Mean Field Control Hierarchy

Giacomo Albi, Young Pil Choi, Massimo Fornasier, Dante Kalise

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

In this paper we model the role of a government of a large population as a mean field optimal control problem. Such control problems are constrained by a PDE of continuity-type, governing the dynamics of the probability distribution of the agent population. We show the existence of mean field optimal controls both in the stochastic and deterministic setting. We derive rigorously the first order optimality conditions useful for numerical computation of mean field optimal controls. We introduce a novel approximating hierarchy of sub-optimal controls based on a Boltzmann approach, whose computation requires a very moderate numerical complexity with respect to the one of the optimal control. We provide numerical experiments for models in opinion formation comparing the behavior of the control hierarchy.

Original languageEnglish
Pages (from-to)93-135
Number of pages43
JournalApplied Mathematics and Optimization
Volume76
Issue number1
DOIs
Publication statusPublished - 2017 Aug 1

Fingerprint

Mean Field
Optimal Control
Opinion Formation
Suboptimal Control
First-order Optimality Conditions
Ludwig Boltzmann
Numerical Computation
Optimal Control Problem
Control Problem
Probability Distribution
Numerical Experiment
Model
Hierarchy
Probability distributions
Experiments
Government

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Applied Mathematics

Cite this

Albi, Giacomo ; Choi, Young Pil ; Fornasier, Massimo ; Kalise, Dante. / Mean Field Control Hierarchy. In: Applied Mathematics and Optimization. 2017 ; Vol. 76, No. 1. pp. 93-135.
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Mean Field Control Hierarchy. / Albi, Giacomo; Choi, Young Pil; Fornasier, Massimo; Kalise, Dante.

In: Applied Mathematics and Optimization, Vol. 76, No. 1, 01.08.2017, p. 93-135.

Research output: Contribution to journalArticle

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