Competitive auctions and digital goods
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
On approximating optimal auctions
Proceedings of the 3rd ACM conference on Electronic Commerce
Online learning in online auctions
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
On the Hardness of Optimal Auctions
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
On profit-maximizing envy-free pricing
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Truthful randomized mechanisms for combinatorial auctions
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Approximating revenue-maximizing combinatorial auctions
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Auctions with intermediaries: extended abstract
Proceedings of the 11th ACM conference on Electronic commerce
Real-time bidding algorithms for performance-based display ad allocation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Quasi-proportional mechanisms: prior-free revenue maximization
LATIN'10 Proceedings of the 9th Latin American conference on Theoretical Informatics
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We study the design of truthful auction mechanisms for maximizing the seller's profit. We focus on the case when the auction mechanism does not have any knowledge of bidders' valuations, especially of their upper bound. For the Single-Item auction, we obtain an “asymptotically” optimal scheme: for any k∈Z+ and ε0, we give a randomized truthful auction that guarantees an expected profit of $\Omega(\frac{OPT}{\ln OPT \ln\ln OPT \cdots (\ln^{(k)}OPT)^{1+\epsilon}})$, where OPT is the maximum social utility of the auction. Moreover, we show that no truthful auction can guarantee an expected profit of $\Omega(\frac{OPT}{\ln OPT \ln\ln OPT\cdots \ln^{(k)}OPT})$. In addition, we extend our results and techniques to Multi-units auction, Unit-Demand auction, and Combinatorial auction.