On Cutting Plane Algorithms for Nonlinear Binary Optimization
提出一种基于割平面的通用方法，用于求解非线性甚至非凸的二元优化问题，并给出严格的收敛性分析和最优性条件。
Current state-of-the-art methods for solving discrete optimization problems are usually restricted to convex settings. In this paper, we propose a general approach based on cutting planes for solving nonlinear, possibly nonconvex, binary optimization problems. We provide a rigorous convergence analysis that quantifies the number of iterations required under different conditions. This is different to most other work in discrete optimization where only finite convergence is proved. Moreover, using tools from variational analysis, we provide necessary and sufficient dual optimality conditions.