Locality-aware Surrogates for Gradient-based Black-box Optimization
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912213056356352 |
|---|---|
| author | Momeni, Ali Uhlich, Stefan Venkitaraman, Arun Hsieh, Chia-Yu Bonetti, Andrea Matsuo, Ryoga Ohbuchi, Eisaku Servadei, Lorenzo |
| author_facet | Momeni, Ali Uhlich, Stefan Venkitaraman, Arun Hsieh, Chia-Yu Bonetti, Andrea Matsuo, Ryoga Ohbuchi, Eisaku Servadei, Lorenzo |
| contents | In physics and engineering, many processes are modeled using non-differentiable black-box simulators, making the optimization of such functions particularly challenging. To address such cases, inspired by the Gradient Theorem, we propose locality-aware surrogate models for active model-based black-box optimization. We first establish a theoretical connection between gradient alignment and the minimization of a Gradient Path Integral Equation (GradPIE) loss, which enforces consistency of the surrogate's gradients in local regions of the design space. Leveraging this theoretical insight, we develop a scalable training algorithm that minimizes the GradPIE loss, enabling both offline and online learning while maintaining computational efficiency. We evaluate our approach on three real-world tasks - spanning automated in silico experiments such as coupled nonlinear oscillators, analog circuits, and optical systems - and demonstrate consistent improvements in optimization efficiency under limited query budgets. Our results offer dependable solutions for both offline and online optimization tasks where reliable gradient estimation is needed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_19161 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Locality-aware Surrogates for Gradient-based Black-box Optimization Momeni, Ali Uhlich, Stefan Venkitaraman, Arun Hsieh, Chia-Yu Bonetti, Andrea Matsuo, Ryoga Ohbuchi, Eisaku Servadei, Lorenzo Machine Learning In physics and engineering, many processes are modeled using non-differentiable black-box simulators, making the optimization of such functions particularly challenging. To address such cases, inspired by the Gradient Theorem, we propose locality-aware surrogate models for active model-based black-box optimization. We first establish a theoretical connection between gradient alignment and the minimization of a Gradient Path Integral Equation (GradPIE) loss, which enforces consistency of the surrogate's gradients in local regions of the design space. Leveraging this theoretical insight, we develop a scalable training algorithm that minimizes the GradPIE loss, enabling both offline and online learning while maintaining computational efficiency. We evaluate our approach on three real-world tasks - spanning automated in silico experiments such as coupled nonlinear oscillators, analog circuits, and optical systems - and demonstrate consistent improvements in optimization efficiency under limited query budgets. Our results offer dependable solutions for both offline and online optimization tasks where reliable gradient estimation is needed. |
| title | Locality-aware Surrogates for Gradient-based Black-box Optimization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.19161 |