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arXiv 1806.03347math.NAcs.NA
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内点法与修改后的增广拉格朗日方法用于惩罚-障碍非线性规划

Interior Point Method with Modified Augmented Lagrangian for Penalty-Barrier Nonlinear Programming

Martin Neuenhofen

AI总结:

本文提出一种数值方法用于非线性规划问题的局部求解,通过改进的增广拉格朗日方法处理大惩罚项和不良标度的对数障碍项,证明了方法的全局收敛性和局部二次收敛性。

摘要:

我们提出了一种数值方法,用于非线性规划问题的局部求解。Fiacco和McCormick的SUMT方法产生了一个具有二次惩罚和对数障碍的效用函数。我们的NLP求解器通过直接最小化这个效用函数来工作。在我们的方法中,我们使用不同的概念,每个概念旨在高效处理该效用函数的相应特殊特征。这些特征是:大的二次惩罚项和不良标度的对数障碍项。二次惩罚通过改进的增广拉格朗日技术处理,这使得在等式约束非线性的情况下也能实现大步长。对数障碍项通过对偶内点路径跟随技术处理。我们证明了该方法的全局收敛性和局部二次收敛性。此外,在非线性规划为线性规划的特殊情况下,我们进一步证明了弱多项式时间复杂性。我们还使用了信任函数,以避免方法收敛到任何不满足约束的 stationary 点。

英文摘要:

We present a numerical method for the local solution of nonlinear programming problems. The SUMT approach of Fiacco and McCormick results in a merit function with quadratic penalties and logarithmic barriers. Our NLP solver works by directly minimizing this merit function. In our method, we use different concepts that each shall aim at the efficient treatment of one respective special feature of this merit function. The features are: large quadratic penalty terms, and badly scaled logarithmic barriers. The quadratic penalties are treated with a modified Augmented Lagrangian technique. It enables large step sizes despite nonlinearity of the equality constraints. The logarithmic barriers we treat with a primal-dual interior-point path-following technique. We prove global convergence of the method and local quadratic convergence. We further prove weak polynomial time-complexity in the special case where the nonlinear program is a linear program. We also use a trust-funnel so to avoid that the method converges to any stationary points that are infeasible to the constraints.

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