Optimality of index policies for a sequential sampling problem

@article{Castan1999OptimalityOI,
  title={Optimality of index policies for a sequential sampling problem},
  author={David A. Casta{\~n}{\'o}n and Simon Streltsov and Pirooz Vakili},
  journal={IEEE Trans. Autom. Control.},
  year={1999},
  volume={44},
  pages={145-148}
}
Consider the following sequential sampling problem: at each time, a choice must be made between obtaining an independent sample from one of a set of random reward variables or stopping the sampling. Sampling a random variable incurs a random cost at each time. The objective of the problem is to maximize the expected nett difference between the largest sample reward obtained before stopping and the accumulated costs incurred while sampling. In this paper, the authors prove that the optimal… 

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References

SHOWING 1-10 OF 11 REFERENCES

Optimal Strategies for Selling an Asset

This paper considers the problem of selling an asset on the open market. The seller receives a random sequence of price offers, which may arrive either periodically or randomly over time. After each

Adaptive partitioned random search to global optimization

  • Z. Tang
  • Computer Science
    IEEE Trans. Autom. Control.
  • 1994
The proposed adaptive partitioned random search (APRS) is a tree search type of algorithms like branch-and-bound algorithms that can provide a much better-than-average solution within a modest number of function evaluations.

A Non-myopic Utility Function for Statistical Global Optimization Algorithms

This work proposes using a new utility function that is provably a globally optimal utility function in a non-adaptive context (where the model of the function values remains unchanged) and expects that its use will lead to the improved performance of statistical global optimization algorithms.

Optimal search for the best alternative

This paper completely characterizes the solution to the problem of searching for the best outcome from alternative sources with different properties. The optimal strategy is an elementary reservation

A counterexample to "adaptive partitioned random search to global optimization"

The optimality theorem presented by Tang (see ibid., vol.39, no.11, p.2235-44, 1994) is stated to be incorrect and a counterexample is presented.

Multi‐Armed Bandit Allocation Indices

3. Multi‐armed Bandit Allocation Indices: A meta-analyses of bandit allocation indices for the period April 1, 1991 to June 30, 1991, as well as a review of the periodical indices published in 1989, show clear trends in allocations between April and June.

A nonmyopic utility function for statistical global optimization

  • to be published.

Multi-armed Bandit Allocation Indices

Stochastic Optimal Control: Discrete Time Case, Mathematics

  • In Science and Engineering Series,
  • 1978

A nonmyopic utility function for statistical global optimization, " to be published

  • A nonmyopic utility function for statistical global optimization, " to be published