Many iterative algorithms in optimization, games, and learning can be viewed as dynamical systems with inputs (measurements, historical data, user feedback), internal states (decision variables, state estimates, Lagrange multipliers), outputs (residuals, actuator commands), and uncertainties (noise, unknown parameters). The last few years have witnessed a growing interest in studying how learning, optimization and game-theoretic algorithms […]

Abstract to come.   Refreshments will be served preceding the talk, starting at 2:30.

  Refreshments will be served preceding the talk, starting at 2:30.

  Refreshments will be served preceding the talk, starting at 2:30.

Abstract to come. Refreshments will be served preceding the talk, starting at 2:30.

Abstract to come. Refreshments will be served preceding the talk, starting at 2:30.