A linear programming-based optimization algorithm for solving nonlinear programming problems

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• 作者：Claus Still ; Tapio Westerlund
• 关键词：Nonlinear programming ; Cutting plane algorithms ; Sequential linear programming
• 刊名：European Journal of Operational Research
• 出版年：2010
• 出版时间：1 February 2010
• 年：2010
• 卷：200
• 期：3
• 页码：658-670
• 全文大小：490 K

文摘

In this paper a linear programming-based optimization algorithm called the Sequential Cutting Plane algorithm is presented. The main features of the algorithm are described, convergence to a Karush–Kuhn–Tucker stationary point is proved and numerical experience on some well-known test sets is showed. The algorithm is based on an earlier version for convex inequality constrained problems, but here the algorithm is extended to general continuously differentiable nonlinear programming problems containing both nonlinear inequality and equality constraints. A comparison with some existing solvers shows that the algorithm is competitive with these solvers. Thus, this new method based on solving linear programming subproblems is a good alternative method for solving nonlinear programming problems efficiently. The algorithm has been used as a subsolver in a mixed integer nonlinear programming algorithm where the linear problems provide lower bounds on the optimal solutions of the nonlinear programming subproblems in the branch and bound tree for convex, inequality constrained problems.