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作者：Lacker, Daniel; Soret, Agathe
作者单位：Columbia University
摘要：This paper studies stochastic games on large graphs and their graphon limits. We propose a new formulation of graphon games based on a single typical player's label state distribution. In contrast, other recently proposed models of graphon games work directly with a continuum of players, which involves serious measure-theoretic technicalities. In fact, by viewing the label as a component of the state process, we show in our formulation that graphon games are a special case of mean field games,...
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作者：El Balghiti, Othman; Elmachtoub, Adam N.; Grigas, Paul; Tewari, Ambuj
作者单位：Columbia University; University of California System; University of California Berkeley; University of Michigan System; University of Michigan
摘要：The predict-then-optimize framework is fundamental in many practical settings: predict the unknown parameters of an optimization problem and then solve the problem using the predicted values of the parameters. A natural loss function in this environment is to consider the cost of the decisions induced by the predicted parameters in contrast to the prediction error of the parameters. This loss function is referred to as the smart predict then-optimize (SPO) loss. In this work, we seek to provid...
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作者：Hallak, Nadav; Teboulle, Marc
作者单位：Technion Israel Institute of Technology; Tel Aviv University
摘要：This paper develops a novel adaptive, augmented, Lagrangian-based method to address the comprehensive class of nonsmooth, nonconvex models with a nonlinear, functional composite structure in the objective. The proposed method uses an adaptive mechanism for the update of the feasibility penalizing elements, essentially turning our multiplier type method into a simple alternating minimization procedure based on the augmented Lagrangian function from some iteration onward. This allows us to avoid...
