Diogo Aguiar Gomes

This paper develops a unified framework for proving the existence of solutions to stationary first-order mean-field games (MFGs) based on the theory of monotone operators in Banach spaces. We cast the coupled MFG system as a variational inequality, overcoming the limitations of prior Hilbert-space approaches that relied on high-order regularization and typically yielded only weak solutions in the monotone operator sense. In contrast, with our low-order regularization, we obtain strong solutions. Our approach addresses the non-coercivity of the underlying MFG operator through two key regularization strategies. First, by adding a low-order [Formula: see text]-Laplacian term, we restore coercivity, derive uniform a priori estimates, and pass to the limit via Minty’s method. This establishes, for the first time via monotonicity methods, the existence of strong solutions for models with both standard power-growth and singular congestion, with the latter requiring a careful restriction of the operator’s domain. Second, for Hamiltonians with only minimal growth hypotheses, we regularize the Hamiltonian itself via infimal convolution to prove the existence of weak solutions. Our Banach-space framework unifies and extends earlier existence results. By avoiding high-order smoothing, it not only provides a more direct theoretical path but is also ideally suited for modern numerical algorithms.

We introduce a derivative-free optimization algorithm that efficiently computes minima for various classes of one-dimensional functions, including nonconvex and nonsmooth functions. This algorithm numerically approximates the gradient flow of a relaxed functional, integrating strategies such as Monte Carlo methods, rejection sampling, and adaptive techniques. These strategies enhance performance in solving a diverse range of optimization problems while significantly reducing the number of required function evaluations compared with established methods. We present a proof of the convergence of the algorithm for locally convex functions and illustrate its numerical performance by comprehensive benchmarking with test functions, showcasing different properties and characteristics. The proposed algorithm offers a substantial potential for real-world models. It is particularly advantageous in situations requiring computationally intensive objective function evaluations, such as hyperparameter tuning in machine learning or line search in large-scale optimization problems involving the discretization of partial differential equations. Funding: The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST). D. A. Gomes was supported by KAUST [baseline funds and Grant KAUST OSR-CRG2021-4674]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2023.0340 .

In this paper, we address the problem of optimizing flows on generalized graphs that feature multiple entry points and multiple populations, each with varying cost structures. We tackle this problem by considering the multi-population Wardrop equilibrium, defined through variational inequalities. We rigorously analyze the existence and uniqueness of the Wardrop equilibrium. Furthermore, we introduce an efficient numerical method to find the solution. In particular, we reformulate the equilibrium problem as a distributed optimization problem over subgraphs and introduce a novel Hessian Riemannian flow method, a Riemannian-manifold-projected Hessian flow, to efficiently compute a solution. Finally, we demonstrate the effectiveness of our approach through examples in urban traffic management, including routing for diverse vehicle types and strategies for minimizing emissions in congested environments.

This chapter examines monotonicity techniques in the theory of mean-field games(MFGs). Originally, monotonicity ideas were used to establish the uniqueness of solutions for MFGs. Later, monotonicity methods and monotone operators were further exploited to build numerical methods and to construct weak solutions under mild assumptions. Here, after a brief discussion on the mean-field game formulation, we introduce the Minty method and regularization strategies for PDEs. These are then used to address typical stationary and time-dependent monotone MFGs and to establish the existence of weak solutions for such MFGs.

This paper addresses the crucial question of solution uniqueness in stationary first-order Mean-Field Games (MFGs). Despite well-established existence results, establishing uniqueness, particularly for weaker solutions in the sense of monotone operators, remains an open challenge. Building upon the framework of monotonicity methods, we introduce a linearization method that enables us to prove a weak-strong uniqueness result for stationary MFG systems on the d-dimensional torus. In particular, we give explicit conditions under which this uniqueness holds.