Develops optimistic and pessimistic calculus rules for set-valued bilevel constraints, derives nonsmooth adjoint inclusions, and proposes a convergent single-loop algorithm demonstrated on total variation inverse problems.
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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Bilevel learning methods rely on implicit differentiation but are restricted by assumptions of unique lower-level solutions and struggle with constraints, and connections to broader bilevel optimization literature may enable more scalable general-purpose algorithms.
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Single-loop approaches to nonsmooth bilevel optimisation
Develops optimistic and pessimistic calculus rules for set-valued bilevel constraints, derives nonsmooth adjoint inclusions, and proposes a convergent single-loop algorithm demonstrated on total variation inverse problems.
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Bilevel learning
Bilevel learning methods rely on implicit differentiation but are restricted by assumptions of unique lower-level solutions and struggle with constraints, and connections to broader bilevel optimization literature may enable more scalable general-purpose algorithms.