Strongly sub-feasible direction method for constrained optimization problems with nonsmooth objective functions

Chun-ming Tang and Jin-bao Jian

European Journal of Operational Research, 2012, vol. 218, issue 1, 28-37

Abstract: In this paper, we propose a strongly sub-feasible direction method for the solution of inequality constrained optimization problems whose objective functions are not necessarily differentiable. The algorithm combines the subgradient aggregation technique with the ideas of generalized cutting plane method and of strongly sub-feasible direction method, and as results a new search direction finding subproblem and a new line search strategy are presented. The algorithm can not only accept infeasible starting points but also preserve the “strong sub-feasibility” of the current iteration without unduly increasing the objective value. Moreover, once a feasible iterate occurs, it becomes automatically a feasible descent algorithm. Global convergence is proved, and some preliminary numerical results show that the proposed algorithm is efficient.

Keywords: Nonlinear programming; Strongly sub-feasible direction; Nonsmooth; Subgradient aggregation; Global convergence (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:218:y:2012:i:1:p:28-37

DOI: 10.1016/j.ejor.2011.10.055

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