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An algorithm for nonlinear optimization using linear programming and equality constrained subproblems

机译:线性规划和等式约束子问题的非线性优化算法

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摘要

This paper describes an active-set algorithm for large-scale nonlinear programming based on the successive linear programming method proposed by Fletcher and Sainz de la Maza [10]. The step computation is performed in two stages. In the first stage a linear program is solved to estimate the active set at the solution. The linear program is obtained by making a linear approximation to the ℓ1 penalty function inside a trust region. In the second stage, an equality constrained quadratic program (EQP) is solved involving only those constraints that are active at the solution of the linear program. The EQP incorporates a trust-region constraint and is solved (inexactly) by means of a projected conjugate gradient method. Numerical experiments are presented illustrating the performance of the algorithm on the CUTEr [1, 15] test set.
机译:本文描述了一种基于Fletcher和Sainz de la Maza [10]提出的连续线性规划方法的大规模非线性规划的有效集算法。分两步进行计算。在第一阶段,求解线性程序以估计解决方案中的有效集。线性程序是通过对信任区域内的> 1 罚函数进行线性近似而获得的。在第二阶段,求解等式约束二次程序(EQP),其中仅包含在线性程序解中有效的那些约束。 EQP合并了信任区域约束,并通过投影的共轭梯度法求解(不精确地)。数值实验表明,该算法在CUTEr [1,15]测试集上的性能。

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  • 来源
    《Mathematical Programming》 |2004年第1期|27-48|共22页
  • 作者单位

    Department of Computer Science University of Colorado;

    Computational Science and Engineering Department Rutherford Appleton Laboratory;

    Department of Electrical and Computer Engineering Northwestern University;

    Department of Electrical and Computer Engineering Northwestern University;

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  • 正文语种 eng
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