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A cutting-plane method to non-smooth multiobjective optimization problems
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DOI：10.1016/j.ejor.2018.12.047.png)
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The cutting-plane optimization methods rely on the idea that any subgradient of the objective function or the active/violated constraints defines a halfspace to be excluded from a set that contains an optimal solution: the localizing set. This algorithm converges towards a global minimum of any pseudoconvex subdifferentiable function. A naive extension for multiobjective optimization would be using simultaneously some subgradients of all objective functions for a given feasible point. However, as demonstrated in this paper, this approach can lead to a convergence towards non-optimal points. This paper introduces an optimization strategy for cutting-plane methods to cope with multiobjective problems without any scalarization procedure. The proposed strategy guarantees that its optimal solution is a Pareto Optimal solution of the original problem, which is also no worse than the starting point, and that any Pareto Optimal solution can be sampled. Moreover, the auxiliary problem is infeasible only if the original problem is also infeasible. The new strategy inherits the original theoretical guarantees of cutting planes methods and it can be applied to build other strategies. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Multiple objective programming
Cutting-plane method
Pseudoconvex functions
Subdifferencial
Nonsmooth optimization
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6
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被引数:
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