Nonlinear Optimization by Successive Linear Programming
介绍逐次线性规划（SLP）算法，一种通过一系列线性规划求解非线性优化问题的方法，报告了其优于GRG2和MINOS/GRG的计算结果，并提供了算法细节和收敛性证明。
Successive Linear Programming (SLP), which is also known as the Method of Approximation Programming, solves nonlinear optimization problems via a sequence of linear programs. This paper reports on promising computational results with SLP that contrast with the poor performance indicated by previously published comparative tests. The paper provides a detailed description of an efficient, reliable SLP algorithm along with a convergence theorem for linearly constrained problems and extensive computational results. It also discusses several alternative strategies for implementing SLP. The computational results show that SLP compares favorably with the Generalized Reduced Gradient Code GRG2 and with MINOS/GRG. It appears that SLP will be most successful when applied to large problems with low degrees of freedom.