Constrained Two-step Look-Ahead Bayesian Optimization
Authors: Yunxiang Zhang, Xiangyu Zhang, Peter Frazier
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In numerical experiments, 2-OPT-C typically improves query efficiency by 2x or more over previous methods, and in some cases by 10x or more. |
| Researcher Affiliation | Academia | Yunxiang Zhang Cornell University yz2547@cornell.edu Xiangyu Zhang Cornell University xz556@cornell.edu Peter I. Frazier Cornell University pf98@cornell.edu |
| Pseudocode | Yes | Pseudocode for using 2-OPT-C is provided in the supplement. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | The benchmark problems include three synthetic problems from [1], named P1, P2, and P3, and two real-world problems, portfolio optimization and robot pushing. Detailed descriptions are in the supplement. |
| Dataset Splits | No | The paper describes sampling initial points and the number of function evaluations, but does not provide specific train/validation/test dataset splits for model training. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'GPy [38]' but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | The 2-OPT-C implementation uses GPs with a constant zero-mean prior and ARD square-exponential kernels for both objectives and constraints. GP hyperparameters are obtained by maximizing the marginal likelihood using GPy [38]. For the initialization of each experiment, we randomly sample three points with at least one feasible point from a Latin hypercube design. We run N = 40 function evaluations for P1 and P2, N = 60 for P3, N = 30 for portfolio optimization problem, and N = 50 for robot pushing problem. We use batch size of 1 for all five experiments. |