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On nondegenerate M-stationary points for sparsity constrained nonlinear optimization

Author

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  • S. Lämmel

    (Chemnitz University of Technology)

  • V. Shikhman

    (Chemnitz University of Technology)

Abstract

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 $$N^C$$ 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.

Suggested Citation

  • S. Lämmel & V. Shikhman, 2022. "On nondegenerate M-stationary points for sparsity constrained nonlinear optimization," Journal of Global Optimization, Springer, vol. 82(2), pages 219-242, February.
    • Handle: RePEc:spr:jglopt:v:82:y:2022:i:2:d:10.1007_s10898-021-01070-7
    DOI: 10.1007/s10898-021-01070-7
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    References listed on IDEAS

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    1. Amir Beck & Nadav Hallak, 2016. "On the Minimization Over Sparse Symmetric Sets: Projections, Optimality Conditions, and Algorithms," Mathematics of Operations Research, INFORMS, vol. 41(1), pages 196-223, February.
    2. Dominik Dorsch & Vladimir Shikhman & Oliver Stein, 2012. "Mathematical programs with vanishing constraints: critical point theory," Journal of Global Optimization, Springer, vol. 52(3), pages 591-605, March.
    3. Max Bucher & Alexandra Schwartz, 2018. "Second-Order Optimality Conditions and Improved Convergence Results for Regularization Methods for Cardinality-Constrained Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 178(2), pages 383-410, August.
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    Cited by:

    1. Christian Kanzow & Matteo Lapucci, 2023. "Inexact penalty decomposition methods for optimization problems with geometric constraints," Computational Optimization and Applications, Springer, vol. 85(3), pages 937-971, July.

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