Convergent First-Order Methods for Bi-level Optimization and Stackelberg Games
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We propose an algorithm to solve a class of bi-level optimization problems using only first-order information. In particular, we focus on a class where the inner minimization has unique solutions. Unlike contemporary algorithms, our algorithm does not require the use of an oracle estimator for the gradient of the bi-level objective or an approximate solver for the inner problem. Instead, we alternate between descending on the inner problem using naïve optimization methods and descending on the upper-level objective function using specially constructed gradient estimators. We provide non-asymptotic convergence rates to stationary points of the bi-level objective in the absence of convexity of the closed-loop function and further show asymptotic convergence to only local minima of the bi-level problem. The approach is inspired by ideas from the literature on two-timescale stochastic approximation algorithms.
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