Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
Authors: Jialin Li, Marta Zagorowska, Giulia De Pasquale, Alisa Rupenyan, John Lygeros
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that TVSAFEOPT compares favorably against SAFEOPT on synthetic data, both regarding safety and optimality. Evaluation on a realistic case study with gas compressors confirms that TVSAFEOPT ensures safety when solving time-varying optimization problems with unknown reward and safety functions. |
| Researcher Affiliation | Academia | Jialin Li ETH Zürich (currently with UIUC) lijial@ethz.ch Marta Zagorowska NTNU (currently with TU Delft) m.a.zagorowska@tudelft.nl Giulia De Pasquale Eindhoven Univeristy of Technology g.de.pasquale@tue.nl Alisa Rupenyan Zürich University of Applied Sciences alisa.rupenyan@zhaw.ch John Lygeros ETH Zürich jlygeros@ethz.ch |
| Pseudocode | Yes | Algorithm 1 TVSAFEOPT |
| Open Source Code | No | The code accompanying the paper is currently under review and will appear shortly at https://www.research-collection.ethz.ch/. |
| Open Datasets | Yes | The data for the demand, compressor head, and degradation for the three compressors were obtained from [40] (Creative Commons Attribution Non Commercial Licence). [40] Marta Zagorowska. Degradation modelling in process control applications. Ph D thesis, Imperial College London, 2020. available at https://spiral.imperial.ac.uk/handle/10044/1/ 105173, online 22 May 2024. |
| Dataset Splits | No | The paper describes the datasets and problem setups but does not specify explicit training, validation, or test dataset splits in terms of percentages or counts, or by referencing predefined splits. |
| Hardware Specification | Yes | Experiments are conducted on an Intel i7-11370H CPU using Python 3.8.5. |
| Software Dependencies | Yes | The implementation utilizes the following libraries: GPy 1.12.0, NumPy 1.22.0, and Matplotlib 3.5.0. |
| Experiment Setup | Yes | The search space is X = [ 2, 2]2, uniformly quantized into 100 100 points. Both algorithms start with the singleton initial safe set {( 0.5, 0.0)}. The measurements are perturbed by i.i.d. Gaussian noise N(0, 0.012). The reward function is formulated as: f(x, t) = ex2 log(1 + y2) + 0.01t; The safety function is formulated as: c1(x, t) = 1 x + 0.5 0.5 1 cos 2π 6 2 y 0.3 0.5 1 cos 2π. ... σ1 1.0, σ2 = 25.0 for f, and σ2 = 15.0 for c1. SAFEOPT: ... σ3 1.0. ETSAFEOPT: ... sentivity of the event trigger δ as 0.01. |