17,128 research outputs found
D-gap functions and descent techniques for solving equilibrium problems
A new algorithm for solving equilibrium problems with differentiable bifunctions is provided. The algorithm is based on descent directions of a suitable family of D-gap functions. Its convergence is proved under assumptions which do not guarantee the equivalence between the stationary points of the D-gap functions and the solutions of the equilibrium problem. Moreover, the algorithm does not require to set parameters according to thresholds which depend on regularity properties of the equilibrium bifunction. The results of preliminary numerical tests on Nash equilibrium problems with quadratic payoffs are reported. Finally, some numerical comparisons with other D-gap algorithms are drawn relying on some further tests on linear equilibrium problems
Online Convex Optimization for Sequential Decision Processes and Extensive-Form Games
Regret minimization is a powerful tool for solving large-scale extensive-form
games. State-of-the-art methods rely on minimizing regret locally at each
decision point. In this work we derive a new framework for regret minimization
on sequential decision problems and extensive-form games with general compact
convex sets at each decision point and general convex losses, as opposed to
prior work which has been for simplex decision points and linear losses. We
call our framework laminar regret decomposition. It generalizes the CFR
algorithm to this more general setting. Furthermore, our framework enables a
new proof of CFR even in the known setting, which is derived from a perspective
of decomposing polytope regret, thereby leading to an arguably simpler
interpretation of the algorithm. Our generalization to convex compact sets and
convex losses allows us to develop new algorithms for several problems:
regularized sequential decision making, regularized Nash equilibria in
extensive-form games, and computing approximate extensive-form perfect
equilibria. Our generalization also leads to the first regret-minimization
algorithm for computing reduced-normal-form quantal response equilibria based
on minimizing local regrets. Experiments show that our framework leads to
algorithms that scale at a rate comparable to the fastest variants of
counterfactual regret minimization for computing Nash equilibrium, and
therefore our approach leads to the first algorithm for computing quantal
response equilibria in extremely large games. Finally we show that our
framework enables a new kind of scalable opponent exploitation approach
Stochastic mirror descent dynamics and their convergence in monotone variational inequalities
We examine a class of stochastic mirror descent dynamics in the context of
monotone variational inequalities (including Nash equilibrium and saddle-point
problems). The dynamics under study are formulated as a stochastic differential
equation driven by a (single-valued) monotone operator and perturbed by a
Brownian motion. The system's controllable parameters are two variable weight
sequences that respectively pre- and post-multiply the driver of the process.
By carefully tuning these parameters, we obtain global convergence in the
ergodic sense, and we estimate the average rate of convergence of the process.
We also establish a large deviations principle showing that individual
trajectories exhibit exponential concentration around this average.Comment: 23 pages; updated proofs in Section 3 and Section
Competitive Gradient Descent
We introduce a new algorithm for the numerical computation of Nash equilibria
of competitive two-player games. Our method is a natural generalization of
gradient descent to the two-player setting where the update is given by the
Nash equilibrium of a regularized bilinear local approximation of the
underlying game. It avoids oscillatory and divergent behaviors seen in
alternating gradient descent. Using numerical experiments and rigorous
analysis, we provide a detailed comparison to methods based on \emph{optimism}
and \emph{consensus} and show that our method avoids making any unnecessary
changes to the gradient dynamics while achieving exponential (local)
convergence for (locally) convex-concave zero sum games. Convergence and
stability properties of our method are robust to strong interactions between
the players, without adapting the stepsize, which is not the case with previous
methods. In our numerical experiments on non-convex-concave problems, existing
methods are prone to divergence and instability due to their sensitivity to
interactions among the players, whereas we never observe divergence of our
algorithm. The ability to choose larger stepsizes furthermore allows our
algorithm to achieve faster convergence, as measured by the number of model
evaluations.Comment: Appeared in NeurIPS 2019. This version corrects an error in theorem
2.2. Source code used for the numerical experiments can be found under
http://github.com/f-t-s/CGD. A high-level overview of this work can be found
under http://f-t-s.github.io/projects/cgd
Some Open Problems in Random Matrix Theory and the Theory of Integrable Systems. II
We describe a list of open problems in random matrix theory and the theory of
integrable systems that was presented at the conference Asymptotics in
Integrable Systems, Random Matrices and Random Processes and Universality,
Centre de Recherches Mathematiques, Montreal, June 7-11, 2015. We also describe
progress that has been made on problems in an earlier list presented by the
author on the occasion of his 60th birthday in 2005 (see [Deift P., Contemp.
Math., Vol. 458, Amer. Math. Soc., Providence, RI, 2008, 419-430,
arXiv:0712.0849]).Comment: for Part I see arXiv:0712.084
Gap functions for quasi-equilibria
An approach for solving quasi-equilibrium problems (QEPs) is proposed relying on gap functions, which allow reformulating QEPs as global optimization problems. The (generalized) smoothness properties of a gap function are analysed and an upper estimate of its Clarke directional derivative is given. Monotonicity assumptions on both the equilibrium and constraining bifunctions are a key tool to guarantee that all the stationary points of a gap function actually solve QEP. A few classes of constraints satisfying such assumptions are identified covering a wide range of situations. Relying on these results, a descent method for solving QEP is devised and its convergence proved. Finally, error bounds are given in order to guarantee the boundedness of the sequence generated by the algorithm
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