  In most optimization problems, users have a clear understanding of the
function to optimize (e.g., minimize the makespan for scheduling problems).
However, the constraints may be difficult to state and their modelling often
requires expertise in Constraint Programming. Active constraint acquisition has
been successfully used to support non-experienced users in learning constraint
networks through the generation of a sequence of queries. In this paper, we
propose Learn&Optimize, a method to solve optimization problems with known
objective function and unknown constraint network. It uses an active constraint
acquisition algorithm which learns the unknown constraints and computes
boundaries for the optimal solution during the learning process. As a result,
our method allows users to solve optimization problems without learning the
overall constraint network.
