Projection-based curve pattern search for black-box optimization over smooth convex sets
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912996833361920 |
|---|---|
| author | Jia, Xiaoxi Lapucci, Matteo Mansueto, Pierluigi |
| author_facet | Jia, Xiaoxi Lapucci, Matteo Mansueto, Pierluigi |
| contents | In this paper, we deal with the problem of optimizing a black-box smooth function over a full-dimensional smooth convex set. We study sets of feasible curves that allow to properly characterize stationarity of a solution and possibly carry out sound backtracking curvilinear searches. We then propose a general pattern search algorithmic framework that exploits curves of this type to carry out poll steps and for which we prove properties of asymptotic convergence to stationary points. We particularly point out that the proposed framework covers the case where search curves are arcs induced by the Euclidean projection of coordinate directions. The method is finally proved to arguably be superior, on smooth problems, than other recent projection-based algorithms and is competitive with state-of-the-art methods from the literature on constrained black-box optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20616 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Projection-based curve pattern search for black-box optimization over smooth convex sets Jia, Xiaoxi Lapucci, Matteo Mansueto, Pierluigi Optimization and Control 90C56, 90C30, 90C26 In this paper, we deal with the problem of optimizing a black-box smooth function over a full-dimensional smooth convex set. We study sets of feasible curves that allow to properly characterize stationarity of a solution and possibly carry out sound backtracking curvilinear searches. We then propose a general pattern search algorithmic framework that exploits curves of this type to carry out poll steps and for which we prove properties of asymptotic convergence to stationary points. We particularly point out that the proposed framework covers the case where search curves are arcs induced by the Euclidean projection of coordinate directions. The method is finally proved to arguably be superior, on smooth problems, than other recent projection-based algorithms and is competitive with state-of-the-art methods from the literature on constrained black-box optimization. |
| title | Projection-based curve pattern search for black-box optimization over smooth convex sets |
| topic | Optimization and Control 90C56, 90C30, 90C26 |
| url | https://arxiv.org/abs/2503.20616 |