<ul><li>This paper introduces a distributed adaptive optimization problem in which agents collaboratively estimate an unknown parameter while finding the optimal solution.</li><li>The proposed Prediction while Optimization scheme utilizes distributed fractional Bayesian learning and distributed gradient descent.</li><li>Under suitable assumptions, the paper proves the convergence of agents' beliefs and decision variables towards the true parameter and optimal solution.</li><li>Numerical experiments are conducted to validate the theoretical analysis.</li></ul>

Distributed Fractional Bayesian Learning for Adaptive Optimization

Discover more