Solving ill-posed bilevel programs
Solving ill-posed bilevel programs
This paper deals with ill-posed bilevel programs, i.e., problems admitting multiple lower-level solutions for some upper-level parameters. Many publications have been devoted to the standard optimistic case of this problem, where the difficulty is essentially moved from the objective function to the feasible set. This new problem is simpler but there is no guaranty to obtain local optimal solutions for the original optimistic problem by this process. Considering the intrinsic non-convexity of bilevel programs, computing local optimal solutions is the best one can hope to get in most cases. To achieve this goal, we start by establishing an equivalence between the original optimistic problem an a certain set-valued optimization problem. Next, we develop optimality conditions for the latter problem and show that they generalize all the results currently known in the literature on optimistic bilevel optimization. Our approach is then extended to multiobjective bilevel optimization, and completely new results are derived for problems with vector-valued upper- and lower-level objective functions. Numerical implementations of the results of this paper are provided on some examples, in order to demonstrate how the original optimistic problem can be solved in practice, by means of a special set-valued optimization problem.
Bilevel optimization · Multiobjective bilevel optimization · Set-valuedoptimization · Variational analysis · Coderivative · Optimality conditions
423-448
Zemkoho, Alain B.
30c79e30-9879-48bd-8d0b-e2fbbc01269e
September 2016
Zemkoho, Alain B.
30c79e30-9879-48bd-8d0b-e2fbbc01269e
Abstract
This paper deals with ill-posed bilevel programs, i.e., problems admitting multiple lower-level solutions for some upper-level parameters. Many publications have been devoted to the standard optimistic case of this problem, where the difficulty is essentially moved from the objective function to the feasible set. This new problem is simpler but there is no guaranty to obtain local optimal solutions for the original optimistic problem by this process. Considering the intrinsic non-convexity of bilevel programs, computing local optimal solutions is the best one can hope to get in most cases. To achieve this goal, we start by establishing an equivalence between the original optimistic problem an a certain set-valued optimization problem. Next, we develop optimality conditions for the latter problem and show that they generalize all the results currently known in the literature on optimistic bilevel optimization. Our approach is then extended to multiobjective bilevel optimization, and completely new results are derived for problems with vector-valued upper- and lower-level objective functions. Numerical implementations of the results of this paper are provided on some examples, in order to demonstrate how the original optimistic problem can be solved in practice, by means of a special set-valued optimization problem.
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Solving-Ill-posed.pdf
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Accepted/In Press date: 6 April 2016
e-pub ahead of print date: 26 April 2016
Published date: September 2016
Keywords:
Bilevel optimization · Multiobjective bilevel optimization · Set-valuedoptimization · Variational analysis · Coderivative · Optimality conditions
Organisations:
Operational Research
Identifiers
Local EPrints ID: 373041
URI: http://eprints.soton.ac.uk/id/eprint/373041
ISSN: 1877-0533
PURE UUID: 18f9f8f0-a99e-4607-be92-2fb273a1bba3
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Date deposited: 06 Jan 2015 12:53
Last modified: 15 Mar 2024 03:51
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