounds(x, y)
lb..ub
)
mod_var = Variable.new(domain, name: Keyword.get(opts, :name, make_ref()))
result(mod_var, Modulo.new(mod_var, x, y))
end
def absolute(x, opts \\ []) do
domain =
Keyword.get(opts, :domain) ||
(
abs_min = abs(Interface.min(x))
abs_max = abs(Interface.max(x))
0..max(abs_min, abs_max)
)
abs_var = Variable.new(domain, name: Keyword.get(opts, :name, make_ref()))
result(abs_var, Absolute.new(x, abs_var))
end
defp compose(constraint1, constraint2, relation) do
b1 = BooleanVariable.new()
b2 = BooleanVariable.new()
reif_c1 = Reified.new([constraint1, b1])
reif_c2 = Reified.new([constraint2, b2])
%{constraints: [reif_c1, reif_c2, relation.new([b1, b2])], derived_variables: [b1, b2]}
end
## Implication, equivalence, inverse implication.
## These function produce the list of constraints:
## - 2 reified constraints for constraint1 and constraint2
## - relational constraint (LessOrEqual for implications, Equal for equivalence)
## over control variables induced by reified constraints.
##
def impl(constraint1, constraint2) do
compose(constraint1, constraint2, LessOrEqual)
end
def equiv(constraint1, constraint2) do
compose(constraint1, constraint2, Equal)
end
def inverse_impl(constraint1, constraint2) do
impl(constraint2, constraint1)
end
defp result(derived_variable, constraint) do
{derived_variable, constraint}
end
end