Proposes a control-theoretic pipeline using Gauss-Markov and instrumental-variable estimators to reconstruct and forecast latent time-varying parameters from noisy gradients in strongly convex online optimization, along with a bound on expected tracking error.
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A tracking ODE for optimal control is shown to yield robust stabilization under bounded measurement errors by deriving input-affine constraints, measurement accuracy bounds, and a sampling trigger that ensures closed-loop convergence to zero.
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Online Optimization with Unknown Time-Varying Parameters from Noisy Gradient Measurements
Proposes a control-theoretic pipeline using Gauss-Markov and instrumental-variable estimators to reconstruct and forecast latent time-varying parameters from noisy gradients in strongly convex online optimization, along with a bound on expected tracking error.
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Time-varying optimal control under measurement errors
A tracking ODE for optimal control is shown to yield robust stabilization under bounded measurement errors by deriving input-affine constraints, measurement accuracy bounds, and a sampling trigger that ensures closed-loop convergence to zero.