Mean-Field Pontryagin Maximum Principle

We derive a maximum principle for optimal control problems with constraints given by the coupling of a system of ordinary differential equations and a partial differential equation of Vlasov type with smooth interaction kernel. Such problems arise naturally as Gamma-limits of optimal control problems constrained by ordinary differential equations, modeling, for instance, external interventions on crowd dynamics by means of leaders. We obtain these first-order optimality conditions in the form of Hamiltonian flows in the Wasserstein space of probability measures with forward–backward boundary conditions with respect to the first and second marginals, respectively. In particular, we recover the equations and their solutions by means of a constructive procedure, which can be seen as the mean-field limit of the Pontryagin Maximum Principle applied to the optimal control problem for the discretized density, under a suitable scaling of the adjoint variables.

[1]  P. Lions,et al.  Mean field games , 2007 .

[2]  Alfio Borzì,et al.  On the control through leadership of the Hegselmann–Krause opinion formation model , 2015 .

[3]  D. Kinderlehrer,et al.  An introduction to variational inequalities and their applications , 1980 .

[4]  A. Bertozzi,et al.  Self-propelled particles with soft-core interactions: patterns, stability, and collapse. , 2006, Physical review letters.

[5]  Giovanni Pisante,et al.  The Semigeostrophic Equations Discretized in Reference and Dual Variables , 2007 .

[6]  Felipe Cucker,et al.  A General Collision-Avoiding Flocking Framework , 2011, IEEE Transactions on Automatic Control.

[7]  Massimo Fornasier,et al.  Sparse control of alignment models in high dimension , 2014, Networks Heterog. Media.

[8]  A. Bensoussan,et al.  Mean Field Games and Mean Field Type Control Theory , 2013 .

[9]  Vicsek,et al.  Novel type of phase transition in a system of self-driven particles. , 1995, Physical review letters.

[10]  L. Ambrosio,et al.  Functions of Bounded Variation and Free Discontinuity Problems , 2000 .

[11]  C. Villani Topics in Optimal Transportation , 2003 .

[12]  Massimo Fornasier,et al.  Mean Field Control Hierarchy , 2016, Applied Mathematics & Optimization.

[13]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[14]  Massimo Fornasier,et al.  Sparse stabilization of dynamical systems driven by attraction and avoidance forces , 2014, Networks Heterog. Media.

[15]  M. Burger,et al.  Mean field games with nonlinear mobilities in pedestrian dynamics , 2013, 1304.5201.

[16]  Daniel Andersson,et al.  A Maximum Principle for SDEs of Mean-Field Type , 2011 .

[17]  Giacomo Albi,et al.  Invisible Control of Self-Organizing Agents Leaving Unknown Environments , 2015, SIAM J. Appl. Math..

[18]  B. Piccoli,et al.  Transport Equation with Nonlocal Velocity in Wasserstein Spaces: Convergence of Numerical Schemes , 2011, 1106.2555.

[19]  K. Pfaffelmoser,et al.  Global classical solutions of the Vlasov-Poisson system in three dimensions for general initial data , 1992 .

[20]  K. Kunisch,et al.  A duality-based approach to elliptic control problems in non-reflexive Banach spaces , 2011 .

[21]  C. Chou The Vlasov equations , 1965 .

[22]  Pierre-Louis Lions,et al.  Propagation of moments and regularity for the 3-dimensional Vlasov-Poisson system , 1991 .

[23]  Massimo Fornasier,et al.  Un)conditional consensus emergence under feedback controls , 2015, 1502.06100.

[24]  Hyunjoong Kim,et al.  Functional Analysis I , 2017 .

[25]  G. D. Maso,et al.  An Introduction to-convergence , 1993 .

[26]  Rainer Hegselmann,et al.  Opinion dynamics and bounded confidence: models, analysis and simulation , 2002, J. Artif. Soc. Soc. Simul..

[27]  Giuseppe Buttazzo,et al.  Γ-convergence and optimal control problems , 1982 .

[28]  M. Fornasier,et al.  Mean-Field Optimal Control , 2013, 1306.5913.

[29]  P. Caines,et al.  Individual and mass behaviour in large population stochastic wireless power control problems: centralized and Nash equilibrium solutions , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[30]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[31]  L. Ambrosio,et al.  Gradient Flows: In Metric Spaces and in the Space of Probability Measures , 2005 .

[32]  E. Tadmor,et al.  From particle to kinetic and hydrodynamic descriptions of flocking , 2008, 0806.2182.

[33]  Massimo Fornasier,et al.  Mean-field sparse optimal control , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[34]  Sébastien Motsch,et al.  Heterophilious Dynamics Enhances Consensus , 2013, SIAM Rev..

[35]  Felipe Cucker,et al.  Emergent Behavior in Flocks , 2007, IEEE Transactions on Automatic Control.

[36]  Boris Vexler,et al.  A Priori Error Analysis for Discretization of Sparse Elliptic Optimal Control Problems in Measure Space , 2013, SIAM J. Control. Optim..

[37]  Cl'ement Mouhot,et al.  On Landau damping , 2009, 0904.2760.

[38]  Massimo Fornasier,et al.  Un)conditional consensus emergence under perturbed and decentralized feedback controls , 2015 .

[39]  A. Bertozzi,et al.  State Transitions and the Continuum Limit for a 2D Interacting, Self-Propelled Particle System , 2006, nlin/0606031.

[40]  B. Piccoli,et al.  Generalized Wasserstein Distance and its Application to Transport Equations with Source , 2012, 1206.3219.

[41]  Georg Stadler,et al.  Elliptic optimal control problems with L1-control cost and applications for the placement of control devices , 2009, Comput. Optim. Appl..

[42]  R. Carmona,et al.  Control of McKean–Vlasov dynamics versus mean field games , 2012, 1210.5771.

[43]  Benedetto Piccoli,et al.  Control to Flocking of the Kinetic Cucker-Smale Model , 2014, SIAM J. Math. Anal..

[44]  Massimo Fornasier,et al.  Sparse Stabilization and Control of Alignment Models , 2012, 1210.5739.

[45]  W. Rappel,et al.  Self-organization in systems of self-propelled particles. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[46]  F. Clarke Functional Analysis, Calculus of Variations and Optimal Control , 2013 .

[47]  Enrique Zuazua,et al.  Optimal location of controllers for the one-dimensional wave equation , 2013 .

[48]  Roger L. Hughes,et al.  A continuum theory for the flow of pedestrians , 2002 .

[49]  Mattia Bongini Invisible Sparse Control of Self-Organizing Agents Leaving Unknown Environments (joint work with G. Albi, E. Cristiani, and D. Kalise) , 2015 .

[50]  Jean-Pierre Raymond,et al.  Hamiltonian Pontryagin's Principles for Control Problems Governed by Semilinear Parabolic Equations , 1999 .

[51]  Massimo Fornasier,et al.  Sparse control of force field dynamics , 2014, 2014 7th International Conference on NETwork Games, COntrol and OPtimization (NetGCoop).

[52]  W. Gangbo,et al.  Hamiltonian ODEs in the Wasserstein space of probability measures , 2008 .