Pang J.-S.,Enterprise Systems |
Scutari G.,State University of New York at Buffalo
IEEE Transactions on Signal Processing | Year: 2013
In this paper, we propose a novel class of Nash problems for Cognitive Radio (CR) networks composed of multiple primary users (PUs) and secondary users (SUs) wherein each SU (player) competes against the others to maximize his own opportunistic throughput by choosing jointly the sensing duration, the detection thresholds, and the vector power allocation over a multichannel link. In addition to power budget constraints, several (deterministic or probabilistic) interference constraints can be accommodated in the proposed general formulation, such as constraints on the maximum individual/aggregate (probabilistic) interference tolerable from the PUs. To keep the optimization as decentralized as possible, global interference constraints, when present, are imposed via pricing; the prices are thus additional variables to be optimized. The resulting players' optimization problems are nonconvex and there are price clearance conditions associated with the nonconvex global interference constraints to be satisfied by the equilibria of the game, which make the analysis of the proposed game a challenging task; none of classical results in the game theory literature can be successfully applied. To deal with the nonconvexity of the game, we introduce a relaxed equilibrium concept $-$ the Quasi-Nash Equilibrium (QNE)-and study its main properties, performance, and connection with local Nash equilibria. Quite interestingly, the proposed game theoretical formulations yield a considerable performance improvement with respect to current centralized and decentralized designs of CR systems, which validates the concept of QNE. © 1991-2012 IEEE. Source
Nedic A.,Enterprise Systems
Mathematical Programming | Year: 2011
This paper deals with iterative gradient and subgradient methods with random feasibility steps for solving constrained convex minimization problems, where the constraint set is specified as the intersection of possibly infinitely many constraint sets. Each constraint set is assumed to be given as a level set of a convex but not necessarily differentiable function. The proposed algorithms are applicable to the situation where the whole constraint set of the problem is not known in advance, but it is rather learned in time through observations. Also, the algorithms are of interest for constrained optimization problems where the constraints are known but the number of constraints is either large or not finite. We analyze the proposed algorithm for the case when the objective function is differentiable with Lipschitz gradients and the case when the objective function is not necessarily differentiable. The behavior of the algorithm is investigated both for diminishing and non-diminishing stepsize values. The almost sure convergence to an optimal solution is established for diminishing stepsize. For non-diminishing stepsize, the error bounds are established for the expected distances of the weighted averages of the iterates from the constraint set, as well as for the expected sub-optimality of the function values along the weighted averages. © Springer and Mathematical Optimization Society 2011. Source
Lee S.,Urbana University |
Nedic A.,Enterprise Systems
IEEE Journal on Selected Topics in Signal Processing | Year: 2013
Random projection algorithm is of interest for constrained optimization when the constraint set is not known in advance or the projection operation on the whole constraint set is computationally prohibitive. This paper presents a distributed random projection algorithm for constrained convex optimization problems that can be used by multiple agents connected over a time-varying network, where each agent has its own objective function and its own constrained set. We prove that the iterates of all agents converge to the same point in the optimal set almost surely. Experiments on distributed support vector machines demonstrate good performance of the algorithm. © 2013 IEEE. Source
Agency: Cordis | Branch: FP7 | Program: CP | Phase: ENERGY.2013.5.1.2 | Award Amount: 5.71M | Year: 2013
Carbon capture and storage (CCS) is one of the technological solutions to decarbonize the energy market while providing secure energy supply. So far, the cost of CCS is dominated by the CO2 capture, reason why new capture techniques should be developed. Adsorption techniques have already been evaluated for CO2 capture. So far, the main drawbacks of this technique are the energetic demand to regenerate the adsorbent and obtain high purity CO2. However, the utilization of commercially available materials was employed in the former evaluations. New materials with targeted properties to capture CO2 from flue gases can improve the performance of adsorption processes significantly. The vision of MATESA is to develop an innovative post-combustion capture termed as Electric Swing Adsorption (ESA). The utilization of hybrid CO2 honeycomb monoliths with high-loading CO2 materials (zeolites and MOFs) will be targeted. Classical ESA regeneration is done by passing electricity through the adsorbent, releasing adsorbed CO2 that can be recovered at high purity. A game-changing innovation in MATESA is the development of a regeneration protocol where electricity is only used to increase the purity of CO2 in the column and further regeneration is done using available low-grade heat. The predicted energy savings of the developed process may transform this CO2 capture process in a key component to make CCS commercially feasible in fossil fuel power plants going into operation after 2020. In order to realize a proof of concept of the proposed process, a strong component of the project will deal with the development of a hybrid material that is able to selectively adsorb CO2, conduct electricity, result in a low pressure drop and have reduced environmental impact. The development of such a material is important for MATESA and will also have a significant impact to increase the energy efficiency of pre-combustion CO2 capture and other energy intensive gas separations.
Agency: GTR | Branch: Innovate UK | Program: | Phase: Collaborative Research & Development | Award Amount: 580.34K | Year: 2014
This project brings together experts in drug development, product formulation, process design, systems modelling and manufacture to create a completely new approach to the design and manufacture of formulated drug products, which involves the integration of qualitative tools for process understanding with a range of in-silico models which describe and predict processing and product performance. It is anticipated that successful outcomes of digital design of drug formulations as envisaged in this proposal via the creation of “Design Space Explorer” will provide unparalleled improvements in reliability, quality and manufacturing processes of pharmaceutical products leading to greater trust by regulatory agencies and by society. Furthermore it is anticipated that a successful outcome to the proposed project has potential to significantly decrease the costs and times associated with the development of new medicines whilst also reducing, refining and and at times removing the need for some clinical studies in patients and healthy volunteers.