A ranking approach to global optimization
Authors: Cedric Malherbe, Emile Contal, Nicolas Vayatis
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Eventually, numerical evidence is given to show that the main algorithm of the paper which adapts empirically to the underlying ranking structure essentially outperforms existing state-of-the-art global optimization algorithms in typical benchmarks. |
| Researcher Affiliation | Academia | CMLA, ENS Cachan, CNRS, Universit e Paris-Saclay, 94235, Cachan, France |
| Pseudocode | Yes | Algorithm 1 RANKOPT(n, f, X, R) and Algorithm 2 ADARANKOPT(n, f, X, p, {RN}N N ) |
| Open Source Code | No | The paper does not provide concrete access to its own source code, only refers to a third-party library it uses (NLOpt). |
| Open Datasets | No | The algorithms were compared on three synthetic problems. No mention of specific datasets or their public availability beyond these synthetic functions. |
| Dataset Splits | No | The algorithms were compared on three synthetic problems and then describes maximizing/minimizing those functions directly. No explicit mention of train/validation/test splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions the 'NLOpt library (Johnson, 2014)' as a tool used for comparison, but it does not provide specific version numbers for its own software dependencies. |
| Experiment Setup | Yes | The tuning parameters were set to default and the parameter p was set to 1/4 for the convex ranking rules and to 1/10 for the polynomial ranking rules. |