Research map / Learning theory and algorithms / Combinatorial and search optimization
Constrained Efficient Global Optimization of Expensive Black-box Functions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LCB surrogates for objective and constraints in auxiliary problem (optimism) instead of penalty or safe sets | CONFIG · Sampled GP instances | cumulative regret and violation | Helped |
| Replace fixed penalty / safe set with optimistic feasible-set LCB approach | constrained GP optimization · Artificial numerical problems P3, P4 | constrained regret | Hurt |
| Declare infeasibility when min LCB of a constraint exceeds zero | CONFIG · Infeasible sampled GP instances | steps to declare infeasibility | Helped |
| Set beta_{i,t} to a constant 3 rather than theoretical schedule | CONFIG · Sampled GP instances and building tuning | qualitative performance | No clear effect |
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