Learning to Make Rent-to-Buy Decisions with Systems Applications

In the single rent-to-buy decision problem, without a priori knowledge of the amount of time a resource will be used we need to decide when to buy the resource, given that we can rent the resource for $1 per unit time or buy it once and for all for $c. In this paper we study algorithms that make a sequence of single rent-to-buy decisions, using the assumption that the resource use times are independently drawn from an unknown probability distribution. Our study of this rent-to-buy problem is motivated by important systems applications described in the paper. We develop a provably optimal and computationally e cient algorithm for our formulation of the rent-to-buy problem. Our algorithm uses O( p t) time and space, and its expected cost for the tth resource use converges to optimal as O( p logt=t), for any bounded probability distribution on the resource use times. Alternatively, using O(1) time and space, the algorithm almost converges to optimal. We describe the experimental results ...

• Type: Conference
• Year: 1995
• Where: ICML
• Authors: P. Krishnan, Philip M. Long, Jeffrey Scott Vitter
• Keywords: Algorithm Yields, Cient Algorithm, ICML 1995, Machine Learning, Non-adaptive 2competitive Algorithm