# Introduction to Domain Reduction

Consider the following problem:
\begin{aligned} P:= \min_{x_1,x_2} & f(x_1,x_2) \\ \text{s.t. } & x_1x_2 \leq 1,\\ & -10x_1-x_2 \leq -6,\\ & x_1 \in [0,1.5],\\ & x_2 \in [1,4]. \end{aligned}
The feasible region for this problem corresponds to the red area in Figure 1 (denoted as $$\Omega$$).

Notice that although the range of the variable $$x_1$$ is originally $$[0,1.5]$$, the feasible region in the figure only admits values of $$x_1$$ between $$0.2$$ and $$1$$ (red dashed lines in Figure 1). Knowing this, it is then logical to change the bounds of $$x$$ in the original problem from $$x_1 \in [0,1.5]$$ to $$x_1 \in [0.2,1]$$ (i.e., tightening the bounds of $$x_1$$).

This change will make other calculations and techniques used by Octeract Engine more effective (e.g., relaxations and branching).

Figure 1.

In general, for a problem $$\min_{x \in \Omega} f(x)$$ we would like to find the tightest bounds for each variable $$x_i$$, i.e., find the lower bound $$l_i^\star = \min_{x \in \Omega} x_i$$, and upper bound $$u_i^\star = \max_{x \in \Omega} x_i$$.

However, unlike our previous example, it is not easy to solve the problems $$\min_{x \in \Omega} x_i$$ and $$\max_{x \in \Omega} x_i$$, and a different approach is required. Feasibility based bounds tightening (FBBT) , Optimisation based bounds tightening (OBBT) and Constraint propagation (CP), are the algorithmic techniques used by Octeract Engine to approach this important task.