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On Stability in Optimistic Bilevel Optimization

arXiv 2408.13323 ↗First posted 23 Aug 2024math.OC cs.LG cs.SYcited by 0

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Abstract (arXiv)

Solutions of bilevel optimization problems tend to suffer from instability under changes to problem data. In the optimistic setting, we construct a lifted formulation that exhibits desirable stability properties under mild assumptions that neither invoke convexity nor smoothness. The upper- and lower-level problems might involve integer restrictions and disjunctive constraints. In a range of results, we invoke at most pointwise and local calmness for the lower-level problem in a sense that holds broadly. The lifted formulation is computationally attractive with structural properties being brought out and an outer approximation algorithm becoming available.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 50 references that are in the map. First posted on arXiv 23 Aug 2024.

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References · 9

  1. 2023A Fully First-Order Method for Stochastic Bilevel Optimizationcited by 97
  2. 2023On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximationcited by 50
  3. 2023Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditionscited by 31
  4. 2021Value-Function-based Sequential Minimization for Bi-level Optimizationcited by 24
  5. 2021A Unified Framework for Multistage and Multilevel Mixed Integer Linear Optimizationcited by 10
  6. 2019New characterizations of Hoffman constants for systems of linear constraintscited by 9
  7. 2022On a Computationally Ill-Behaved Bilevel Problem with a Continuous and Nonconvex Lower Levelcited by 5
  8. 2017Two-level value function approach to nonsmooth optimistic and pessimistic bilevel programscited by 3
  9. 2021A Framework for Generalized Benders' Decomposition and Its Application to Multilevel Optimizationcited by 1

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