# Finding Steady States of Communicating Markov Processes Combining Aggregation/Disaggregation with Tensor Techniques

@inproceedings{Macedo2016FindingSS, title={Finding Steady States of Communicating Markov Processes Combining Aggregation/Disaggregation with Tensor Techniques}, author={Francisco Macedo}, booktitle={European Performance Engineering Workshop}, year={2016} }

Stochastic models for interacting processes feature a dimensionality that grows exponentially with the number of processes. This state space explosion severely impairs the use of standard methods for the numerical analysis of such Markov chains. In this work, we develop algorithms for the approximation of steady states of structured Markov chains that consider tensor train decompositions, combined with well-established techniques for this problem – aggregation/disaggregation techniques…

## One Citation

### Low-rank tensor methods for large Markov chains and forward feature selection methods

- Computer Science
- 2018

This thesis presents and compares several approaches for the determination of the steady-state of large-scale Markov chains with an underlying lowrank tensor structure, and develops a theoretical framework that allows evaluating the methods based on their theoretical properties.

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