12,569 research outputs found
Multigrid methods for two-player zero-sum stochastic games
We present a fast numerical algorithm for large scale zero-sum stochastic
games with perfect information, which combines policy iteration and algebraic
multigrid methods. This algorithm can be applied either to a true finite state
space zero-sum two player game or to the discretization of an Isaacs equation.
We present numerical tests on discretizations of Isaacs equations or
variational inequalities. We also present a full multi-level policy iteration,
similar to FMG, which allows to improve substantially the computation time for
solving some variational inequalities.Comment: 31 page
Mean Field Games models of segregation
This paper introduces and analyses some models in the framework of Mean Field
Games describing interactions between two populations motivated by the studies
on urban settlements and residential choice by Thomas Schelling. For static
games, a large population limit is proved. For differential games with noise,
the existence of solutions is established for the systems of partial
differential equations of Mean Field Game theory, in the stationary and in the
evolutive case. Numerical methods are proposed, with several simulations. In
the examples and in the numerical results, particular emphasis is put on the
phenomenon of segregation between the populations.Comment: 35 pages, 10 figure
A Probabilistic Numerical Method for Fully Nonlinear Parabolic PDEs
We consider the probabilistic numerical scheme for fully nonlinear PDEs
suggested in \cite{cstv}, and show that it can be introduced naturally as a
combination of Monte Carlo and finite differences scheme without appealing to
the theory of backward stochastic differential equations. Our first main result
provides the convergence of the discrete-time approximation and derives a bound
on the discretization error in terms of the time step. An explicit
implementable scheme requires to approximate the conditional expectation
operators involved in the discretization. This induces a further Monte Carlo
error. Our second main result is to prove the convergence of the latter
approximation scheme, and to derive an upper bound on the approximation error.
Numerical experiments are performed for the approximation of the solution of
the mean curvature flow equation in dimensions two and three, and for two and
five-dimensional (plus time) fully-nonlinear Hamilton-Jacobi-Bellman equations
arising in the theory of portfolio optimization in financial mathematics
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