通过幽灵罚函数处理非凸复合问题的递减步长方法：从一般情形到凸正则约束情形
Diminishing Stepsize Methods for Nonconvex Composite Problems via Ghost Penalties: from the General to the Convex Regular Constrained Case
AI总结：
本文扩展了非凸约束问题的递减步长方法以处理等式约束和复合非光滑目标，并证明在凸约束满足标准约束规范时可大幅简化算法、降低每次迭代计算量。
摘要：
本文首先将文献[4]中针对非凸约束问题提出的递减步长方法进行扩展，以处理等式约束和复合型非光滑目标函数。随后，我们考虑约束为凸且满足标准约束规范的特殊情形，并证明在该设定下算法可以大幅简化，从而降低每次迭代的计算负担。
英文摘要：
In this paper we first extend the diminishing stepsize method for nonconvex constrained problems presented in [4] to deal with equality constraints and a nonsmooth objective function of composite type. We then consider the particular case in which the constraints are convex and satisfy a standard constraint qualification and show that in this setting the algorithm can be considerably simplified, reducing the computational burden of each iteration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
