Stochastic optimization methods for the simultaneous control of parameter-dependent systems

Stochastic optimization methods for the simultaneous control of parameter-dependent systems
Speaker. Umberto Biccari

Abstract. We address the application of stochastic optimization methods for the simultaneous control of parameter-dependent systems. In particular, we focus on the classical Stochastic Gradient Descent (SGD) approach of Robbins and Monro, and on the recently developed Continuous Stochastic Gradient (CSG) algorithm. We consider the problem of computing simultaneous controls through the minimization of a cost functional defined as the superposition of individual costs for each realization of the system. We compare the performances of these stochastic approaches, in terms of their computational complexity, with those of the more classical Gradient Descent (GD) and Conjugate Gradient (CG) algorithms, and we discuss the advantages and disadvantages of each methodology. In agreement with well-established results in the machine learning context, we show how the SGD and CSG algorithms can significantly reduce the computational burden when treating control problems depending on a large number of parameters. This is corroborated by numerical experiments.

Zoom link

https://zoom.us/j/96101999387
Meeting ID: 961 0199 9387
Pin/password code: 003932

Get the Official app at: