# Uncertain Parameters and Uncertainty Constraints

Uncertain parameters

Uncertain parameters are modeled in AIMMS as numeric Parameters for which the Uncertain property has been set (see also Parameter Declaration and Attributes). When a parameter has been declared Uncertain AIMMS will create two new attributes Region and Uncertainty.

The Region attribute

The Region attribute of an uncertain parameter offers an easy way to define the uncertainty set without the need to introduce additional uncertain parameters. AIMMS supports a number of predefined regions which you can enter here:

• $${\texttt{Box}}(l,u)$$,

• $${\texttt{Ellipsoid}}(c,r)$$, and

• $${\texttt{ConvexHull}}(s, v(s))$$.

Box example

If we want to specify that parameter A is uncertain and constrained as follows:

$l(i,j) \leq A(i,j) \leq u(i,j)$

then it suffices to specify the uncertainty set of A using its Region attribute as follows

Parameter A {
IndexDomain  : (i,j);
Property     : Uncertain;
Region       : Box( l(i,j), u(i,j) );
}


where l(i,j) and u(i,j) are ordinary parameters in your model.

Ellipsoid example

It is also possible to specify the region using an Ellipsoid region

Parameter A {
IndexDomain  : (i,j);
Property     : Uncertain;
Region       : Ellipsoid( A.level(i,j), r(i,j) );
}


which leads to an uncertainty set for A defined as an ellipsoid around the nominal value of A as follows:

$\sum_{i,j} \Bigg( \frac{A(i,j) - A.\mathit{level}(i,j)}{r(i,j)} \Bigg)^2 \leq 1.$

ConvexHull example

The region can also be defined as a ConvexHull region

Parameter A {
IndexDomain  : (i,j);
Property     : Uncertain;
Region          : ConvexHull( s, A_s(s,i,j) );
}


which says that the uncertain parameter A belongs to an uncertainty set that is described by the convex hull of the values of a collection of values A_s for a given set of scenarios, i.e.,

\begin{split}\begin{align} A(i,j) &= \sum_s \lambda_s A_s(s,i,j)\\ 1 &=\sum_s \lambda_s, \qquad \lambda_s \geq 0. \end{align}\end{split}

Dependencies

If there are two parameters A and B that both depend on scenario-dependent data, then those scenarios can either be dependent or independent. To differentiate between these two possibilities, AIMMS uses the name of the binding index used in the ConvexHull operator. If the names of the binding indices are identical, then AIMMS assumes that the scenarios are dependent. If the index names are different, even if they refer to the same scenario set, AIMMS assumes the scenarios to be independent.

Dependent scenarios example

Consider the following two declarations of uncertain parameters

Parameter A {
IndexDomain  :  (i,j);
Property     :  Uncertain;
Region       :  ConvexHull( s, A_s(s,i,j) );
}
Parameter B {
IndexDomain  :  (i,j);
Property     :  Uncertain;
Region       :  ConvexHull( s, B_s(s,i,j) );
}


Based on these declarations AIMMS will generate a single convex hull as follows

\begin{split}\begin{align} \begin{bmatrix}A(i,j)\\B(i,j)\end{bmatrix} = &\sum_s \lambda_s \begin{bmatrix}A_s(s,i,j)\\B_s(s,i,j)\end{bmatrix}\\ \sum_s \lambda_s =& 1, \qquad \lambda_s \geq 0. \end{align}\end{split}

If A and B consist of a single value each, and there are two scenarios for s, then the combined convex hull for A and B is depicted in Fig. 7.

Independent scenarios example

If, on the other hand, both declarations are given as

Parameter A {
IndexDomain  :  (i,j);
Property     :  Uncertain;
Region       :  ConvexHull( s, A_s(s,i,j) );
}
Parameter B {
IndexDomain  :  (i,j);
Property     :  Uncertain;
Region       :  ConvexHull( t, B_t(t,i,j) );
}


then AIMMS will generate two separate convex hulls as follows

\begin{split}\begin{align} \begin{bmatrix}A(i,j)\\B(i,j)\end{bmatrix} = & \begin{bmatrix}\sum_{s}\lambda_s A_s(s,i,j)\\\sum_{t}\mu_t B_t(t,i,j)\end{bmatrix}\\ \sum_s \lambda_s=&\sum_t \mu_t = 1, \qquad \lambda_s \geq 0, \mu_t \geq 0. \end{align}\end{split}

If A and B consist of a single value each, and there are two scenarios for s and t each, then the combined convex hull for A and B is depicted in Fig. 8.

ConvexHullEx

The ConvexHull operator AIMMS can be used to express that an uncertain parameter is defined as the convex combination of a certain parameter on some set of scenarios. The ConvexHullEx operator is an extension for which the user explicitly has to define the “lambda” parameter as an uncertain parameter. For example:

Parameter A {
IndexDomain  : (i,j);
Property     : Uncertain;
Region          : ConvexHullEx( s, A_s(s,i,j), L(s,i) );
}


which says that the uncertain parameter A belongs to an uncertainty set that is described by the convex hull of the values of a collection of values A_s for a given set of scenarios using the uncertain parameter L, i.e.,

\begin{split}\begin{align} A(i,j) &= \sum_s L_s(i) A_s(s,i,j)\\ 1 &=\sum_s L_s(i), \qquad L_s(i) \geq 0. \end{align}\end{split}

More flexibility

The ConvexHullEx operator offers more flexibility as demonstrated by the above example in which the lambda parameter L depends on the indices s and i while the implicitly generated lambda parameter in case of the ConvexHull operator only depends on the index s. Moreover, the lambda parameter can be used in the Dependency attribute of an adjustable variable (see Adjustable Variables). The same lambda parameter can be used in ConvexHullEx in regions of different uncertain parameters to define a dependency between the uncertain parameters. As the lambda parameter is not an ordinary uncertainty parameter, it cannot be used in uncertainty constraints.

The Uncertainty attribute

Through the Uncertainty attribute of an uncertain parameter you can define a relation in term of other ordinary and uncertain parameters in your model which must hold for the uncertain value of that parameter.

Example

Consider the following declaration

Parameter Demand {
IndexDomain  : (c,t);
Property     : Uncertain;
Uncertainty  : Demand.level(c,t) + Sum[k, D(c,t,k) * xi(k)];
}


where D(c,t,k) is an ordinary parameter and xi an uncertain parameter. The reference to Demand.level in the Uncertainty attribute refers to the deterministic (or nominal) value of Demand. The uncertain value of Demand is defined as its nominal value plus a linear combination of some other uncertain parameter xi(k).

Non-exclusive attributes

Note that the Region and Uncertainty attributes are non-exclusive, i.e., you can use them in conjuction to each other. In such a case, AIMMS will make sure that the solution is robust with respect to both relations.

Uncertainty constraints

The Region and the Uncertainty attribute of a uncertain parameter can be used to specify possible realizations of the uncertain parameters. In some cases, however, more flexibility is needed in specifying special relations for one or more uncertain parameters. For this purpose AIMMS allows you to specify UncertaintyConstraints. An UncertaintyConstraint is a constraint that specifies the relation between uncertain parameters. It is similar to an ordinary constraint in which the uncertain parameters play the role for variables; the definition of an UncertaintyConstraint may only refer to normal and uncertain parameters, and not to variables.

Example

The following example specifies a condition on an uncertain parameter that cannot be expressed through its Region or Uncertainty attributes.

Parameter A {
IndexDomain  : (i,j);
Property     : Uncertain;
}
UncertaintyConstraint ConditionOnA {
IndexDomain  : i;
Definition   : Sum( j, A(i,j) ) <= 1;
}


The Constraints attribute

Through the Constraint attribute of an UncertaintyConstraint you can specify to which (normal) constraints the UncertaintyConstraint should apply. In this way it is possible to use different uncertainty sets for different constraints. If the Constraints attribute is empty then the UncertaintyConstraint will be active for all constraints.

Example

Consider the following declarations

UncertaintyConstraint ConditionOnA {
IndexDomain  : i;
Constraints  : CapacityRestriction(j) : UncertaintyDependency(i,j);
Definition   : Sum( j, A(i,j) ) <= 1;
}
Constraint CapacityRestriction {
IndexDomain  : j;
Definition   : Sum( i, A(i,j) * Transport(i,j) ) <= Capacity(j);
}
Parameter UncertaintyDependency {
IndexDomain  : (i,j);
Definition   : 1 \$ (i = j);
}


These declarations yield that the uncertainty constraint ConditionOnA(i) is only active for constraint CapacityRestriction(j) for all elements j equal to i.

Generalized ellipsoid

Besides linear uncertainty constraints, AIMMS also allows you to formulate the following uncertainty set for a uncertain parameter $$\xi$$, that generalizes the ellipsoidal uncertainty sets that can be defined by using the Ellipsoid region:

$\xi^T Q_0 \xi + \sum_{m=1}^{M} \sqrt{\xi^T Q_m \xi} \le b,$

where $$Q_0$$ and $$Q_m$$ should be positive semidefinite matrices. If your model contains an ellipsoidal uncertainty constraint then the robust counterpart will become a second-order cone program, except if the ellipsoidal uncertainty constraints are of the form

$\sum_i \sqrt{\xi_i^2} \le b,$

in which case the robust counterpart will be a linear program.