Abstract This package ports 6 constrained multi-objective test problems from BoTorch’s test_functions.multi_objective module to OptunaHub’s BaseProblem interface: BNH, CONSTR, ConstrainedBraninCurrin, OSY, SRN, and MW7. Each problem is reimplemented with plain Python and numpy only, so it can be evaluated without installing torch or botorch.
Conventions Parameters are named x0, x1, … following each problem’s input vector order. evaluate_constraints keys are named c0, c1, … following each problem’s constraint order. BoTorch’s constraint convention is that a slack c_i(x) >= 0 is feasible; OptunaHub’s BaseProblem convention is that a value <= 0 is feasible.
Abstract This package provides the real-world multi-objective optimization benchmark problems (the RE problem suite) introduced in An Easy-to-use Real-world Multi-objective Problem Suite. The original benchmark implementation is available here. This package serves as a wrapper for a re-implementation of the original benchmark, ported to Python from the reference C source code (reproblem.c).
Note that ConstrainedProblem relies on optuna.trial.Trial.set_constraint, which requires Optuna v5.0.0 or newer.
APIs Problem(problem_name: str)
problem_name: The name of an unconstrained benchmark problem.