A learning-based mathematical programming formulation for the automatic
  configuration of optimization solvers
Gabriele Iommazzo Claudia D'Ambrosio Antonio Frangioni Leo Liberti
We propose a methodology, based on machine learning and optimization, for
selecting a solver configuration for a given instance. First, we employ a set
of solved instances and configurations in order to learn a performance function
of the solver. Secondly, we formulate a mixed-integer nonlinear program where
the objective/constraints explicitly encode the learnt information, and which
we solve, upon the arrival of an unknown instance, to find the best solver
configuration for that instance, based on the performance function. The main
novelty of our approach lies in the fact that the configuration set search
problem is formulated as a mathematical program, which allows us to a) enforce
hard dependence and compatibility constraints on the configurations, and b)
solve it efficiently with off-the-shelf optimization tools.