1,358 research outputs found
Optimal Favoritism in All-Pay Auctions and Lottery Contests
We analyze the revenue-enhancing potential of favoring specific contestants in complete-information all-pay auctions and lottery contests with several heterogeneous contestants. Two instruments of favoritism are considered: head starts that are added to the bids of specific contestants and multiplicative biases that give idiosyncratic weights to the bids. In the all-pay auction, head starts are more effective than biases while optimally combining both instruments even yields first-best revenue. In the lottery contest, head starts are less effective than biases and combining both instruments cannot further increase revenue. As all-pay auctions revenue-dominate lottery contests under optimal biases, we thus obtain an unambiguous revenue-ranking of all six combinations of contest formats and instruments
Cloud-based Quadratic Optimization with Partially Homomorphic Encryption
The development of large-scale distributed control systems has led to the
outsourcing of costly computations to cloud-computing platforms, as well as to
concerns about privacy of the collected sensitive data. This paper develops a
cloud-based protocol for a quadratic optimization problem involving multiple
parties, each holding information it seeks to maintain private. The protocol is
based on the projected gradient ascent on the Lagrange dual problem and
exploits partially homomorphic encryption and secure multi-party computation
techniques. Using formal cryptographic definitions of indistinguishability, the
protocol is shown to achieve computational privacy, i.e., there is no
computationally efficient algorithm that any involved party can employ to
obtain private information beyond what can be inferred from the party's inputs
and outputs only. In order to reduce the communication complexity of the
proposed protocol, we introduced a variant that achieves this objective at the
expense of weaker privacy guarantees. We discuss in detail the computational
and communication complexity properties of both algorithms theoretically and
also through implementations. We conclude the paper with a discussion on
computational privacy and other notions of privacy such as the non-unique
retrieval of the private information from the protocol outputs
Energy-Efficient Scheduling and Power Allocation in Downlink OFDMA Networks with Base Station Coordination
This paper addresses the problem of energy-efficient resource allocation in
the downlink of a cellular OFDMA system. Three definitions of the energy
efficiency are considered for system design, accounting for both the radiated
and the circuit power. User scheduling and power allocation are optimized
across a cluster of coordinated base stations with a constraint on the maximum
transmit power (either per subcarrier or per base station). The asymptotic
noise-limited regime is discussed as a special case. %The performance of both
an isolated and a non-isolated cluster of coordinated base stations is examined
in the numerical experiments. Results show that the maximization of the energy
efficiency is approximately equivalent to the maximization of the spectral
efficiency for small values of the maximum transmit power, while there is a
wide range of values of the maximum transmit power for which a moderate
reduction of the data rate provides a large saving in terms of dissipated
energy. Also, the performance gap among the considered resource allocation
strategies reduces as the out-of-cluster interference increases.Comment: to appear on IEEE Transactions on Wireless Communication
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