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math.OC2020
Making Simulated Annealing Sample Efficient for Discrete Stochastic Optimization
Suhail M. Shah
We study the regret of simulated annealing (SA) based approaches to solving discrete stochastic optimization problems. The main theoretical conclusion is that the regret of the sim…
math.OC2020
Dynamic social learning under graph constraints
Konstantin Avrachenkov, Vivek S. Borkar, Sharayu Moharir +1
We introduce a model of graph-constrained dynamic choice with reinforcement modeled by positively -homogeneous rewards. We show that its empirical process, which can be written…