markov chain monte carlo – Optimization Online

Betweenness Central Nodes Under Uncertainty: An Absorbing Markov Chain Approach

Published: 2026/05/13

Applications - OR and Management Sciences, Applications - Science and Engineering, Robust Optimization betweenness, Data-Driven Robust Optimization, group betweenness centrality, markov chain, markov chain approximation, markov chain monte carlo, markov chain sample average approximation, stochastic network design

We propose a betweenness centrality measure and algorithms for stochastic networks, where edges can fail and weights vary across realizations, making the most central node random. Our approach models the sequence of reported central nodes as an absorbing Markov chain and measures node importance by the share of pre-absorption time spent at each node. This … Read more

Importance Sampling in Stochastic Programming: A Markov Chain Monte Carlo Approach

Published: 2012/08/06, Updated: 2013/11/05

Stochastic Programming benders decomposition, importance sampling, markov chain monte carlo, stochastic programming, variance reduction

Stochastic programming models are large-scale optimization problems that are used to facilitate decision-making under uncertainty. Optimization algorithms for such problems need to evaluate the expected future costs of current decisions, often referred to as the recourse function. In practice, this calculation is computationally difficult as it requires the evaluation of a multidimensional integral whose integrand … Read more