Sample-and-Bound for Non-convex Optimization
Authors: Yaoguang Zhai, Zhizhen Qin, Sicun Gao
AAAI 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed algorithms on high-dimensional nonconvex optimization benchmarks against competitive baselines and analyze the effects of the hyper parameters. |
| Researcher Affiliation | Academia | University of California, San Diego EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | The pseudocode of MCIR is provided in Alg. 1 |
| Open Source Code | No | No explicit statement or link to open-source code for the described methodology was found. |
| Open Datasets | Yes | To evaluate the performance of our algorithms, our benchmark sets include three distinct categories: synthetic functions designed for nonlinear optimization, bound-constrained non-convex global optimization problems derived from real-world scenarios, and neural networks fitted for single valued functions. [...] Synthetic functions are widely-used in nonlinear optimization benchmarks (Lavezzi, Guye, and Ciarci a 2022). These functions usually have numerous local minima, valleys, and ridges in their landscapes which is hard for normal optimization algorithms. In our tests, we choose three functions: Levy, Ackley, and Michalewicz [...] For our evaluation of non-convex global optimization problems in various fields, we select bound-constrained problems from the collection presented in (The Optimization Firm 2023; Puranik and Sahinidis 2017) that do not involve any additional inequality or equality constraints. |
| Dataset Splits | No | The paper uses benchmark functions but does not explicitly provide specific train/validation/test dataset splits, percentages, or sample counts. |
| Hardware Specification | Yes | We conduct our experiments on a local machine with Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz, 16G RAM, and NVIDIA Ge Force GTX 1080 graphic card. |
| Software Dependencies | Yes | Gurobi (Gurobi Optimization 2023) is a widely used commercial optimization solver [...] CMA-ES/pycma: r3.3.0 |
| Experiment Setup | Yes | In this formula, Clb, Cv and Cx are weights for the function s lower bound, the volume of the box, and visitationbased exploration, respectively. [...] In most cases we cap the number of iterations at fewer than 50, as we do not want to overemphasize the choice of the local optimizer. |