Stochastic Mirror Descent Dynamics and Their Convergence in Monotone Variational Inequalities

Panayotis Mertikopoulos () and Mathias Staudigl ()
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Panayotis Mertikopoulos: Univ. Grenoble Alpes
Mathias Staudigl: Maastricht University

Journal of Optimization Theory and Applications, 2018, vol. 179, issue 3, No 6, 838-867

Abstract: Abstract 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.

Keywords: Mirror descent; Variational inequalities; Saddle-point problems; Stochastic differential equations; 90C25; 90C33; 90C47 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10957-018-1346-x

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