Machine learningOptimization
增广拉格朗日方法
增广拉格朗日方法由 Magnus R. Hestenes 和 M. J. D. Powell 于 1969 年提出，是一种用于求解约束优化问题的强大技术。它通过向拉格朗日函数添加二次惩罚项，将约束问题转化为一系列无约束子问题，从而能够高效地求解包括凸和非凸情况在内的大规模问题。
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来源
- Hestenes, M. R. (1969). Multiplier and gradient methods. Journal of Optimization Theory and Applications, 4(5), 303-320. DOI: 10.1007/BF00927673 ↗
- Powell, M. J. D. (1969). A method for nonlinear constraints in minimization problems. In Optimization (pp. 283-298). Academic Press. link ↗
- Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1), 1-122. DOI: 10.1561/2200000016 ↗
如何引用本页
ScholarGate. (2026, June 3). Augmented Lagrangian Method for Constrained Optimization. ScholarGate. https://scholargate.app/zh/operations-research/augmented-lagrangian-method
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