Efficient and fair resource allocation strategies are being extensively studied in current research in order to address the requirements of future wireless applications. A novel resource allocation scheme is developed...
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Efficient and fair resource allocation strategies are being extensively studied in current research in order to address the requirements of future wireless applications. A novel resource allocation scheme is developed for orthogonal frequency-division multiplexing (OFDM) networks designed to maximise performance while limiting the received interference at each user. This received interference is in essence used as a fairness metric;moreover, by de. ning different interference tolerances for different sets of users, the proposed allocation scheme can be exploited in various cognitive radio scenarios. As applied to the scheme, the authors investigate a scenario where two cellular OFDM-based networks operate as primary and secondary systems in the same band, and the secondary system benefits by accessing the unused resources of the primary system if additional capacity is required. The primary system benefits either by charging the secondary system for the use of its resources or by some form of reciprocal arrangement allowing it to use the secondary system's licenced bands in a similar manner, when needed. Numerical results show our interference-limited scheduling approach to achieve excellent levels of efficiency and fairness by allocating resources more intelligently than proportional fair scheduling. A further important contribution is the application of sequential quadratic programming to solve the non-convex optimisation problems which arise in such scenarios.
Multi-scenario optimization is a convenient way to formulate and solve multi-set parameter estimation problems that arise from errorsin-variables-measured (EVM) formulations. These large-scale problems lead to nonline...
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Multi-scenario optimization is a convenient way to formulate and solve multi-set parameter estimation problems that arise from errorsin-variables-measured (EVM) formulations. These large-scale problems lead to nonlinear programs (NLPs) with specialized structure that can be exploited by the NLP solver in order to obtained more efficient solutions. Here we adapt the IPOPT barrier nonlinear programming algorithm to provide efficient parallel solution of multi-scenario problems. The recently developed object oriented framework, IPOPT 3.2, has been specifically designed to allow specialized linear algebra in order to exploit problem specific structure. This study discusses high-level design principles of IPOPT 3.2 and develops a parallel Schur complement decomposition approach for large-scale multi-scenario optimization problems. A large-scale case study example for the identification of an industrial low-density polyethylene (LDPE) reactor model is presented. The effectiveness of the approach is demonstrated through the solution of parameter estimation problems with over 4100 ordinary differential equations, 16,000 algebraic equations and 2100 degrees of freedom in a distributed cluster. (c) 2007 Elsevier Ltd. All rights reserved.
A standard Quadratic programming problem (StQP) consists in minimizing a (nonconvex) quadratic form over the standard simplex. For solving a SLQP we present an exact and a heuristic algorithm, that are based on new th...
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A standard Quadratic programming problem (StQP) consists in minimizing a (nonconvex) quadratic form over the standard simplex. For solving a SLQP we present an exact and a heuristic algorithm, that are based on new theoretical results for quadratic and convex optimization problems. With these results a StQP is reduced to a constrained nonlinear minimum weight clique problern in an associated graph. Such a Clique problem, which does not seem to have been Studied before, is then solved with all exact and a heuristic algorithm. Some computational experience shows that Our algorithms are able to solve StQP problems of at least one order of magnitude larger than those reported in the literature. (c) 2007 Elsevier B.V. All rights reserved.
This paper considers the problem of optimally placing fixed and switched type capacitors in a radial distribution network. The aim of this problem is to minimize the costs associated with capacitor banks, peak power, ...
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This paper considers the problem of optimally placing fixed and switched type capacitors in a radial distribution network. The aim of this problem is to minimize the costs associated with capacitor banks, peak power, and energy losses whilst satisfying a pre-specified set of physical and technical constraints. The proposed solution is obtained using a two-phase approach. In phase-I, the problem is formulated as a conic program in which all nodes are candidates for placement of capacitor banks whose sizes are considered as continuous variables. A global solution of the phase-I problem is obtained using an interior-point based conic programming solver. Phase-II seeks a practical optimal solution by considering capacitor sizes as discrete variables. The problem in this phase is formulated as a mixed integer linear program based on minimizing the L1-norm of deviations from the phase-I state variable values. The solution to the phase-II problem is obtained using a mixed integer linear programming solver. The proposed method is validated via extensive comparisons with previously published results. (C) 2007 Elsevier B.V. All rights reserved.
Low-thrust propulsion systems offer a fuel-efficient means to maneuver satellites to new orbits;however, they can only perform such maneuvers when they are continuously operated for a long time. Such long-term maneuve...
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Low-thrust propulsion systems offer a fuel-efficient means to maneuver satellites to new orbits;however, they can only perform such maneuvers when they are continuously operated for a long time. Such long-term maneuvers occur over many orbital revolutions, often rendering short time scale trajectory optimization methods ineffective. An approach to multirevolution large time scale optimal control of an electrodynamic tether is investigated for a tethered satellite system in low Earth orbit with atmospheric drag. Control is assumed to be periodic over several orbits because, under the assumptions of a nearly circular orbit, periodic control yields the only solution that significantly contributes to secular changes in the orbital parameters. The optimal control problem is constructed in such a way as to maneuver the satellite to a new orbit while minimizing a cost function subject to the constraints of the time-averaged equations of motion by controlling current in the tether. Three optimal maneuvers were investigated for a 4 km tether in a 270 km initial orbit: maximum climb, maximum final inclination, and a minimum time orbit change. The resulting control solutions were propagated to verify their accuracy.
We consider a procurement problem where suppliers offer concave quantity discounts. The resulting continuous knapsack problem involves the minimization of a sum of separable concave functions. We identify polynomially...
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We consider a procurement problem where suppliers offer concave quantity discounts. The resulting continuous knapsack problem involves the minimization of a sum of separable concave functions. We identify polynomially solvable special cases of. this NP-hard problem, and provide a fully polynomial-time approximation scheme for the general problem. (c) 2007 Elsevier B.V. All rights reserved.
Augmented Lagrangian methods for large-scale optimization usually require efficient algorithms for minimization with box constraints. On the other hand, active-set box-constraint methods employ unconstrained optimizat...
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Augmented Lagrangian methods for large-scale optimization usually require efficient algorithms for minimization with box constraints. On the other hand, active-set box-constraint methods employ unconstrained optimization algorithms for minimization inside the faces of the box. Several approaches may be employed for computing internal search directions in the large-scale case. In this paper a minimal-memory quasi-Newton approach with secant preconditioners is proposed, taking into account the structure of Augmented Lagrangians that come from the popular Powell-Hestenes-Rockafellar scheme. A combined algorithm, that uses the quasi-Newton formula or a truncated-Newton procedure, depending on the presence of active constraints in the penalty-Lagrangian function, is also suggested. Numerical experiments using the Cute collection are presented.
A new MINLP algorithm is presented for a class of problems whose formulation contains black-box models. Black-box models describe process behavior when closed-form equations are absent and can be functions of continuo...
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A new MINLP algorithm is presented for a class of problems whose formulation contains black-box models. Black-box models describe process behavior when closed-form equations are absent and can be functions of continuous and/or integer variables. To address the lack of explicit equations, kriging is used to build surrogate data-driven global models because a robust process description can be obtained even when noise is present. The global models are used to identify promising solutions for local refinement, and the continuous variables are then optimized using a response surface method. The integer variables are optimized using Branch-and-Bound if a continuous relaxation exists and direct search otherwise. The four algorithms are unified into a comprehensive approach that can be used to obtain optimal process synthesis and design solutions when noise and black-box models are present. The performance of the proposed algorithm is evaluated based on its application to two industrial case studies.
Wireless ad hoc networks have attracted a lot of attentions recently. Resource allocation in such networks needs to address both fairness and overall network performance. Pricing is a prospective direction to regulate...
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Wireless ad hoc networks have attracted a lot of attentions recently. Resource allocation in such networks needs to address both fairness and overall network performance. Pricing is a prospective direction to regulate behaviors of individual nodes while providing incentives for cooperation. In this work, we develop some pricing strategies for resource allocation by taking account of factors like multiple transmission rates and energy consumption of nodes, which have not been well studied in former works. Multi-rate transmission capability is commonly seen in most wireless products nowadays, while energy is one of the most important resources in portable devices. We propose a clique-based model which allows us to achieve optimal resource utilization and fairness among network flows when multi-rate transmission is considered. We also show how to extend the model to dynamically adjust prices based on energy consumptions of flows. In particular, our model takes into account energy consumptions in the transmitters' side, the receivers' side, and those that are non-transmitters and non-receivers but are interfered by these activities. So our model can more accurately reflect the real energy constraint in a wireless network. Simulation results are presented to show the convergence and other properties of these strategies. (C) 2008 Elsevier B.V. All rights reserved.
We propose a globalization strategy for nonlinear constrained optimization. The method employs a 'flexible' penalty function to promote convergence, where during each iteration the penalty parameter can be cho...
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We propose a globalization strategy for nonlinear constrained optimization. The method employs a 'flexible' penalty function to promote convergence, where during each iteration the penalty parameter can be chosen as any number within a prescribed interval, rather than a fixed value. This increased flexibility in the step acceptance procedure is designed to promote long productive steps for fast convergence. An analysis of the global convergence properties of the approach in the context of a line search sequential quadratic programming method and numerical results for the KNITRO software package are presented.
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