pith:2RTRNKAF
On the Nature of Regularity Assumptions in Bilevel Optimization with Constrained Lower-level Problem
Requiring lower-level regularity conditions at every upper-level point in bilevel optimization is non-prevalent, as structural invariants cannot be made consistent by small perturbations.
arxiv:2605.14409 v1 · 2026-05-14 · math.OC
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Claims
the requirement that these conditions hold at every upper-level variable x is strong, in the sense that it is non-prevalent: there exist problems for which no sufficiently small perturbation of the lower-level objective and constraints can make the conditions hold at every x.
The rigidity theorems assume that the lower-level problem is defined by smooth functions and that the structural invariants (e.g., active-set signatures) are well-defined and constant when the regularity conditions hold; this may fail for non-smooth or degenerate lower-level problems not covered by the counterexamples.
Requiring LICQ/SCS/SOSC everywhere in bilevel optimization is non-prevalent and rigid, while holding almost everywhere is prevalent, but the distinction introduces fundamental difficulties.
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Receipt and verification
| First computed | 2026-05-17T23:39:07.387165Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
d46716a805f4294296f1b3938f6ace3c1ba73750084587e3f456ae329102c025
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/2RTRNKAF6QUUFFXRWOJY62WOHQ \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d46716a805f4294296f1b3938f6ace3c1ba73750084587e3f456ae329102c025
Canonical record JSON
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