Nearest Reversible Markov Chains with Sparsity Constraints: An Optimization Approach

Reversibility is a key property of Markov chains, central to algorithms such as Metropolis-Hastings and other MCMC methods.

Yet many applications yield non-reversible chains, motivating the problem of approximating them by reversible ones with minimal modification.

We formulate this task as a matrix nearness problem and focus on the practically relevant case of sparse transition matrices.

The resulting optimization problem is a quadratic programming problem, and numerical experiments illustrate the effectiveness of the approach.

This framework provides a principled way to enforce reversibility and sparsity patterns in Markov chains with applications in MCMC, computational chemistry, and data-driven modeling.