Constraints over real variables

Choco-solver offers the possibility to combine real variables together into an expression. This is the easiest way to declare constraints over real variables.

Expressions with RealVar

Two types of expressions can be defined with RealVar : arithmetic and relational.

Arithmetic expressions

First any RealVar is an arithmetic expression itself.

Based on one variable x, an arithmetic expression can be built with the following operators :

• x.neg(): returns $-x$,
• x.abs(): returns $|x|$,
• x.add(y): returns $x+y$,
• x.sub(y)returns $x - y$,
• x.mul(y): returns $x\times y$,
• x.div(y): returns $\frac{x}{y}$,
• x.pow(y): returns $x^y$,
• x.min(y): returns $\min(x,y)$,
• x.max(y): returns $\max(x,y)$,
• x.atan2(y): returns $\operatorname{atan2}{(x,y)}$,
• x.exp(): returns $e^x$,
• x.ln(): returns $\ln{(x)}$,
• x.sqr(): returns $x^2$,
• x.sqrt(): returns $\sqrt{x}$,
• x.cub(): returns $x^3$,
• x.cbrt(): returns $\sqrt[3]{x}$,
• x.cos(): returns $\cos{(x)}$,
• x.sin(): returns $\sin{(x)}$,
• x.tan(): returns $\tan{(x)}$,
• x.acos(): returns $\arccos{(x)}$,
• x.asin(): returns $\arcsin{(x)}$,
• x.atan(): returns $\arctan{(x)}$,
• x.cosh(): returns $\cosh{(x)}$,
• x.sinh(): returns $\sinh{(x)}$,
• x.tanh(): returns $\tanh{(x)}$,
• x.acosh(): returns $\operatorname{acosh}{(x)}$,
• x.asinh(): returns $\operatorname{asinh}{(x)}$,
• x.atanh(): returns $\operatorname{atanh}{(x)}$,

Note that y can be either a double or an arithemic expression.

An arithmetic expression can be turned into a RealVar by calling the realVar(prec) method on it. Here, prec is the precision of the variable to return. If necessary, it creates intermediary variable and posts intermediary constraints then returns the resulting variable.

• Java

double p = 0.01d;
RealVar x = model.realVar(1, 5, p);
RealVar y = model.realVar(1, 5, p);
// z = x^(y-2)
RealVar z = x.pow(y.sub(2)).realVar(p);

Relational expressions

Based on an arithmetic expression x, a relational expression can be built using the following operators:

• x.lt(y): states that $x < y$,
• x.le(y): states that $x \leq y$,
• x.gt(y): states that $x > y$,
• x.ge(y): states that $x \geq y$,
• x.eq(y): states that $x = y$.

Note that y can be either an double or an arithemic expression.

A relational expression can be posted into the model as an equation or added to an Ibex instance.

As an equation

Calling equation() on a relation expression will return a Constraint object that embeds a propagator using HC4 algorithm for filtering values based on the equation expressed. It must be then posted or reified. The constraint stores the expression as an internal variable.

A call to this method does not create additional variables and returns a single constraint.

• Java

double p = 0.01d;
RealVar x = model.realVar(1, 5, p);
RealVar y = model.realVar(1, 5, p);
// x / (y-2) <= 1.5
x.div(y.sub(2)).le(1.5).equation().post();

pow, atan2, ln, tan, acos, asin, atan, cosh, sinh, tanh, acosh, asinh, atanh 

Into Ibex

Alternatively, an expression can be added Ibex. This is achieved calling the ibex(prec) method which returns a Constraint object where prec denotes the precision. It must be then posted or reified.

• Java

double p = 0.01d;
RealVar x = model.realVar(1, 5, p);
RealVar y = model.realVar(1, 5, p);
// x / (y-2) >= 1.6
x.div(y.sub(2)).ge(1.6).ibex(p).post();

Other constraints

Equality between a RealVar and an IntVar

It is sometimes relevant to map a real variable to an integer variable. Doing so, the real variable is forced to take integer values but it can be declared in real constraints (either as an equation or in Ibex). This is achieved by posting an eq constraint like this:

• Java

IntVar foo = model.intVar("foo", new int[]{0, 15, 20});
RealVar bar = model.realVar("bar", 0, 20, 1e-5);
model.eq(bar, foo).post();

Binding a real value from an array

It is possible to set the value of a real variable thanks to an array of double values and an integer variable as the position of the value in the array. This relation is also known as an element constraint. All double values in the array must be different and sorted in a increasing order.

• Java

RealVar value = model.realVar("V", 0., 10., 1.e-4);
IntVar index = model.intVar("I", 0, 5);
double[] values = new double[]{-1., .8, Math.PI, 12.};
model.element(value, values, index).post();

Scalar product

A scalar product where coefficients are double values can be defined over a set of integer variables and/or real variables. Available operators are "=", ">=", "<=".

• Java

double[] coeffs = new double[]{1, 5, 7, 8};
RealVar[] vars = model.realVarArray(4, 1., 6., .1);
model.scalar(vars, coeffs, "=", 35).post();