Near-Optimal Convex Simple Bilevel Optimization with a Bisection Method

This paper studies a class of simple bilevel optimization problems where we minimize a composite convex function at the upper-level subject to a composite convex lower-level problem.Existing methods either provide asymptotic guarantees for the upper-level objective or attain slow sublinear convergence rates.We propose a bisection algorithm to find a solution that is $\epsilon_f$-optimal for the upper-level objective and $\epsilon_g$-optimal for the lower-level objective.In each iteration, the binary search narrows the interval by assessing inequality system feasibility.Under mild conditions, the total operation complexity of our method is ${{\mathcal{O}}}\left(\max\{\sqrt{L_{f_1}/\epsilon_f},\sqrt{L_{g_1}/\epsilon_g}\} \right)$.Here, a unit operation can be a function evaluation, gradient evaluation, or the invocation of the proximal mapping, $L_{f_1}$ and $L_{g_1}$ are the Lipschitz constants of the upper- and lower-level objectives' smooth components, and ${\mathcal{O}}$ hides logarithmic terms.Our approach achieves a near-optimal rate in unconstrained smooth or composite convex optimization when disregarding logarithmic terms.Numerical experiments demonstrate the effectiveness of our method.