MIN mode via acquisition function
#340
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AVHopp
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Scienfitz
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This avoids inverting the computational representation, which gives inverted predictions back to the user calling the surrogate.
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This PR fixes a critical bug introduced in #340 that has been present since version `0.10.1` and adds a corresponding test. The bug occurs when using improvement-based Monte Carlo acquisition functions (such as the default `qLogExpectedImprovement`) in with a single numerical target in `MIN` mode. The cause was a missing inversion of the `best_f` reference value.
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This PR changes the behavior of
NumericalTargetinMINmode in that the minimization is lo longer implement by negating the computational representation of the target but placing an objective on the acquisition function.This approach avoids the problem that otherwise the surrogate would be trained on inverted targets, resulting in inverted predictions, which prevents us from exposing the surrogate as-is to the user. Also, it cleanly separates the objective (i.e. "minimization") from the target representation.