Lookahead Bayesian Optimization with Inequality Constraints
Authors: Remi Lam, Karen Willcox
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present numerical experiments demonstrating the performance improvements of such a lookahead approach compared to several greedy BO algorithms, including constrained expected improvement (EIC) and predictive entropy search with constraint (PESC). |
| Researcher Affiliation | Academia | Remi R. Lam Massachusetts Institute of Technology Cambridge, MA rlam@mit.edu Karen E. Willcox Massachusetts Institute of Technology Cambridge, MA kwillcox@mit.edu |
| Pseudocode | Yes | Algorithm 1 Constrained Bayesian Optimization; Algorithm 2 Rollout Utility Function |
| Open Source Code | No | The paper mentions using the 'Spearmint package' and provides a link to its repository, but it does not provide a link or state that the authors are releasing the source code for their own proposed methodology. |
| Open Datasets | No | The paper evaluates on analytic functions (P1-P3) and a reacting flow model (P4), which are not traditional publicly available datasets with access information. For P4, it cites a paper about the model, not an explicit dataset. |
| Dataset Splits | No | The paper discusses evaluation budget and iterations for the optimization process but does not specify training, validation, or test dataset splits typical for machine learning tasks. |
| Hardware Specification | No | The paper mentions that 'solving large systems of PDEs can take over a day on a supercomputer' as a general statement about problem complexity, but it does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments reported in the paper. |
| Software Dependencies | No | The paper mentions using the 'Spearmint package' but does not specify a version number for it or any other software dependencies crucial for reproducibility. |
| Experiment Setup | Yes | For the rollout algorithm, we use independent zero-mean GPs with automatic relevance determination (ARD) square-exponential kernel to model each expensive-to-evaluate function. ... To compute the expectations of Eqs. 11-12, we employ Nq = 3I+1 Gauss-Hermite quadrature weights and points and we set the discount factor to γ = 0.9. Finally, at iteration n, the best value f Sn best is set to the minimum posterior mean µn(x; f) over the designs x in the training set Sn, such that the posterior mean of each constraint is feasible. ... For P1 and P2, we use N = 40 evaluations ... For P3 and P4, we use a small number of iterations N = 60 |