论文标题
顺序分段线性编程，用于收敛的非凸问题
Sequential Piecewise Linear Programming for Convergent Optimization of Non-Convex Problems
论文作者
论文摘要
在每个算法的每次迭代中，都依次收缩了一个非凸功能函数的有界域的界面域，则呈现一个顺序的分段线性编程方法。尽管不能保证可行性和最佳性，但我们表明该方法能够在许多非线性编程（NLP）（NLP）和混合整数非线性编程（MINLP）问题上获得收敛性和最佳解决方案，仅使用少量的断点和Integer变量。
A sequential piecewise linear programming method is presented where bounded domains of non-convex functions are successively contracted about the solution of a piecewise linear program at each iteration of the algorithm. Although feasibility and optimality are not guaranteed, we show that the method is capable of obtaining convergent and optimal solutions on a number of Nonlinear Programming (NLP) and Mixed Integer Nonlinear Programming (MINLP) problems using only a small number of breakpoints and integer variables.
