An ADMM-based SQP method for separably smooth nonconvex optimization

[摘要] This work is about a splitting approach for solving separably smooth nonconvex linearly constrained optimization problems. Based on the ideas from two classical methods, namely the sequential quadratic programming (SQP) and the alternating direction method of multipliers (ADMM), we propose an ADMM-based SQP method. We focus on decomposing the quadratic programming (QP) subproblem of the primal problem into small-scale QP subproblems, which further embedded with Bregman distances can be solved effectively and followed by a dual ascent type update for the Lagrangian multipliers. Under suitable conditions as well as the crucial Kurdyka–Łojasiewicz property, we establish the global and strong convergence properties of the proposed method.

[学科分类] 电力

[关键词] Nonconvex optimization;SQP;ADMM;Kurdyka–Łojasiewicz property;Convergence