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A New Complexity Result on Solving the Markov Decision Problem

Department of Management Science and Engineering, Terman 316, Stanford University, Stanford, California 94305
yinyu-ye{at}stanford.edu, www.stanford.edu/~yyye

We present a new complexity result on solving the Markov decision problem (MDP) with n states and a number of actions for each state, a special class of real-number linear programs with the Leontief matrix structure. We prove that when the discount factor is strictly less than 1, the problem can be solved in at most O(n^1.5(log 1/(1 – ) + log n)) classical interior-point method iterations and O(n⁴(log 1/(1 – ) + log n)) arithmetic operations. Our method is a combinatorial interior-point method related to the work of Ye (1990. A "build-down" scheme for linear programming. Math. Programming 46 61–72) and Vavasis and Ye (1996. A primal-dual interior-point method whose running time depends only on the constraint matrix. Math. Programming 74 79–120). To our knowledge, this is the first strongly polynomial-time algorithm for solving the MDP when the discount factor is a constant less than 1.

Key Words: Markov decision problem; linear programming; complexity
History: Received: November 4, 2003; revision received: October 3, 2004;revision received: December 27, 2004;

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