TY - JOUR
T1 - Non-additive two-option ski rental
AU - Levi, Amir
AU - Patt-Shamir, Boaz
N1 - Publisher Copyright:
© 2015 Elsevier B.V.
PY - 2015/6/13
Y1 - 2015/6/13
N2 - We consider the following generalization of the classical problem of ski rental. There is a game that ends at an unknown time, and the algorithm needs to decide how to pay for the time until the game ends. In our generalization, there are two "payment plans" called "options," such that each option i (for i=1, 2) consists of two kinds of costs: bi is the (one time) cost to start using Option i, and ai is the (ongoing) usage cost per unit of time for Option i. We assume w.l.o.g. that a1>a2 and b12. Additionally, we assume the existence of a transition cost c, which is incurred if we switch from Option 1 to Option 2. (In the classical version, b1=0, a2=0 and c=b2.)We give deterministic and randomized algorithms for this general setting and analyze their competitive ratio. We also prove that the competitive ratios of our algorithms are the best possible by presenting matching lower bounds for both the deterministic and the randomized cases.
AB - We consider the following generalization of the classical problem of ski rental. There is a game that ends at an unknown time, and the algorithm needs to decide how to pay for the time until the game ends. In our generalization, there are two "payment plans" called "options," such that each option i (for i=1, 2) consists of two kinds of costs: bi is the (one time) cost to start using Option i, and ai is the (ongoing) usage cost per unit of time for Option i. We assume w.l.o.g. that a1>a2 and b12. Additionally, we assume the existence of a transition cost c, which is incurred if we switch from Option 1 to Option 2. (In the classical version, b1=0, a2=0 and c=b2.)We give deterministic and randomized algorithms for this general setting and analyze their competitive ratio. We also prove that the competitive ratios of our algorithms are the best possible by presenting matching lower bounds for both the deterministic and the randomized cases.
KW - Buy or rent
KW - Competitive analysis
KW - Duration prediction
KW - Randomized algorithms
KW - Ski rental
UR - https://www.scopus.com/pages/publications/84951769249
U2 - 10.1016/j.tcs.2015.01.038
DO - 10.1016/j.tcs.2015.01.038
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AN - SCOPUS:84951769249
SN - 0304-3975
VL - 584
SP - 42
EP - 52
JO - Theoretical Computer Science
JF - Theoretical Computer Science
ER -