Finite Markov Chains and Algorithmic Applications: Simulated annealing

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How researchers see this paper

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Probability Theory : The Coupling Method (2012, background): “A basic result of Markov chain theory is that πi = 1/mi, i ∈ S (see Häggström [5], Chapter 5, and Kraaikamp [7], Section 2.”
Structured Learning and Prediction in Computer Vision (2011, methodology): “Further details on Markov chains can be found in [55].”
Markov Chains and Mixing Times (2008, methodology): “For the one-dimensional case, we note that Häggström and Jonasson (1997) examined lamplighter walks on cycles.”
A survey of average contractive iterated function systems (2012, unknown): “Häggström [19].”
Markov Chains on Orbits of Permutation Groups (2012, background): “[43, 20, 38, 19, 23, 3]), we expect orbital Markov chains to improve the algorithms’ performance when applied to problems that exhibit symmetries.”
The Gremlin graph traversal machine and language (invited talk) (2015, background): “If the graph is strongly connected and aperiodic [6], then the larger a vertex’s value in v, the more central it is in the graph.”
Sub-Channel Assignment, Power Allocation, and User Scheduling for Non-Orthogonal Multiple Access Networks (2016, methodology): “Proof: USMA-2 is proposed based on the simulated annealing algorit hm, which has been proved to reach a global optimum with a sufficiently large num ber of iterations in [38] [39].”