Jean-François BAFFIER (azzaare@github)

In many fields of optimization, there is often a tradeoff between efficiency and the simplicity of the model. *ConstraintLearning.jl* is an interface to several tools designed to smooth that tradeoff.
- *CompositionalNetworks.jl*: a scaling glass-box method to learn highly combinatorial functions [JuliaCon 2021]
- *QUBOConstraints.jl*: a package to automatically learn QUBO matrices from optimization constraints.

Applications are not limited to Constraint Programming, but are focused on it.

In Constraint Programming, a problem can be modeled as simply as
- a set of variables' domains
- a set of predicates over those variables called constraints
- an optional objective

Often, efficient solvers expect more complex models to provide additional efficiency. For instance, Constraint-Based Local Search (CBLS) solvers have significant speedups when the constraint is encoded as a more refined function than a predicate. We designed *CompositionalNetworks.jl* to learn those functions from simple predicates, effectively removing the modeling complexity.

Similarly, we designed *QUBOConstraints.jl* such that QUBO matrices are learned from simple predicates. Among other things, QUBO encoding can be used on QUBO based solvers and quantum annealing machines.

Finally, **ConstraintLearning.jl** provides a common interface for both learning techniques. It also effectively allows both packages to only contains minimal data structures and generic solving interfaces to be including in appropriate solvers.

Jean-François BAFFIER (azzaare@github)

Talks:

ConstraintLearning: Ever wanted to learn more about constraints?

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