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Exact penalty methods with adaptive discretization for nonsmooth semi-infinite optimization

Abstract

The paper considers a nonsmooth semi-infinite optimization problem in which the constraints are reformulated through a value function. Stationary points of the cutting surface approximations are studied, showing that stationarity is preserved in the limit under boundedness of the associated sequence of multipliers. Building on these results, an algorithm that combines the cutting surface approximations with a penalty scheme is devised, together with some proximal variants. An exact penalty result is established under suitable global conditions, and numerical results are reported to compare the methods with existing exact penalty and cutting surface algorithms.

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BibTeX
@misc{bigi2026exact,
  title = {{Exact penalty methods with adaptive discretization for nonsmooth semi-infinite optimization}},
  author = {Giancarlo Bigi and Riccardo Tomassini},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.03920},
  eprint = {2611.03920},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.03920},
}
RIS
TY  - UNPB
TI  - Exact penalty methods with adaptive discretization for nonsmooth semi-infinite optimization
AU  - Giancarlo Bigi
AU  - Riccardo Tomassini
PY  - 2026
DA  - 2026/11/02
PB  - arXiv
DO  - 10.48550/arxiv.2611.03920
UR  - https://arxiv.org/abs/2611.03920
AB  - The paper considers a nonsmooth semi-infinite optimization problem in which the constraints are reformulated through a value function. Stationary points of the cutting surface approximations are studied, showing that stationarity is preserved in the limit under boundedness of the associated sequence of multipliers. Building on these results, an algorithm that combines the cutting surface approximations with a penalty scheme is devised, together with some proximal variants. An exact penalty result is established under suitable global conditions, and numerical results are reported to compare the methods with existing exact penalty and cutting surface algorithms.
ER  -