ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / Decoding and Turbo Equalization (TEQ) algorithms based on the Sum-Product Algorithm (SPA) are well established for LDPC codes. However there is increasing interest in linear and nonlinear programming (NLP)-based decoders which may offer computational and performance advantages over the SPA. We present NLP decoders and Turbo equalizers based on an Augmented Lagrangian formulation of the decoding problem. The decoders update estimates of both the Lagrange multipliers and transmitted codeword while solving an approximate quadratic programming problem. Simulation results show that the NLP decoder performance is intermediate between the SPA and bit-flipping algorithms. The NLP may thus be attractive in some applications as it eliminates the tanh/atanh computations in the SPA.

published_or_final_version / abstract / Electrical and Electronic Engineering / Master / Master of Philosophy

Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE. / Bibliography: leaves 412-417. / by Siri Jirapongphan. / Sc.D.

This thesis is aimed at the optimum design of uncertain mechanical components and systems involving multiple objectives and constraints. There are various mechanical and design problems that are encountered every now and then which require the output that equalize several conflicting objectives. In recent years several methods have been developed to find a solution to multiobjective problems. The most efficient method for obtaining a compromise solution is the game theory method, which is based on the Pareto minimum or optimum solution. A thorough methodology is developed, and subsequently applied to three examples problems. The first problem is to design four helical springs which are further used to support a milling machine. The objective is to minimize the weight of the spring, also to minimize the deflection, and to maximize the natural frequency thus making the problem as a multiobjective problem. Further the subjected constraint is the shear stress constraint. After finding the optimized solution of the deterministic problem, the problem is again solved using Stochastic Nonlinear Programming, and after that it is solved using Interval Analysis. Game theory is used individually in all the three cases. The second problem is to design a gear box where the objectives are defined as the weight of the gear box, stress developed in the shaft 1, and the stress developed in shaft 2. It is subjected to nine constraints which are bending stress in teeth, contact stress of teeth, transverse displacement of shafts 1 and 2, and constraints related to the torque. The third problem is to design a power screw and the objective is to minimize the volume of the screw, and to maximize the critical buckling load and thus making it a multiobjective problem. It is subjected to constraints of being screw to be self locking, then the shear stress in screw thread, and the bearing stress in threads. The results of all the three problems that are achieved using Deterministic, Stochastic Nonlinear Programming, and Interval Analysis Method are tabulated, and the value of each objective achieved using these three methods for each problem at a time are compared to find out the most optimized solution.

This work is concerned with the study of nonlinear nonconvex stochastic programming, in particular in the context of trust-region approaches. We first explore how to exploit the structure of multistage stochastic nonlinear programs with linear constraints, in the framework of primal-dual interior point methods. We next study consistency of sample average approximations (SAA) for general nonlinear stochastic programs. We also develop a new algorithm to solve the SAA problem, using the statistical inference information to reduce numercial costs, by means of an internal variable sample size strategy. We finally assess the numerical efficiency of the proposed method for the estimation of discrete choice models, more precisely mixed logit models, using our software AMLET, written for this purpose.

This study addresses the problem of designing a new natural gas transmission network or expanding an existing network while minimizing the total investment and operating costs. A substantial reduction in costs can be obtained by effectively designing and operating the network. A well-designed network helps natural gas companies minimize the costs while increasing the customer service level. The aim of the study is to determine the optimum installation scheduling and locations of new pipelines and compressor stations. On an existing network, the model also optimizes the total flow through pipelines that satisfy demand to determine the best purchase amount of gas. A mixed integer nonlinear programming model for steady-state natural gas transmission problem on tree-structured network is introduced. The problem is a multi-period model, so changes in the network over a planning horizon can be observed and decisions can be made accordingly in advance. The problem is modeled and solved with easily accessible modeling and solving tools in order to help decision makers to make appropriate decisions in a short time. Various test instances are generated, including problems with different sizes, period lengths and cost parameters, to evaluate the performance and reliability of the model. Test results revealed that the proposed model helps to determine the optimum number of periods in a planning horizon and the crucial cost parameters that affect the network structure the most.

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The development of infectious disease models remains important to provide scientists with tools to better understand disease dynamics and develop more effective control strategies. In this work we focus on the estimation of seasonally varying transmission parameters in infectious disease models from real measles case data. We formulate both discrete-time and continuous-time models and discussed the benefits and shortcomings of both types of models. Additionally, this work demonstrates the flexibility inherent in large-scale nonlinear programming techniques and the ability of these techniques to efficiently estimate transmission parameters even in very large-scale problems. This computational efficiency and flexibility opens the door for investigating many alternative model formulations and encourages use of these techniques for estimation of larger, more complex models like those with age-dependent dynamics, more complex compartment models, and spatially distributed data. How- ever, the size of these problems can become excessively large even for these powerful estimation techniques, and parallel estimation strategies must be explored. Two parallel decomposition approaches are presented that exploited scenario based de- composition and decomposition in time. These approaches show promise for certain types of estimation problems.

Thesis (M. Eng.)(Industrial)--University of Pretoria, 2004. / Includes summary. Includes bibliographical references.

Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2007. / Yorai Wardi, Committee Member ; Gunter Sharp, Committee Member ; Spiridon Reveliotis, Committee Member ; Leon F. McGinnis, Committee Member ; Chen Zhou, Committee Chair.