A new norm-relaxed SQP algorithm with global convergence
A new norm-relaxed SQP algorithm with global convergence
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DOI：
10.1016/j.aml.2010.02.005
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发表时间：
2010-06
期刊：
影响因子：
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通讯作者：
Hai-Yan Zheng;J. Jian;Chunming Tang;Ran Quan
中科院分区：
文献类型：
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作者：
Hai-Yan Zheng;J. Jian;Chunming Tang;Ran Quan
对不等式约束问题提出了一种新的具有全局收敛性的范数松弛序列二次规划算法，每次迭代都能求解二次规划子问题。在不对迭代序列作有界性假设的情况下，在适当的条件下，通过带l∞罚函数的线搜索，证明了算法的全局收敛性.
A new norm-relaxed sequential quadratic programming algorithm with global convergence for inequality constrained problem is presented in this paper, and the quadratic programming subproblem can be solved at each iteration. Without the boundedness assumptions on any of the iterative sequences, the global convergence can be guaranteed by line search with l∞penalty function and under some mild assumptions.