退化非线性规划的约束识别与算法稳定性
Constraint Identification and Algorithm Stabilization for Degenerate Nonlinear Programs
AI总结：
针对退化非线性规划，提出区分强弱积极约束的技术，并据此改进序列二次规划算法，在更弱假设下实现超线性收敛。
摘要：
在非线性规划问题的解附近，严格互补性和积极约束的线性无关性可能同时失效。我们描述了一种区分弱积极约束与强积极约束的技术。我们证明，该信息可用于修改序列二次规划算法，使其在比以往分析更弱的假设下，对解表现出超线性收敛。
英文摘要：
In the vicinity of a solution of a nonlinear programming problem at which both strict complementarity and linear independence of the active constraints may fail to hold, we describe a technique for distinguishing weakly active from strongly active constraints. We show that this information can be used to modify the sequential quadratic programming algorithm so that it exhibits superlinear convergence to the solution under assumptions weaker than those made in previous analyses.
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