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arXiv 2202.08464math.OC
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低秩矩阵Optimization与仿射流形的Normal Cones交规则及最优性分析

Normal Cones Intersection Rule and Optimality Analysis for Low-Rank Matrix Optimization with Affine Manifolds

Xinrong Li, Ziyan Luo

摘要:

仿射流形上的低秩矩阵优化(rank-MOA)的目标是极小化与仿射流形相交的低秩集上的连续可微函数。本文主要研究秩次MOA的最优性分析。作为基础,在一些温和的线性独立性假设下,建立了Fréchet法锥与秩MOA可行集的相交规则。借助于所得到的法锥的显式公式,研究了秩MOA的F-稳定点和α-稳定点,并以一阶最优性条件的形式揭示了它们与局部/全局极小的关系.基于模型的二阶微分信息,给出了二阶最优性分析的必要条件和充分条件。这些结果将丰富低秩矩阵优化理论,并为设计求解低秩矩阵优化问题的有效数值算法提供潜在的线索。同时,两个具体的应用程序的秩MOA进行了讨论,以说明我们提出的最优性分析。

英文摘要:

The low-rank matrix optimization with affine manifold (rank-MOA) aims to minimize a continuously differentiable function over a low-rank set intersecting with an affine manifold. This paper is devoted to the optimality analysis for rank-MOA. As a cornerstone, the intersection rule of the Fréchet normal cone to the feasible set of the rank-MOA is established under some mild linear independence assumptions. Aided with the resulting explicit formulae of the underlying normal cone, the so-called F-stationary point and the α-stationary point of rank-MOA are investigated and the relationship with local/global minimizers are then revealed in terms of first-order optimality conditions. Furthermore, the second-order optimality analysis, including the necessary and the sufficient conditions, is proposed based on the second-order differentiation information of the model. All these results will enrich the theory of low-rank matrix optimization and give potential clues to designing efficient numerical algorithms for seeking low rank solutions. Meanwhile, two specific applications of the rank-MOA are discussed to illustrate our proposed optimality analysis.

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。

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