Constrained Bayesian Optimization Benchmarks and Applications

282.7 KB of data supporting a novel Bayesian optimization method for handling constraints. The dataset, authored by Raju Chowdhury and last updated in March 2026, includes results from four benchmark problems and applications in groundwater remediation and neural network hyperparameter tuning. The data is shared under a CC-BY-4.0 license on figshare.

Use Cases

• Benchmarking new constrained optimization algorithms based on the four included benchmark problems.
• Applying the proposed feasibility-infeasibility weighted improvement criterion to groundwater remediation design.
• Tuning hyperparameters for neural networks under constraints using the provided framework.

Strengths

• Data is associated with a peer-reviewed methodological article introducing a novel acquisition function.
• Includes results from four distinct benchmark problems for validation.
• Applies the method to two concrete domains: hydrology and machine learning.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 282.7 KB size suggests a limited scope, likely containing summary results rather than raw experimental data.

Provenance

Source

figshare, authored by Raju Chowdhury.

Collection Method

Likely contains experimental results from benchmark simulations and applied case studies.

Freshness

Last updated 2026-03-17 22:45:32; freshness should be verified.