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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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Proposes single-loop online methods for PDE-constrained dynamic inverse problems that replace exact gradients with estimates having summable errors to retain standard regret bounds.
Proves time-averaged reconstruction errors converge to zero in online dynamic inverse problems as noise, algorithmic errors, and regularization vanish with growing horizon.
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Dynamic inverse problems: Online regularisation theory
Proves time-averaged reconstruction errors converge to zero in online dynamic inverse problems as noise, algorithmic errors, and regularization vanish with growing horizon.