Research map / Probabilistic and scientific modeling / Bayesian optimization
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Aggressively optimize EI with L-BFGS-B in the slack-AL early on (ablation) | Slack-AL · LSQ problem, iteration 10 | best valid objective: 0.902 -> 0.957 | Hurt |
| Add L-BFGS-B optimization of EI to the slack-AL at later iterations (ablation) | Slack-AL · LSQ problem, iteration 30 | best valid objective: 0.6010 -> 0.6002 | Helped |
| Replace original AL with slack-variable AL | ALBO · LSQ problem, iteration 30 | best valid objective: 0.689 -> 0.6010 | Helped |
| Slack-AL+optim vs PESC (best vs worst at iteration 10) | ALBO · LSQ problem, iteration 10 | best valid objective: 0.861 -> 0.957 | Hurt |
| Use slack-AL with L-BFGS-B optimized EI versus EFI, nlopt and NOMAD | ALBO · Linear-Hartman-Ackley mixed constraints | best valid objective | Helped |
| Use slack-AL with optimization versus EFI | ALBO · GBSP problem | proportion of valid and optimal runs | Helped |
| Use slack-AL instead of original AL | ALBO · GBSP problem | best valid objective | Helped |
| Default slack choice relying on GP posterior mean under non-smooth constraints | Slack-AL · Lockwood problem | best valid objective | Hurt |
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