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• W2003171289 abstract "We present an approach for nonlinear programming based on the direct minimization of an exact differentiable penalty function using trust-region Newton techniques. The approach provides desirable features required for scalability: it can detect and exploit directions of negative curvature, it is superlinearly convergent, and it enables the scalable computation of the Newton step through iterative linear algebra. Moreover, it presents features that are desirable for parametric optimization problems that must be solved in a latency-limited environment, as is the case for model predictive control and mixed-integer nonlinear programming. These features are fast detection of activity, efficient warm starting, and progress on a primal-dual merit function at every iteration. We note that other algorithmic approaches fail to satisfy at least one of these features. We derive general convergence results for our approach and demonstrate its behavior through numerical studies." @default.
• W2003171289 created "2016-06-24" @default.
• W2003171289 creator A5000943506 @default.
• W2003171289 creator A5083590669 @default.
• W2003171289 date "2014-01-01" @default.
• W2003171289 modified "2023-10-12" @default.
• W2003171289 title "Scalable Nonlinear Programming via Exact Differentiable Penalty Functions and Trust-Region Newton Methods" @default.
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• W2003171289 doi "https://doi.org/10.1137/120888181" @default.
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