poirot

Type Members

class BooleanVar extends IntVar

Defines a boolean variable and its primitive constraints.
class FSM extends org.jacop.util.fsm.FSM

FSM specification for regular constraint.
class IndomainRandom[A <: IntVar] extends Indomain[A]

Like IndomainRandom but with explicit random generator argument.
class IntSet extends IntervalDomain

Defines an ordered set of integers and basic operations on these sets.
class IntVar extends org.jacop.core.IntVar

Defines a finite domain integer variable and its primitive constraints.
class Model extends Store

Manages all variables, constraints and global constraints for JaCoP constraint solver.
class Network extends NetworkBuilder

Network specification for networkflow constraint
trait Problem extends AnyRef
class ScalaSolutionListener[A <: Var] extends SimpleSolutionListener[A]

Solution listener that prints solutions of search using user specified functions.
class SetVar extends org.jacop.set.core.SetVar

Defines a set variable and its primitive constraints.
case class Stats(nodes: Int, decisions: Int, wrong: Int, backtracks: Int, depth: Int, solutions: Int) extends Product with Serializable
type Vec[+A] = IndexedSeq[A]

Value Members

def AND(xs: PrimitiveConstraint*)(implicit model: Model): PrimitiveConstraint

Wrapper for org.jacop.constraints.And.
Wrapper for org.jacop.constraints.And.
xs
constraints to be conjunction.
returns
the constraint that is a a conjunction of constraints.
object BooleanVar
object FSM
object Implicits

Implicit conversions of Int and Bool to IntVar and BoolVar.
object IntSet
object IntVar
object Model
def NOT(constr: PrimitiveConstraint)(implicit model: Model): PrimitiveConstraint

Wrapper for org.jacop.constraints.Not.
Wrapper for org.jacop.constraints.Not.
constr
constraints to be negated.
returns
the negated constraint.
object Network
def OR(list: PrimitiveConstraint*)(implicit model: Model): PrimitiveConstraint

Wrapper for org.jacop.constraints.Or.
Wrapper for org.jacop.constraints.Or.
list
constraints to be disjunction.
returns
the constraint that is a a disjunction of constraints.
val Vec: IndexedSeq.type
def abs(x: IntVar)(implicit model: Model): IntVar

Wrapper for org.jacop.constraints.AbsXeqY.
Wrapper for org.jacop.constraints.AbsXeqY.
x
variable for abs operation.
returns
absolute value result.
def allDifferent(xs: Iterable[IntVar])(implicit model: Model): Unit

Wrapper for org.jacop.constraints.Alldiff.
Wrapper for org.jacop.constraints.Alldiff.
xs
set of variables to be different.
def allDistinct(xs: Iterable[IntVar])(implicit model: Model): Unit

Wrapper for org.jacop.constraints.Alldistinct.
Wrapper for org.jacop.constraints.Alldistinct.
xs
set of variables to be different.
def among(listX: Array[IntVar], listY: Array[IntVar], n: IntVar)(implicit model: Model): Unit

Wrapper for org.jacop.constraints.AmongVar.
Wrapper for org.jacop.constraints.AmongVar.
XXX TODO: remove arrays, unify sequences, rename to assignAmong, add proper among
listX
array of variables.
listY
array of variables to be checked if their values .
n
number of values found.
def among(xs: Iterable[IntVar], kSet: IntSet, n: IntVar)(implicit model: Model): Unit

Wrapper for org.jacop.constraints.Among.
Wrapper for org.jacop.constraints.Among.
XXX TODO: rename to assignAmong, add proper among
xs
variables.
kSet
values to be checked.
n
number of values found.
def antiFirstFail[A <: Var]: LargestDomain[A]

Wrapper for org.jacop.search.LargestDomain.
Wrapper for org.jacop.search.LargestDomain.
returns
related variable selection method.
def antiFirstFailSet[A <: org.jacop.set.core.SetVar]: MaxCardDiff[A]

Wrapper for org.jacop.set.search.MaxCardDiff.
Wrapper for org.jacop.set.search.MaxCardDiff.
returns
related variable selection method.
def assignment(x: Array[IntVar], y: Array[IntVar])(implicit model: Model): Unit

Wrapper for org.jacop.constraints.Assignment.
Wrapper for org.jacop.constraints.Assignment.
XXX TODO: remove arrays, unify sequences
x
array of variables.
y
array variables that values are permutation of x.
def binPacking(bins: (IntVar, IntVar, Int)*)(implicit model: Model): Unit

Wrapper for org.
Wrapper for org.jacop.constraints.binpack.Binpack (?).
bins
list containing which tuples of bins, their loads and their weights
def card(s: SetVar, n: Int)(implicit model: Model): Unit

Wrapper for org.jacop.set.constraints.CardA.
Wrapper for org.jacop.set.constraints.CardA.
s
constrained set variable.
n
cardinality.
def card(s: SetVar)(implicit model: Model): IntVar

Wrapper for org.jacop.set.constraints.CardAeqX.
Wrapper for org.jacop.set.constraints.CardAeqX.
s
constrained set variable.
returns
variable defining cardinality of s.
def circuit(nodes: Iterable[IntVar])(implicit model: Model): Unit

Wrapper for org.jacop.constraints.Circuit.
Wrapper for org.jacop.constraints.Circuit.
nodes
variables, which domains define next nodes in the graph.
def count(xs: Iterable[IntVar], value: Int)(implicit model: Model): IntVar

Wrapper for org.jacop.constraints.Count.
Wrapper for org.jacop.constraints.Count.
xs
variables to count number of values value.
returns
number of values value.
def cumulative(xs: Iterable[(IntVar, IntVar, IntVar)], limit: IntVar)(implicit model: Model): Unit

Wrapper for org.jacop.constraints.Cumulative.
Wrapper for org.jacop.constraints.Cumulative.
xs
tuples consisting of (t, d, r), where t is start times of tasks, d is duration of tasks, r is number of resources of tasks.
limit
limit on number of resources used in a schedule.
def dfs[A <: Var](all: Boolean): DepthFirstSearch[A]

Depth first search method.
Depth first search method.
returns
standard depth first search.
def diff2(rectangles: Vec[Vec[IntVar]])(implicit model: Model): Unit

Wrapper for org.jacop.constraints.Diff2.
Wrapper for org.jacop.constraints.Diff2.
rectangles
sequence of four element vectors representing rectangles [x, y, lx, ly]
def diff2(x: Array[IntVar], y: Array[IntVar], lx: Array[IntVar], ly: Array[IntVar])(implicit model: Model): Unit

Wrapper for org.jacop.constraints.Diff2.
Wrapper for org.jacop.constraints.Diff2.
XXX TODO: remove arrays, unify sequences
x
coordinate X of rectangle.
y
coordinate Y of rectangle.
lx
length in derection X of rectangle.
ly
length in derection Y of rectangle.
def firstFail[A <: Var]: SmallestDomain[A]

Wrapper for org.jacop.search.SmallestDomain.
Wrapper for org.jacop.search.SmallestDomain.
returns
related variable selection method.
def firstFailSet[A <: org.jacop.set.core.SetVar]: MinCardDiff[A]

Wrapper for org.jacop.set.search.MinCardDiff.
Wrapper for org.jacop.set.search.MinCardDiff.
returns
related variable selection method.
def gcc(xs: (IntVar, IntVar)*)(implicit model: Model): Unit

Wrapper for org.jacop.constraints.GCC.
Wrapper for org.jacop.constraints.GCC.
xs
array of tuples of variables and their counters
def indomainMax[A <: org.jacop.core.IntVar]: IndomainMax[A]

Wrapper for org.jacop.search.IndomainMax.
Wrapper for org.jacop.search.IndomainMax.
returns
related variable selection method.
def indomainMaxSet[A <: org.jacop.set.core.SetVar]: IndomainSetMax[A]

Wrapper for org.jacop.set.search.IndomainSetMax.
Wrapper for org.jacop.set.search.IndomainSetMax.
returns
related indomain method.
def indomainMedian[A <: org.jacop.core.IntVar]: IndomainMedian[A]

Wrapper for org.jacop.search.IndomainMedian.
Wrapper for org.jacop.search.IndomainMedian.
returns
related variable selection method.
def indomainMiddle[A <: org.jacop.core.IntVar]: IndomainMiddle[A]

Wrapper for org.jacop.search.IndomainMiddle.
Wrapper for org.jacop.search.IndomainMiddle.
returns
related variable selection method.
def indomainMin[A <: org.jacop.core.IntVar]: IndomainMin[A]

Wrapper for org.jacop.search.IndomainMin.
Wrapper for org.jacop.search.IndomainMin.
returns
related variable selection method.
def indomainMinSet[A <: org.jacop.set.core.SetVar]: IndomainSetMin[A]

Wrapper for org.jacop.set.search.IndomainSetMin.
Wrapper for org.jacop.set.search.IndomainSetMin.
returns
related indomain method.
def indomainRandom[A <: IntVar](r: Random = new util.Random()): IndomainRandom[A]

Wrapper for org.jacop.search.IndomainRandom.
Wrapper for org.jacop.search.IndomainRandom.
returns
related variable selection method.
def indomainRandomSet[A <: org.jacop.set.core.SetVar]: IndomainSetRandom[A]

Wrapper for org.jacop.set.search.IndomainSetRandom.
Wrapper for org.jacop.set.search.IndomainSetRandom.
returns
related indomain method.
def inputOrder[A <: Var]: ComparatorVariable[A]

Defines null variable selection method that is interpreted by JaCoP as input order.
Defines null variable selection method that is interpreted by JaCoP as input order.
XXX TODO: ugly null; ought to use place holde value
returns
related variable selection method.
def intAt(index: IntVar, xs: Seq[Int], offset: Int = 0)(implicit model: Model): IntVar

Wrapper for org.jacop.constraints.Element.
Wrapper for org.jacop.constraints.Element.
index
index to select element from list of elements.
xs
sequence of integers that can be assigned to values.
offset
value of index offset (shift).
returns
the variable yielding the element at the given index
def intVarAt(index: IntVar, xs: Seq[IntVar], offset: Int = 0)(implicit model: Model): IntVar
def knapsack(profits: Array[Int], weights: Array[Int], quantity: List[IntVar], knapsackCapacity: IntVar, knapsackProfit: IntVar)(implicit model: Model): Unit

Wrapper for org.jacop.constraints.knapsack.Knapsack.
Wrapper for org.jacop.constraints.knapsack.Knapsack.
XXX TODO: replace arrays, unify tuples
profits
array of profite for items.
weights
array of weights for items.
quantity
array of quantities of items.
knapsackCapacity
knapsack capacity.
knapsackProfit
profite when selling items.
def largest[A <: org.jacop.core.IntVar]: LargestMax[A]

Wrapper for org.jacop.search.LargestMax.
Wrapper for org.jacop.search.LargestMax.
returns
related variable selection method.
def lex(x: Array[Array[IntVar]])(implicit model: Model): Unit

Wrapper for org.jacop.constraints.Lex.
Wrapper for org.jacop.constraints.Lex.
x
array of vectors of variables to be lexicographically ordered.
def matching[A <: org.jacop.core.IntVar](a: SetVar, list: Iterable[A])(implicit model: Model): Unit

Wrapper for org.jacop.set.constraints.Match.
Wrapper for org.jacop.set.constraints.Match.
a
a set variable to be matched against list of IntVar.
list
variables that get values from the set.
def max(xs: IntVar*)(implicit model: Model): IntVar

Wrapper for org.jacop.constraints.Max.
Wrapper for org.jacop.constraints.Max.
xs
variables where maximum values is to be found.
returns
max value.
var maxNumSolutions: Int

The maximum number of solutions to be explored.
The maximum number of solutions to be explored. -1 indicates that there is no limit.
def maxRegret[A <: org.jacop.core.IntVar]: MaxRegret[A]

Wrapper for org.jacop.search.MaxRegret.
Wrapper for org.jacop.search.MaxRegret.
returns
related variable selection method.
def maximize[A <: Var](select: SelectChoicePoint[A], cost: IntVar, printSolutions: () ⇒ Unit*)(implicit m: ClassTag[A], model: Model): Boolean

Maximization search method.
Maximization search method.
select
select method defining variable selection and value assignment methods.
cost
Cost variable
returns
true if solution found and false otherwise.
def maximizeSeq[A <: Var](select: Iterable[SelectChoicePoint[A]], cost: IntVar, printSolutions: () ⇒ Unit*)(implicit m: ClassTag[A], model: Model): Boolean

Maximization method for sequence of search methods (specified by list of select methods).
Maximization method for sequence of search methods (specified by list of select methods).
select
list of select methods defining variable selection and value assignment methods for sequence of searchs.
cost
Cost variable
returns
true if solution found and false otherwise.
def min(xs: IntVar*)(implicit model: Model): IntVar

Wrapper for org.jacop.constraints.Min.
Wrapper for org.jacop.constraints.Min.
xs
variables where minimum values is to be found.
returns
minimum value.
def minGLBCard[A <: org.jacop.set.core.SetVar]: MinGlbCard[A]

Wrapper for org.jacop.set.search.MinGlbCard.
Wrapper for org.jacop.set.search.MinGlbCard.
returns
related variable selection method.
def minLUBCard[A <: org.jacop.set.core.SetVar]: MinLubCard[A]

Wrapper for org.jacop.set.search.MinLubCard.
Wrapper for org.jacop.set.search.MinLubCard.
returns
related variable selection method.
def minimize[A <: Var](select: SelectChoicePoint[A], cost: IntVar, printSolutions: () ⇒ Unit*)(implicit m: ClassTag[A], model: Model): Boolean

Minimization search method.
Minimization search method.
select
select method defining variable selection and value assignment methods.
cost
Cost variable
returns
true if solution found and false otherwise.
def minimizeSeq[A <: Var](select: Iterable[SelectChoicePoint[A]], cost: IntVar, printSolutions: () ⇒ Unit*)(implicit m: ClassTag[A], model: Model): Boolean

Minimization method for sequence of search methods (specified by list of select methods).
Minimization method for sequence of search methods (specified by list of select methods).
select
list of select methods defining variable selection and value assignment methods for sequence of searchs.
cost
Cost variable
returns
true if solution found and false otherwise.
def mostConstrained[A <: Var]: MostConstrainedStatic[A]

Wrapper for org.jacop.search.MostConstrainedStatic.
Wrapper for org.jacop.search.MostConstrainedStatic.
returns
related variable selection method.
def mostConstrainedSet[A <: org.jacop.set.core.SetVar]: MostConstrainedStatic[A]

Wrapper for org.jacop.search.MostConstrainedStatic.
Wrapper for org.jacop.search.MostConstrainedStatic.
returns
related variable selection method.
def networkFlow(net: NetworkBuilder)(implicit model: Model): Unit
def numDistinct(xs: IntVar*)(implicit model: Model): IntVar

Wrapper for org.jacop.constraints.Values.
Wrapper for org.jacop.constraints.Values.
xs
variables to count number of different values.
returns
number of different values.
var recordSolutions: Boolean
def regular(dfa: FSM, vars: Iterable[IntVar])(implicit model: Model): Unit

Wrapper for org.jacop.constraints.regular.Regular.
Wrapper for org.jacop.constraints.regular.Regular.
dfa
specification of finite state machine using class fsm.
vars
list of variables assigned to fsm nodes.
def satisfy[A <: Var](select: SelectChoicePoint[A], printSolutions: () ⇒ Unit*)(implicit m: ClassTag[A], model: Model): Boolean

Search method that finds a solution.
Search method that finds a solution.
select
select method defining variable selection and value assignment methods.
returns
true if solution found and false otherwise.
def satisfyAll[A <: Var](select: SelectChoicePoint[A], printSolutions: () ⇒ Unit*)(implicit m: ClassTag[A], model: Model): Boolean

Search method that finds all solutions.
Search method that finds all solutions.
select
select method defining variable selection and value assignment methods.
returns
true if solution found and false otherwise.
def satisfyAllSeq[A <: Var](select: Iterable[SelectChoicePoint[A]], printSolutions: () ⇒ Unit*)(implicit m: ClassTag[A], model: Model): Boolean

Search method for finding all solutions using a sequence of search methods (specified by list of select methods).
Search method for finding all solutions using a sequence of search methods (specified by list of select methods).
select
list of select methods defining variable selection and value assignment methods for sequence of searchs.
returns
true if solution found and false otherwise.
def satisfySeq[A <: Var](select: Iterable[SelectChoicePoint[A]], printSolutions: () ⇒ Unit*)(implicit m: ClassTag[A], model: Model): Boolean

Search method for finding a solution using a sequence of search methods (specified by list of select methods).
Search method for finding a solution using a sequence of search methods (specified by list of select methods).
select
list of select methods defining variable selection and value assignment methods for sequence of searchs.
returns
true if solution found and false otherwise.
def search[A <: Var](vars: Iterable[A], heuristic: ComparatorVariable[A], indom: Indomain[A])(implicit m: ClassTag[A]): SelectChoicePoint[A]

Defines list of variables, their selection method for search and value selection
Defines list of variables, their selection method for search and value selection
returns
select method for search.
def searchSplit[A <: org.jacop.core.IntVar](vars: Iterable[A], heuristic: ComparatorVariable[A])(implicit m: ClassTag[A]): SelectChoicePoint[A]

Defines list of variables, their selection method for split search and value selection
Defines list of variables, their selection method for split search and value selection
returns
select method for search.
def searchVector[A <: Var](vars: Vec[Vec[A]], heuristic: ComparatorVariable[A], indom: Indomain[A])(implicit m: ClassTag[A]): SelectChoicePoint[A]

Defines list of variables, their selection method for sequential search and value selection
Defines list of variables, their selection method for sequential search and value selection
returns
select method for search.
def sequence(list: Array[IntVar], set: IntSet, q: Int, min: Int, max: Int)(implicit model: Model): Unit

Wrapper for org.jacop.constraints.Sequence.
Wrapper for org.jacop.constraints.Sequence.
list
list of variables to be constrained.
set
set of values to be checked.
q
length of the sub-sequence.
min
minimal number of occurrences of values in the sub-sequence.
max
maximal number of occurrences of values in the sub-sequence.
def smallest[A <: org.jacop.core.IntVar]: SmallestMin[A]

Wrapper for org.jacop.search.SmallestMin.
Wrapper for org.jacop.search.SmallestMin.
returns
related variable selection method.
def smallestMin[A <: org.jacop.core.IntVar]: SmallestMin[A]

Wrapper for org.jacop.search.SmallestMin.
Wrapper for org.jacop.search.SmallestMin.
returns
related variable selection method.
def smallestSet[A <: org.jacop.set.core.SetVar]: MinGlbCard[A]

Currently equivalent to minGLBCard.
Currently equivalent to minGLBCard.
returns
related variable selection method.
def softAlldifferent(xVars: Array[IntVar], costVar: IntVar)(implicit model: Model): Unit

Wrapper for org.jacop.constraints.SoftAlldifferent.
Wrapper for org.jacop.constraints.SoftAlldifferent.
xVars
array of variables to be constrained to be different.
costVar
measures degree of violation (uses value based violation).
def softGCC(xVars: Array[IntVar], hardLowerBound: Array[Int], hardUpperBound: Array[Int], countedValue: Array[Int], softCounters: Array[IntVar], costVar: IntVar)(implicit model: Model): Unit

Wrapper for org.jacop.constraints.SoftGCC.
Wrapper for org.jacop.constraints.SoftGCC.
xVars
array of variables to be constrained to be different.
hardLowerBound
lower bound on limits that can not be violated.
hardUpperBound
upper bound on limits that can not be violated
countedValue
values that are counted.
softCounters
specifies preferred values for counters and can be violated.
def stretch(values: Array[Int], min: Array[Int], max: Array[Int], x: Array[IntVar])(implicit model: Model): Unit

Wrapper for org.jacop.constraints.Stretch.
Wrapper for org.jacop.constraints.Stretch.
values
a list of values to be assigned to sub-sequences.
min
minimal length of the sub-sequence for each value on position i.
max
maximal length of the sub-sequence for each value on position i.
x
list of variables to be constrained.
def sum(xs: Iterable[IntVar])(implicit model: Model): IntVar

Wrapper for org.jacop.constraints.Sum.
Wrapper for org.jacop.constraints.Sum.
xs
variables to be summed up.
returns
summation result.
def table(list: Iterable[(IntVar, Iterable[Int])])(implicit model: Model): Unit

Wrapper for org.jacop.constraints.ExtensionalSupportVA.
Wrapper for org.jacop.constraints.ExtensionalSupportVA.
XXX TODO: rename to assignTable, add proper table
list
sequence of tuples consisting of variables and sequences of allowed values be assigned
var timeOut: Int

The search time out in seconds.
The search time out in seconds. -1 indicates that there is no time out.
var trace: Boolean
def weightedSum(vars: Seq[IntVar], weights: Seq[Int])(implicit model: Model): IntVar

Wrapper for org.jacop.constraints.SumWeight.
Wrapper for org.jacop.constraints.SumWeight.
vars
variables to sum
weights
weights for the variables
returns
summation result.
def withStatistics[Z](block: ⇒ Z): (Z, Stats)

package poirot

Type Members

class BooleanVar extends IntVar

class FSM extends org.jacop.util.fsm.FSM

class IndomainRandom[A <: IntVar] extends Indomain[A]

class IntSet extends IntervalDomain

class IntVar extends org.jacop.core.IntVar

class Model extends Store

class Network extends NetworkBuilder

trait Problem extends AnyRef

class ScalaSolutionListener[A <: Var] extends SimpleSolutionListener[A]

class SetVar extends org.jacop.set.core.SetVar

case class Stats(nodes: Int, decisions: Int, wrong: Int, backtracks: Int, depth: Int, solutions: Int) extends Product with Serializable

type Vec[+A] = IndexedSeq[A]

Value Members

def AND(xs: PrimitiveConstraint*)(implicit model: Model): PrimitiveConstraint

object BooleanVar

object FSM

object Implicits

object IntSet

object IntVar

object Model

def NOT(constr: PrimitiveConstraint)(implicit model: Model): PrimitiveConstraint

object Network

def OR(list: PrimitiveConstraint*)(implicit model: Model): PrimitiveConstraint

val Vec: IndexedSeq.type

def abs(x: IntVar)(implicit model: Model): IntVar

def allDifferent(xs: Iterable[IntVar])(implicit model: Model): Unit

def allDistinct(xs: Iterable[IntVar])(implicit model: Model): Unit

def among(listX: Array[IntVar], listY: Array[IntVar], n: IntVar)(implicit model: Model): Unit

def among(xs: Iterable[IntVar], kSet: IntSet, n: IntVar)(implicit model: Model): Unit

def antiFirstFail[A <: Var]: LargestDomain[A]

def antiFirstFailSet[A <: org.jacop.set.core.SetVar]: MaxCardDiff[A]

def assignment(x: Array[IntVar], y: Array[IntVar])(implicit model: Model): Unit

def binPacking(bins: (IntVar, IntVar, Int)*)(implicit model: Model): Unit

def card(s: SetVar, n: Int)(implicit model: Model): Unit

def card(s: SetVar)(implicit model: Model): IntVar

def circuit(nodes: Iterable[IntVar])(implicit model: Model): Unit

def count(xs: Iterable[IntVar], value: Int)(implicit model: Model): IntVar

def cumulative(xs: Iterable[(IntVar, IntVar, IntVar)], limit: IntVar)(implicit model: Model): Unit

def dfs[A <: Var](all: Boolean): DepthFirstSearch[A]

def diff2(rectangles: Vec[Vec[IntVar]])(implicit model: Model): Unit

def diff2(x: Array[IntVar], y: Array[IntVar], lx: Array[IntVar], ly: Array[IntVar])(implicit model: Model): Unit

def firstFail[A <: Var]: SmallestDomain[A]

def firstFailSet[A <: org.jacop.set.core.SetVar]: MinCardDiff[A]

def gcc(xs: (IntVar, IntVar)*)(implicit model: Model): Unit

def indomainMax[A <: org.jacop.core.IntVar]: IndomainMax[A]

def indomainMaxSet[A <: org.jacop.set.core.SetVar]: IndomainSetMax[A]

def indomainMedian[A <: org.jacop.core.IntVar]: IndomainMedian[A]

def indomainMiddle[A <: org.jacop.core.IntVar]: IndomainMiddle[A]

def indomainMin[A <: org.jacop.core.IntVar]: IndomainMin[A]

def indomainMinSet[A <: org.jacop.set.core.SetVar]: IndomainSetMin[A]

def indomainRandom[A <: IntVar](r: Random = new util.Random()): IndomainRandom[A]

def indomainRandomSet[A <: org.jacop.set.core.SetVar]: IndomainSetRandom[A]

def inputOrder[A <: Var]: ComparatorVariable[A]

def intAt(index: IntVar, xs: Seq[Int], offset: Int = 0)(implicit model: Model): IntVar

def intVarAt(index: IntVar, xs: Seq[IntVar], offset: Int = 0)(implicit model: Model): IntVar

def knapsack(profits: Array[Int], weights: Array[Int], quantity: List[IntVar], knapsackCapacity: IntVar, knapsackProfit: IntVar)(implicit model: Model): Unit

def largest[A <: org.jacop.core.IntVar]: LargestMax[A]

def lex(x: Array[Array[IntVar]])(implicit model: Model): Unit

def matching[A <: org.jacop.core.IntVar](a: SetVar, list: Iterable[A])(implicit model: Model): Unit

def max(xs: IntVar*)(implicit model: Model): IntVar

var maxNumSolutions: Int

def maxRegret[A <: org.jacop.core.IntVar]: MaxRegret[A]

def maximize[A <: Var](select: SelectChoicePoint[A], cost: IntVar, printSolutions: () ⇒ Unit*)(implicit m: ClassTag[A], model: Model): Boolean

def maximizeSeq[A <: Var](select: Iterable[SelectChoicePoint[A]], cost: IntVar, printSolutions: () ⇒ Unit*)(implicit m: ClassTag[A], model: Model): Boolean

def min(xs: IntVar*)(implicit model: Model): IntVar

def minGLBCard[A <: org.jacop.set.core.SetVar]: MinGlbCard[A]

def minLUBCard[A <: org.jacop.set.core.SetVar]: MinLubCard[A]

def minimize[A <: Var](select: SelectChoicePoint[A], cost: IntVar, printSolutions: () ⇒ Unit*)(implicit m: ClassTag[A], model: Model): Boolean

def minimizeSeq[A <: Var](select: Iterable[SelectChoicePoint[A]], cost: IntVar, printSolutions: () ⇒ Unit*)(implicit m: ClassTag[A], model: Model): Boolean

def mostConstrained[A <: Var]: MostConstrainedStatic[A]

def mostConstrainedSet[A <: org.jacop.set.core.SetVar]: MostConstrainedStatic[A]

def networkFlow(net: NetworkBuilder)(implicit model: Model): Unit

def numDistinct(xs: IntVar*)(implicit model: Model): IntVar

var recordSolutions: Boolean

def regular(dfa: FSM, vars: Iterable[IntVar])(implicit model: Model): Unit

def satisfy[A <: Var](select: SelectChoicePoint[A], printSolutions: () ⇒ Unit*)(implicit m: ClassTag[A], model: Model): Boolean

def satisfyAll[A <: Var](select: SelectChoicePoint[A], printSolutions: () ⇒ Unit*)(implicit m: ClassTag[A], model: Model): Boolean