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.
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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: Single-loop online algorithms
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.