An Accelerated Gradient Method for Simple Bilevel Optimization with Convex Lower-level Problem

In this paper, we focus on simple bilevel optimization problems, where we minimize a convex smooth objective function over the optimal solution set of another convex smooth constrained optimization problem. We present a novel bilevel optimization method that locally approximates the solution set of the lower-level problem using a cutting plane approach and employs an accelerated gradient-based update to reduce the upper-level objective function over the approximated solution set. We measure the performance of our method in terms of suboptimality and infeasibility errors and provide non-asymptotic convergence guarantees for both error criteria. Specifically, when the feasible set is compact, we show that our method requires at most O(max\1/√εf, 1/εg\) iterations to find a solution that is εf-suboptimal and εg-infeasible. Moreover, under the additional assumption that the lower-level objective satisfies the r-th H\"olderian error bound, we show that our method achieves an iteration complexity of O(max\εf-2r-1/2r,εg-2r-1/2r\), which matches the optimal complexity of single-level convex constrained optimization when $r=1$.