scholarly journals On nondegenerate M-stationary points for sparsity constrained nonlinear optimization

Author(s):  
S. Lämmel ◽  
V. Shikhman

AbstractWe 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.

2020 ◽  
Vol 32 (16) ◽  
pp. 12427-12452 ◽  
Author(s):  
Avijit Duary ◽  
Md Sadikur Rahman ◽  
Ali Akbar Shaikh ◽  
Seyed Taghi Akhavan Niaki ◽  
Asoke Kumar Bhunia

Author(s):  
A. Vincent Huffaker ◽  
Leonid Charny

Abstract This paper demonstrates techniques to allow relations on parametric curves in a variational design system. Constraints on the curves, which are normally represented as constrained nonlinear optimization problems, are reduced to systems of nonlinear equations (using the necessary conditions of the NLP). Additional degrees of freedom are constrained through fairing the curve and the resulting NLP is also reduced to its necessary conditions. Although the solution set of the necessary conditions contains the optimum, it contains many other solutions as well. The COAST design consistency algorithm is reviewed and then extended to handle consistency when constraints take the form of relations between objects. An example is given involving distance constraints on Bezier curves.


2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Hui Wang

This paper presents a modified barebones particle swarm optimization (OBPSO) to solve constrained nonlinear optimization problems. The proposed approach OBPSO combines barebones particle swarm optimization (BPSO) and opposition-based learning (OBL) to improve the quality of solutions. A novel boundary search strategy is used to approach the boundary between the feasible and infeasible search region. Moreover, an adaptive penalty method is employed to handle constraints. To verify the performance of OBPSO, a set of well-known constrained benchmark functions is used in the experiments. Simulation results show that our approach achieves a promising performance.


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