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Revisiting approximate minimality and approximate criticality in scalar optimization with constraints

Marius Durea et al.

Mathematical Methods of Operations Research2026https://doi.org/10.1007/s00186-026-00915-9article
AJG 1
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0.50

What the paper says

This paper examines various questions related to approximate minimality and approximate criticality in constrained scalar optimization. We establish necessary optimality conditions for the approximate minimality of a lower semicontinuous (and therefore non-Lipschitz) function under a set constraint. Additionally, we explore the relationship between approximate criticality and genuine minimality of perturbed functions. A key tool in deriving these results is the use of well-known penalization functions.

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https://doi.org/https://doi.org/10.1007/s00186-026-00915-9

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@article{marius2026,
  title        = {{Revisiting approximate minimality and approximate criticality in scalar optimization with constraints}},
  author       = {Marius Durea et al.},
  journal      = {Mathematical Methods of Operations Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s00186-026-00915-9},
}

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Revisiting approximate minimality and approximate criticality in scalar optimization with constraints

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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