Online Optimization with Unknown Time-Varying Parameters Using Noisy Gradient Measurements
The episode discusses a paper on online optimization where cost function parameters change over time under unknown dynamics and noisy gradient measurements. The proposed solution uses sequential control theory tools: a Gauss-Markov estimator to reconstruct parameters, an instrumental variable estimator to identify dynamics, and forecasting for future optimization. This provides rigorous bounds on expected tracking error based on the number of measurements.