# Designing a constraint

In this part, we are going to see how to create a constraint to be used by Choco-solver. The work will be based on the sum constraint, more specifically: $\sum_{i = 1}^{n} x_i \leq b$ where $x_i = [\underline{x_i},\overline{x_i}]$ are distinct variables and where $b$ is a constant.

Bounds Consistency Techniques for Long Linear Constraint by W.Harvey and J.Schimpf described in details how such a constraint is implemented and will serve as a basis of this tutorials.

The first filtering algorithm they depicted in the article is roughly the following:

• First, compute $F = b - \sum_{i = 1}^{n} \underline{x_i}$
• then, update variables domain, $\forall i \in [1,n], x_i \leq F + \underline{x_i}$

Note that if $F < 0$ the constraint is unsatisfiable.

## A first implementation

When one needs to declare its own constraint, actually, he needs to create a propagator. Indeed, in Choco-solver, a constraint is a container which is composed of propagators, and each propagator can eliminate values from domain variables. So the first step will be to create a java class that extends Propagator<IntVar>. The generic parameter <IntVar> indicates that the propagator only manages integer variable. Set it to BoolVar, SetVar or Variable are possible alternatives.

Once the class is created, a constructor is needed plus two methods :

• public void propagate(int evtmask) throws ContradictionException where the filtering algorithm will be applied,
• public ESat isEntailed() where the entailment/satisfaction of the propagator is checked.

We now describe how these two methods can be implemented, plus an optional yet important method and the constructor parametrization.

### Entailment

For debugging purpose or to enable constraint reification, a method named isEntailed() has to be implemented. The former is mainly used when implementing the constraint to make sure that found solutions respect the constraint specifications. The latter is called to valuate the boolean variable attached to a propagator when it is reified. The method returns ESat.TRUE, ESat.FALSE or ESat.UNDEFINED when respectively with respect to the current domain of the variables, the propagator can always be satisfied however they are instantiated, the propagator can never be satisfied and nothing can be deduced.

For example, consider the constraint $c = (x_1 + x_2 \leq 10)$ and the three following states:

• $x_1 = [1,2], x_2 = [1,2]$ : the method returns ESat.TRUE since all combinations satisfy c,
• $x_1 = [22,23], x_2 = [10,12]$ : the method returns ESat.FALSE since no combination satisfies c and
• $x_1 = [1,10], x_2 = [1,10]$ : the method returns ESat.UNDEFINED since some combinations satisfy $c$, other don’t.

The entailment method can be implemented as is:

Override
public ESat isEntailed() {
int sumUB = 0, sumLB = 0;
for (int i = 0; i < vars.length; i++) {
sumLB += vars[i].getLB();
sumUB += vars[i].getUB();
}
if (sumUB <= b) {
return ESat.TRUE;
}
if (sumLB > b) {
return ESat.FALSE;
}
return ESat.UNDEFINED;
}


### Filtering algorithm

A propagator’s first objective is to remove, from its variables domain, values that cannot belong to any solutions. This is the role of the propagate(int m) method. This method bases its deductions on the current domain of the variables and can update their domain on the fly. The expected state of this method exit is called a ‘fix-point’.

Indeed, a propagator ‘p’ is not notified of its modifications but only those triggered by other propagators which modified at least one variable of ‘p’. Each time one, at least, of its variable is modified, the satisfaction of a propagator need to check along with some filtering, if any, based on earlier modification.

Applying filtering rules can lead to a contradiction. In that case, the solver resumes after the filtering algorithm is stopped and manages to undo domain modification. Since restoring previous states is managed by the solver, it can safely be ignored when creating a propagator.

In the case of the sum constraint, $F$ is computed first, then fast check of $F$ is made to check obvious unsatisfaction and eventually a loop is operated over the variables to make sure that each upper bound is correct wrt to $F$. A simple loop is enough since $F$ is computed reading $\overline{x_i}$ and writing $\underline{x_i}$.

Note that the method can throw an exception. An exception denotes that a failure is detected and the execution has to be stopped. In our case, if $F < 0$ an exception should be thrown. In other cases, the methods that modify the variables domain can thrown such an exception too, when for example, the domain becomes empty.

The filtering method can be implemented as is:

@Override
int sumLB = 0;
for (int i  = 0; i < vars.length; i++) {
sumLB += vars[i].getLB();
}
int F = b - sumLB;
if (F < 0) {
fails();
}
for (int i  = 0; i < vars.length; i++) {
int lb = vars[i].getLB();
int ub = vars[i].getUB();
if (ub - lb > F) {
vars[i].updateUpperBound(F + lb, this);
}
}
}


The parameter of the method is ignored for now. On line 9, since the condition of unsatisfaction is met, a ContradictionException is thrown by calling fails(). On line 16, the $i^{th}$ variable upper bound is updated. If the new value is greater or equal to than the current upper bound of the variable, nothing happens. If not, the variable is modified. If the new upper bound is lesser than the current lower bound, a ContradictionException is thrown automatically. Otherwise, the old upper bound is stored (for future restoration), the new upper bound is set and the propagators’ list of the variable is iterated to inform each of them (except the one that triggers the event) that the variable domain has changed which can question their local fix-point.

### Propagation conditions (optional)

When a variables is modified, the type of event the modification corresponds is declared. For example when the upper bound of a variables is decreased, the event indicates DEC_UPP.

Not all types of event is relevant for all propagators and each of them can give its filtering conditions. By default, a propagator is informed of all type of modifications.

In our case, nothing can be done on value removal nor on upper bound modification. Thus, the following method can be override (note that is optional but leads to better performances):

@Override
public int getPropagationConditions(int vIdx) {
return IntEventType.combine(IntEventType.INSTANTIATE, IntEventType.INCLOW);
}


Note that this method is called statically on each of its variables (denoted by vIdx) when posting the constraint to the model. Some propagators can thus declare distinct propagation conditions for each variable.

### Constructor

Finally, any propagator should extends Propagator which is an abstract class and a call to super is expected as first instruction of the constructor.

Propagator abstract class provides three constructors but we will only depict one, the most important: Propagator(V[] vars, PropagatorPriority priority, boolean reactToFineEvt).

The first argument is the list of variables, here an array of IntVar. The list of all variables the propagator can react on should be passed here. Consider that, with few exceptions, all variables of the propagator are expected.

The second parameter considers the filtering algorithm arity or complexity. There are seven ordered levels of priority, the three first ones (arity levels) are UNARY, BINARY and TERNARY. The three following ones (complexity levels) are LINEAR, QUADRATIC, CUBIC. Actually a TERNARY priority propagator is expected to run faster than a QUADRATIC priority one. So, considering the complexity instead of the arity may be more relevant when the filtering algorithm is very costly even if the propagator relies on only three variables.

The third parameter indicates if the propagator is able to react on fine events. This parameter will be presented in more details later on.

In our case, the input parameters are the array of IntVar ‘x’, the priority is based on the complexity which is linear in the number of variables and false. In addition, the constant ‘b’ needs to be stored too.

/**
* Constructor of the specific sum propagator : x1 + x2 + ... + xn <= b
* @param x array of integer variables
* @param b a constant
*/
public MyPropagator(IntVar[] x, int b) {
super(x, PropagatorPriority.LINEAR, false);
this.b = b;
}


### MyPropagator

A basic yet sound propagator which ensures that the sum of all variables is less than or equal to a constant is declared below.

public class MyPropagator extends Propagator<IntVar> {

/**
* The constant the sum cannot be greater than
*/
final int b;

/**
* Constructor of the specific sum propagator : x1 + x2 + ... + xn <= b
* @param x array of integer variables
* @param b a constant
*/
public MyPropagator(IntVar[] x, int b) {
super(x, PropagatorPriority.LINEAR, false);
this.b = b;
}

@Override
public int getPropagationConditions(int vIdx) {
return IntEventType.combine(IntEventType.INSTANTIATE, IntEventType.INCLOW);
}

@Override
int sumLB = 0;
for (IntVar var : vars) {
sumLB += var.getLB();
}
int F = b - sumLB;
if (F < 0) {
fails();
}
for (IntVar var : vars) {
int lb = var.getLB();
int ub = var.getUB();
if (ub - lb > F) {
var.updateUpperBound(F + lb, this);
}
}
}

@Override
public ESat isEntailed() {
int sumUB = 0, sumLB = 0;
for (IntVar var : vars) {
sumLB += var.getLB();
sumUB += var.getUB();
}
if (sumUB <= b) {
return ESat.TRUE;
}
if (sumLB > b) {
return ESat.FALSE;
}
return ESat.UNDEFINED;
}
}


This first implementation outlines key concepts a propagator required. The entailment method should not ignored since it is helpful (even essential) to check the correctness of the implementation. The optional one which describes the propagation conditions can sometimes reduce the number of times a propagator is called without deducing new information (domain modifications or failure).

## A more complex version

Based on Bounds Consistency Techniques for Long Linear Constraint, the first version can be improved in some ways.

We will consider first to desactivate the propagator when some conditions are satisfied, then we will show how backtrackable structures can be used and finally how a propagator can react to fine events.

### Reduce to silence

An interesting feature available by default is the capacity to set passive a propagator that is entailed (i.e., is always true). Indeed, if all variables domain are in such state that any combinations satisfy the constraint, the propagator can be ignored in the propagation loop since it will not filter values nor fail.

In our case, this happens when the sum of the upper bounds is equal to or less than ‘b’. If so, the propagator can safely be set to a passivate state in which it will not be informed of any new modifications occurring in the current search sub-tree (i.e., the propagator will be reactivated automatically on backtrack).

The filtering method can be modified like that:

@Override
int sumLB = 0;
for (int i  = 0; i < vars.length; i++) {
sumLB += vars[i].getLB();
}
int F = b - sumLB;
if (F < 0) {
fails();
}
int sumUB = 0;
for (int i  = 0; i < vars.length; i++) {
int lb = vars[i].getLB();
int ub = vars[i].getUB();
if (ub - lb > F) {
vars[i].updateUpperBound(F + lb, this);
}
sumUB += vars[i].getUB();
}
int E = sumUB - b;
if (E <= 0) {
this.setPassive();
}
}


Line 18, a counter is updated with the sharpest upper bound of each variables. Line 21-23, if the condition is satisfied, the propagator is entailed and set to a passive state.

### Incrementally updating $F$

One may have noted that F is always computed as first step of propagate(int evtmask) method. On cases where few bounds are updated, there could be a benefit to incrementally compute $F$.

To compute $F$ in an incremental way, three steps are needed:

1. creating a backtrackable int to record $F$ but also variables’ lower bound
2. initializing it on propagate(int evtmask) first call
3. anytime a variable is being modified, maintaining $F$

First, a IStateInt object and an IStateInt array are declared as class variables. In the propagator’s constructor, through the Model, the objects are initialized:

/**
* The constant the sum cannot be greater than
*/
final int b;

/**
* object to store F in an incremental way.
* Corresponds to a backtrackable int.
*/
final IStateInt F;

/**
* array to store variables' previous lower bound.
* each cell is a backtrackable int.
*/
final IStateInt[] prev_lbs;

/**
* Constructor of the specific sum propagator : x1 + x2 + ... + xn <= b
* @param x array of integer variables
* @param b a constant
*/
public MyPropagator(IntVar[] x, int b) {
super(x, PropagatorPriority.LINEAR, false);
this.b = b;
this.F = this.model.getEnvironment().makeInt(0);
this.prev_lbs = new IStateInt[x.length];
for(int i = 0 ; i < x.length; i++){
prev_lbs[i] = this.model.getEnvironment().makeInt(0);
}
}


$F$ is created with value 0; its correct value will be set on the first call to propagate(int evtmask) method. Same goes with prev_ubs. Any backtrackable primitive or operation is created thanks to the environment attached to the model. This ensures the integrity of the structure when backtracks occur.

The role of prev_ubs is to store the value of each variable lower bound. Then, anytime a variable lower bound is modified, its value can be retrieved and substracted from the current value to update $F$.

Second, $F$ is initialized in the first call to propagate(int evtmask) method. This is where the value of evtmask is helpful. It can take 2 distinct values: one is dedicated to a full propagation, the other to a custom propagation. A full propagation is run on the initial propagation call, when each propagator is awaken by the solver. Then, if the propagator was declared not reacting to fine events (last parameter of the super constructor), full propagation is always run. On the other hand, if the propagator reacts to fine events, which will be the case for now, the initial propagation is kept full but then the main entry point of the filtering algorithm will be propagate(int vIdx, int evtmask) method (with two arguments). This method reacts to fine events, that means all variables modifications will be given as input thanks to the variable’s index in vars (vIdx) and the event mask which is can be a combination of event types, like in propagation conditions.

Most of the time, this method is decomposed into a fast but naive filtering algorithm and a delayed call to a custom, presumably not fast, filtering algorithm. But it can be made of no filtering at all (that’s the case here) or no delayed call to custom filtering algorithm.

In our case, we will only incrementally maintain $F$ and then delegate the filtering to the custom propagation.

private void prepare(){
int sumLB = 0;
for(int i = 0 ; i < vars.length; i++){
sumLB += vars[i].getLB();
// set the current lower bound in 'prev_lbs'
prev_lbs[i].set(vars[i].getLB());
}
// set the value of F
F.set(b - sumLB);
}

@Override
// 1. get the current lower bound of the modified variable
int lb = vars[vIdx].getLB();
// 2. update F with the difference between old and new lower bound
// 3. set the new lower bound
prev_lbs[vIdx].set(lb);
// 4. delegate the filtering later on
forcePropagate(PropagatorEventType.CUSTOM_PROPAGATION);
}

@Override
// First call to the filtering algorithm, F is not up-to-date
// so prepare initialize its value and 'prev_lbs'
prepare();
}
if (F.get() < 0) {
fails();
}
for (IntVar var : vars) {
int lb = var.getLB();
int ub = var.getUB();
if (ub - lb > F.get()) {
var.updateUpperBound(F.get() + lb, this);
}
}
}


A call to forcePropagate(int evtmask) will call propagate(int evtmask) only when all fine events are received. This ensures that F is set to the correct value before filtering forbidden values.