The complexity of Markov decision processes
Mathematics of Operations Research
An algorithm for solving linear programming programs in O(n3L) operations
Progress in Mathematical Programming Interior-point and related methods
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Verifying nash equilibria in pagerank games on undirected web graphs
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
Perron vector optimization applied to search engines
Applied Numerical Mathematics
Reputation games for undirected graphs
Discrete Applied Mathematics
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The importance of a node in a directed graph can be measured by its PageRank. The PageRank of a node is used in a number of application contexts - including ranking websites - and can be interpreted as the average portion of time spent at the node by an infinite random walk. We consider the problem of maximizing the PageRank of a node by selecting some of the edges from a set of edges that are under our control. By applying results from Markov decision theory, we show that an optimal solution to this problem can be found in polynomial time. It also indicates that the provided reformulation is well-suited for reinforcement learning algorithms. Finally, we show that, under the slight modification for which we are given mutually exclusive pairs of edges, the problem of PageRank optimization becomes NP-hard.