An Inner-Outer Approximation Approach to Chance Constrained Optimization
针对难以求解的非线性机会约束优化问题，提出一种光滑逼近方法，通过内外两层解析逼近将原问题转化为两个可求解的非线性规划问题，内层解保证原问题可行，内外解均渐近收敛于原问题最优解。
Nonlinear chance constrained optimization (CCOPT) problems are known to be difficult to solve. This work proposes a smooth approximation approach consisting of an inner and an outer analytic approximation of chance constraints. In this way, CCOPT is approximated by two parametric nonlinear programming (NLP) problems which can be readily solved by an NLP solver. Any optimal solution of the inner approximation problem is a priori feasible to the CCOPT. The solutions of the inner and outer problems, respectively, converge asymptotically to the optimal solution of the CCOPT.