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We consider a class of multi-agent cooperative consensus optimization problems with local nonlinear convex constraints where only those agents connected by an edge can directly communicate, hence, the optimal consensus decision lies in the intersection of these private sets. We develop an asynchronous distributed accelerated primal-dual algorithm to solve the considered problem. The proposed scheme is the first asynchronous method with an optimal convergence guarantee for this class of problems, to the best of our knowledge. In particular, we provide an optimal convergence rate of $$\mathcal O(1/K)$$ for suboptimality, infeasibility, and consensus violation. 

In this paper, we study a class of bilevel optimization problems, also known as simple bilevel optimization, where we minimize a smooth objective function over the optimal solution set of another convex constrained optimization problem. Several iterative methods have been developed for tackling this class of problems. Alas, their convergence guarantees are either asymptotic for the upper-level objective, or the convergence rates are slow and sub-optimal. To address this issue, in this paper, we introduce a novel bilevel optimization method that locally approximates the solution set of the lower-level problem via a cutting plane and then runs a conditional gradient update to decrease the upper-level objective. When the upper-level objective is convex, we show that our method requires $${O}(\max\{1/\epsilon_f,1/\epsilon_g\})$$ iterations to find a solution that is $$\epsilon_f$$-optimal for the upper-level objective and $$\epsilon_g$$-optimal for the lower-level objective. Moreover, when the upper-level objective is non-convex, our method requires $${O}(\max\{1/\epsilon_f^2,1/(\epsilon_f\epsilon_g)\})$$ iterations to find an $$(\epsilon_f,\epsilon_g)$$-optimal solution. We also prove stronger convergence guarantees under the Holderian error bound assumption on the lower-level problem. To the best of our knowledge, our method achieves the best-known iteration complexity for the considered class of bilevel problems.