@conference {
	title = {Per-se Privacy Preserving Solution Methods Based on Optimization},
	author = {Weeraddana, P. C., Athanasiou, G., Fischione, Carlo., Baras, John S.},
	year = {2013},
	month = {12/2013},
	abstract = {Ensuring privacy is an essential requirement in various contexts, such as social networks, healthcare data, ecommerce, banks, and government services. Here, different entities coordinate to address specific problems where the sensitive problem data are distributed among the involved entities and no entity wants to publish its data during the solution procedure. Existing privacy preserving solution methods are mostly based on cryptographic procedures and thus have the drawback of substantial computational complexity. Surprisingly, little attention has been devoted thus far to exploit mathematical optimization techniques and their inherent properties for preserving privacy. Yet, optimization based approaches to privacy require much less computational effort compared to cryptographic variants, which is certainly desirable in practice. In this paper, a unified framework for transformation based optimization methods that ensure privacy is developed. A general definition for the privacy in the context of transformation methods is proposed. A number of examples are provided to illustrate the ideas. It is concluded that the theory is still in its infancy and that huge benefits can be achieved by a substantial development.},
	address = {Florence, Italy},
	booktitle = {52nd IEEE Conference on Decision and Control},
	pages = {pp. 206-211}
}
@conference {
	title = {Asymptotic Consensus Solutions in Non-Linear Delayed Distributed Algorithms Under Deterministic \& Stochastic Perturbations},
	author = {Somarakis, Christoforos., Baras, John S.},
	year = {2013},
	month = {12/2013},
	abstract = {We consider a multi-agent non-linear delayed model which sustains consensus type of solutions. We use fixed point theory arguments to establish sufficient conditions for existence and uniqueness of solutions that converge exponentially fast to a common value with prescribed rate. The conditions depend on the communication topology, the nonlinearity of the model as well as the delay in the propagation of information. Furthermore we test the robustness of our results in the presence of independent time varying perturbations both deterministic and stochastic.},
	address = {Florence, Italy},
	booktitle = {52nd IEEE Conference on Decision and Control},
	pages = {pp. 7821-7826}
}
@conference {
	title = {Distributed Algorithms for Optimization Problems with Equality Constraints},
	author = {Matei, Ion., Baras, John S.},
	year = {2013},
	month = {12/2013},
	abstract = {In this paper we introduce two discrete-time, distributed optimization algorithms executed by a set of agents whose interactions are subject to a communication graph. The algorithms can be applied to optimization problems where the cost function is expressed as a sum of functions, and where each function is associated to an agent. In addition, the agents can have equality constraints as well. The algorithms are not consensus-based and can be applied to non-convex optimization problems with equality constraints. We demonstrate that the first distributed algorithm results naturally from applying a first order method to solve the first order necessary conditions for a lifted optimization problem with equality constraints; the solution of our original problem is embedded in the solution of this lifted optimization problem. Using an augmented Lagrangian idea, we derive a second distributed algorithm that requires weaker conditions for local convergence compared to the first algorithm. For both algorithms we address the local convergence properties.},
	address = {Florence, Italy},
	booktitle = {52nd IEEE Conference on Decision and Control},
	pages = {pp. 2352-2357}
}
@conference {
	title = {A Distributed Learning Algorithm with Bit-valued Communications for Multi-agent Welfare Optimization},
	author = {Menon, Anup., Baras, John S.},
	year = {2013},
	month = {12/2013},
	abstract = {A multi-agent system comprising N agents, each picking actions from a finite set and receiving a payoff that depends on the action of the whole, is considered. The exact form of the payoffs are unknown and only their values can be measured by the respective agents. A decentralized algorithm was proposed by Marden et al. and in the authors{\textquoteright} earlier work that, in this setting, leads to the agents picking welfare optimizing actions under some restrictive assumptions on the payoff structure. This algorithm is modified in this paper to incorporate exchange of certain bit-valued information between the agents over a directed communication graph. The notion of an interaction graph is then introduced to encode known interaction in the system. Restrictions on the payoff structure are eliminated and conditions that guarantee convergence to welfare minimizing actions w.p. 1 are derived under the assumption that the union of the interaction graph and communication graph is strongly connected.},
	address = {Florence, Italy},
	booktitle = {Proceedings of the 52nd IEEE Conference on Decision and Control,},
	pages = {pp. 2406-2411}
}
@conference {
	title = {Coordinated Scheduling and Power Control for Downlink Cross-tier Interference Mitigation in Heterogeneous Cellular Networks},
	author = {Sung, Doo-hyun., Baras, John S., Zhu, Chenxi.},
	year = {2013},
	month = {12/2013},
	abstract = {In heterogeneous cellular networks, the deployment~of low-powered picocells provides user offloading and capacity~enhancement. The expansion of a picocell{\textquoteright}s coverage by adding~a positive bias for cell association can maximize these effects.~Under this circumstance, downlink cross-tier interference from~a macro base station to pico mobile stations in the expanded~picocell range deteriorates those pico mobile stations{\textquoteright} performance~significantly. In this paper, a coordinated scheduling~and power control algorithm is proposed, whereby the macro~base station reduces its transmission power for those victim~pico mobile stations in the expanded picocell range only on a~set of resource blocks to minimize performance degradation at~the macro base station. First, the transmission power level is~calculated based on the mobile stations{\textquoteright} channel condition and
QoS requirements. Then, a set of resource blocks is determined~by solving a binary integer programming to minimize the sum~of transmission power reduction subject to victim pico mobile~stations{\textquoteright} QoS constraints. To reduce computational complexity,~we utilize a heuristic algorithm, i.e., max-min greedy method,~to solve the problem. Through system level simulations, we~show that average and 5\%-ile throughputs of victim pico mobile~stations are significantly improved.},
	address = {Atlanta, Georgia},
	booktitle = {IEEE Globecom 2013},
	pages = {pp. 3914-3918}
}
