Although initially proposed as the deployable alternative to IP multicast, the overlay network actually revolutionizes the way network applications can be built. In this paper, we study the rate allocation problem in ...
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Although initially proposed as the deployable alternative to IP multicast, the overlay network actually revolutionizes the way network applications can be built. In this paper, we study the rate allocation problem in overlay-based multirate multicast, which can be understood as a utility-based resource allocation problem. Each receiver is associated with a utility defined as a function of its streaming rate. Our goal is to maximize the aggregate utility of all receivers, subject to network capacity constraint and data constraint. The latter constraint is unique in overlay multicast, mainly due to the dual role of end hosts as both receivers and senders. We use a price-based approach to address this problem. Two types of prices, network price and data price, are generated with regard to the two constraints of the problem. A distributed algorithm is proposed, where each receiver adjusts its flow rate according to the associated network price and data price. The algorithm is proved to converge to the optimal point, where the aggregate utility of all receivers is maximized. We implement our algorithm using an end-host-based protocol. Our protocol purely relies on the coordination of end hosts to accomplish tasks originally assigned to network routers, which makes it directly deployable to the existing network infrastructure.
The preliminary design of multiple gravity-assist-trajectories is formulated as a global optimization problem. An analysis of the structure of the solution space reveals a strong multimodality, which is strictly depen...
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The preliminary design of multiple gravity-assist-trajectories is formulated as a global optimization problem. An analysis of the structure of the solution space reveals a strong multimodality, which is strictly dependent on the complexity of the model. On the other hand, it is shown how an oversimplification could prevent finding potentially interesting solutions-A trajectory model, which represents a compromise between model completeness and optimization problem complexity, is then presented. The exploration of the resulting solution space is performed through a novel global search approach, which hybridizes an evolutionary-based algorithm with a systematic branching strategy. This approach allows an efficient exploration of complex solution domains by automatically balancing local convergence and global search. A number of difficult multiple gravity-assist trajectory design cases demonstrates the effectiveness of the proposed methodology.
Simultaneous dynamic optimization strategies, also known as direct transcription methods, have been applied in a wide number of domains. These include off-line applications like optimal control, trajectory planning, n...
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Simultaneous dynamic optimization strategies, also known as direct transcription methods, have been applied in a wide number of domains. These include off-line applications like optimal control, trajectory planning, nonlinear parameter estimation and optimization of reactors and batch processes, as well as on-line applications such as nonlinear model predictive control, nonlinear state estimation and dynamic, real-time optimization. Here we discuss recent advances for the simultaneous approach and emphasize the characteristics, benefits and challenges related to these strategies. In particular, we compare the properties of solutions generated by simultaneous approaches to those of classical variational methods, for a variety of problem classes. We next demonstrate why simultaneous strategies are especially beneficial for dynamic systems with unstable modes, with path constraints and for large-scale, structured problems. Finally, we outline a number of challenges and open research questions that will further improve the effectiveness of these methods on a wider range of applications. (c) 2006 Elsevier Ltd. All rights reserved.
Multidisciplinary design optimization (MDO) problems are engineering design problems that require the consideration of the interaction between several design disciplines. Due to the organizational aspects of MDO probl...
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Multidisciplinary design optimization (MDO) problems are engineering design problems that require the consideration of the interaction between several design disciplines. Due to the organizational aspects of MDO problems, decomposition algorithms are often the only feasible solution approach. Decomposition algorithms reformulate the MDO problem as a set of independent subproblems, one per discipline, and a coordinating master problem. A popular approach to MDO problems is bilevel decomposition algorithms. These algorithms use nonlinear optimization techniques to solve both the master problem and the subproblems. In this paper, we propose two new bilevel decomposition algorithms and analyze their properties. In particular, we show that the proposed problem formulations are mathematically equivalent to the original problem and that the proposed algorithms converge locally at a superlinear rate. Our computational experiments illustrate the numerical performance of the algorithms.
The problem of minimizing a separable nonlinear objective function under linear constraints is considered in this paper. A systematic approach is proposed to obtain an approximately globally optimal solution via piece...
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The problem of minimizing a separable nonlinear objective function under linear constraints is considered in this paper. A systematic approach is proposed to obtain an approximately globally optimal solution via piecewise-linear approximation. By means of the new approach a minimum point of the original problem confined in a region where more than one linear piece is needed for satisfactory approximation can be found by solving only one linear programming problem. Hence, the number of linear programming problems to be solved for finding the approximately globally optimal solution may be much less than that of the regions partitioned. In addition, zero-one variables are not introduced in this approach. These features are desirable for efficient computation. The practicability of the approach is demonstrated by an example. (c) 2005 Elsevier B.V. All rights reserved.
In this paper we consider the question of solving equilibriumproblems-formulated as complementarity problems and, more generally, mathematical programs withequilibrium constraints (MPECs)-as nonlinear programs, using ...
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In this paper we consider the question of solving equilibriumproblems-formulated as complementarity problems and, more generally, mathematical programs withequilibrium constraints (MPECs)-as nonlinear programs, using an interior-point approach. Theseproblems pose theoretical difficulties for nonlinear solvers, including interior-point methods. Weexamine the use of penalty methods to get around these difficulties and provide substantialnumerical results. We go on to show that penalty methods can resolve some problems thatinterior-point algorithms encounter in general.
The dynamics of air manifold and fuel injection of the spark ignition engines are severely nonlinear. This is reflected in nonlinearities of the model parameters in different regions of the operating space. Control of...
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The dynamics of air manifold and fuel injection of the spark ignition engines are severely nonlinear. This is reflected in nonlinearities of the model parameters in different regions of the operating space. Control of the engines has been investigated using observer-based methods or sliding-mode methods. In this paper, the model predictive control (MPC) based on a neural network model is attempted for air-fuel ratio, in which the model is adapted on-line to cope with nonlinear dynamics and parameter uncertainties. A radial basis function (RBF) network is employed and the recursive least-squares (RLS) algorithm is used for weight updating. Based on the adaptive model, a MPC strategy for controlling air-fuel ratio is realised to a nonlinear simulation of the engines, and its control performance is compared with that of a conventional PI controller. A reduced Hessian method, a new developed sequential quadratic programming (SQP) method for solving nonlinear programming (NLP) problems, is implemented to speed up the nonlinear optimisation in MPC. (c) 2005 Elsevier Ltd. All rights reserved.
This paper describes an approach to determine a layout for the order picking area in warehouses, so that the average travel distance for the order pickers is minimized. We give analytical formulas that can be used to ...
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This paper describes an approach to determine a layout for the order picking area in warehouses, so that the average travel distance for the order pickers is minimized. We give analytical formulas that can be used to calculate the average length of an order picking route under two different routing policies. The optimal layout can be determined by using these formulas as the objective function in a nonlinear programming model. The optimal number of aisles in an order picking area appears to depend strongly on the required storage space and the pick list size.
The paper presents the cost optimization of composite floor trusses composed from a reinforced concrete slab of constant depth and steel trusses consisting of hot rolled channel sections. The optimization was performe...
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The paper presents the cost optimization of composite floor trusses composed from a reinforced concrete slab of constant depth and steel trusses consisting of hot rolled channel sections. The optimization was performed by the nonlinear programming approach, NLP. Accordingly, a NLP optimization model for composite floor trusses was developed. An accurate objective function of the manufacturing material, power and labour costs was proposed to be defined for the optimization. Alongside the costs, the objective function also considers the fabrication times, and the electrical power and material consumption. Composite trusses were optimized according to Eurocode 4 for the conditions of both the ultimate and the serviceability limit states. A numerical example of the optimization of the composite truss system presented at the end of the paper demonstrates the applicability of the proposed approach.
A previous analysis of second-order behavior of generalized pattern search algorithms for unconstrained and linearly constrained minimization is extended to the more general class of mesh adaptive direct search ( MADS...
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A previous analysis of second-order behavior of generalized pattern search algorithms for unconstrained and linearly constrained minimization is extended to the more general class of mesh adaptive direct search ( MADS) algorithms for general constrained optimization. Because of the ability of MADS to generate an asymptotically dense set of search directions, we are able to establish reasonable conditions under which a subsequence of MADS iterates converges to a limit point satisfying second-order necessary or sufficient optimality conditions for general set-constrained optimization problems.
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