On nondegenerate M-stationary points for sparsity constrained nonlinear optimization

被引:3
作者
Lammel, S. [1 ]
Shikhman, V [1 ]
机构
[1] Tech Univ Chemnitz, Dept Math, Reichenhainer Str 41, D-09126 Chemnitz, Germany
关键词
Sparsity constraint; M-stationarity; M-index; Nondegeneracy; Genericity; Morse theory; Saddle points; OPTIMALITY CONDITIONS; MATHEMATICAL PROGRAMS; PENALTY;
D O I
10.1007/s10898-021-01070-7
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
We study sparsity constrained nonlinear optimization (SCNO) from a topological point of view. Special focus will be on M-stationary points from Burdakov et al. (SIAM J Optim 26:397-425, 2016), also introduced as N-C-stationary points in Pan et al. (J Oper Res Soc China 3:421-439, 2015). We introduce nondegenerate M-stationary points and define their M-index. We show that all M-stationary points are generically nondegenerate. In particular, the sparsity constraint is active at all local minimizers of a generic SCNO. Some relations to other stationarity concepts, such as S-stationarity, basic feasibility, and CW-minimality, are discussed in detail. By doing so, the issues of instability and degeneracy of points due to different stationarity concepts are highlighted. The concept of M-stationarity allows to adequately describe the global structure of SCNO along the lines of Morse theory. For that, we study topological changes of lower level sets while passing an M-stationary point. As novelty for SCNO, multiple cells of dimension equal to the M-index are needed to be attached. This intriguing fact is in strong contrast with other optimization problems considered before, where just one cell suffices. As a consequence, we derive a Morse relation for SCNO, which relates the numbers of local minimizers and M-stationary points of M-index equal to one. The appearance of such saddle points cannot be thus neglected from the perspective of global optimization. Due to the multiplicity phenomenon in cell-attachment, a saddle point may lead to more than two different local minimizers. We conclude that the relatively involved structure of saddle points is the source of well-known difficulty if solving SCNO to global optimality.
引用
收藏
页码:219 / 242
页数:24
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