Randomized algorithms
Stochastic on-line knapsack problems
Mathematical Programming: Series A and B
Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
TCP dynamic acknowledgment delay (extended abstract): theory and practice
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Online computation and competitive analysis
Online computation and competitive analysis
Computationally Manageable Combinational Auctions
Management Science
Average-case analysis of off-line and on-line knapsack problems
Journal of Algorithms - Special issue on SODA '95 papers
Online bin packing with lookahead
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Competitive non-preemptive call control
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Approximate Algorithms for the 0/1 Knapsack Problem
Journal of the ACM (JACM)
Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems
Journal of the ACM (JACM)
On-line Resource Management with Applications to Routing and Scheduling
ICALP '95 Proceedings of the 22nd International Colloquium on Automata, Languages and Programming
Fast approximation algorithms for knapsack problems
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Probabilistic computations: Toward a unified measure of complexity
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Throughput-competitive on-line routing
SFCS '93 Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science
| Hi-index | 0.00 |
In this paper we provide an algorithmic approach to the study of online auctioning. From the perspective of the seller we formalize the auctioning problem as that of designing an algorithmic strategy that fairly maximizes the revenue earned by selling n identical items to bidders who submit bids online. We give a randomized online algorithm that is O(logB)-competitive against an oblivious adversary, where the bid values vary between 1 and B per item. We show that this algorithm is optimal in the worst-case and that it performs significantly better than any worst-case bounds achievable via deterministic strategies. Additionally we present experimental evidence to show that our algorithm outperforms conventional heuristic methods in practice. And finally we explore ways of modifying the conventional model of online algorithms to improve competitiveness of other types of auctioning scenarios while still maintaining fairness.