On the Complexity of First-Order Methods in Stochastic Bilevel Optimization

التفاصيل البيبلوغرافية
العنوان: On the Complexity of First-Order Methods in Stochastic Bilevel Optimization
المؤلفون: Kwon, Jeongyeol, Kwon, Dohyun, Lyu, Hanbaek
سنة النشر: 2024
المجموعة: Computer Science
Mathematics
مصطلحات موضوعية: Mathematics - Optimization and Control, Computer Science - Machine Learning
الوصف: We consider the problem of finding stationary points in Bilevel optimization when the lower-level problem is unconstrained and strongly convex. The problem has been extensively studied in recent years; the main technical challenge is to keep track of lower-level solutions $y^*(x)$ in response to the changes in the upper-level variables $x$. Subsequently, all existing approaches tie their analyses to a genie algorithm that knows lower-level solutions and, therefore, need not query any points far from them. We consider a dual question to such approaches: suppose we have an oracle, which we call $y^*$-aware, that returns an $O(\epsilon)$-estimate of the lower-level solution, in addition to first-order gradient estimators {\it locally unbiased} within the $\Theta(\epsilon)$-ball around $y^*(x)$. We study the complexity of finding stationary points with such an $y^*$-aware oracle: we propose a simple first-order method that converges to an $\epsilon$ stationary point using $O(\epsilon^{-6}), O(\epsilon^{-4})$ access to first-order $y^*$-aware oracles. Our upper bounds also apply to standard unbiased first-order oracles, improving the best-known complexity of first-order methods by $O(\epsilon)$ with minimal assumptions. We then provide the matching $\Omega(\epsilon^{-6})$, $\Omega(\epsilon^{-4})$ lower bounds without and with an additional smoothness assumption on $y^*$-aware oracles, respectively. Our results imply that any approach that simulates an algorithm with an $y^*$-aware oracle must suffer the same lower bounds.
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2402.07101
رقم الأكسشن: edsarx.2402.07101
قاعدة البيانات: arXiv