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41 
A solution to optimization problems with discontinuitiesMcLennan, Clyde Jack, 1939 January 1967 (has links)
No description available.

42 
The specification and synthesis of highorder control systemsDial, Joseph Hubbard, 1939 January 1968 (has links)
No description available.

43 
Complex quadratic optimization via semidefinite programming: models and applications. / CUHK electronic theses & dissertations collectionJanuary 2005 (has links)
Finally, as combinatorial applications of complex quadratic optimization; we consider Max 3Cut with fixed nodes constraints, Max 3Dicut with weight constraints, Max 3XOR, and so on, and present corresponding bounds on the approximation ratios. / Quadratic optimization problems with complexvalued decision variables, in short called complex quadratic optimization problems, find many applications in engineering. In this dissertation, we study several instructive models of complex quadratic optimization, as well as its applications in combinatorial optimization. The tool that we use is a combination of semidefinite programming (SDP) relaxation and randomization technique, which has been well exploited in the last decade. Since most of the optimization models are NPhard in nature, we shall design polynomial time approximation algorithms for a general model, or polynomial time exact algorithms for some restricted instances of a general model. / To enable the analysis, we first develop two basic theoretical results: one is the probability formula for a bivariate complex normal distribution vector to be in a prescribed angular region, and the other one is the rankone decomposition theorem for complex positive semidefinite matrices. The probability formula enables us to compute the expected value of a randomized (with a specific rounding rule) solution based on an optimal solution of the SDP relaxation problem, while the rankone decomposition theorem provides a new proof of the complex S lemma and leads to novel deterministic rounding procedures. / With the above results in hand, we then investigate the models and applications of complex quadratic optimization via semidefinite programming in detail. We present an approximation algorithm for a convex quadratic maximization problem with discrete complex decision variables, where the approximation analysis is based on the probability formula. Besides, an approximation algorithm is proposed for a nonconvex quadratic maximization problem with discrete complex decision variables. Then we study a limit of the model, i.e., a quadratic maximization problem with continuous unit module decision variables. The problem is shown to be strongly NPhard. Approximation algorithms are described for the problem, including both convex case and nonconvex case. Furthermore, if the objective matrix has a sign structure, then a stronger approximation result is shown to hold. In addition, we use the complex matrix decomposition theorem to solve complex quadratically constrained complex quadratic programming. We consider several interesting cases for which the corresponding SDP relaxation admits no gap with the true optimal value, and consequently, this yields polynomial time procedures for solving those special cases of complex quadratic optimization. / Huang Yongwei. / "August 2005." / Adviser: Shuzhong Zhang. / Source: Dissertation Abstracts International, Volume: 6707, Section: B, page: 4033. / Thesis (Ph.D.)Chinese University of Hong Kong, 2005. / Includes bibliographical references (p. 142155). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

44 
A study an analysis of stochastic linear programmingFoes, Chamberlain Lambros 01 May 1970 (has links)
This essay investigates the concept of linear programming in general and linear stochastic programming in particular. Linear stochastic programming is described as the model where the parameters of the linear programming admit random variability. The first three chapters present through a setgeometric approach the foundations of linear programming. Chapter one describes the evolution of the concepts which resulted in the adoption of the model. Chapter two describes the constructs in ndimensional euclidian space which constitute the mathematical basis of linear programs, and chapter three defines the linear programming model and develops the computational basis of the simplex algorithm. The second three chapters analyze the effect of the introduction of risk into the linear programming model. The different approaches of estimating and measuring risk are studied and the difficulties arising in formulating the stochastic problem and deriving the equivalent deterministic problems are treated from the theoretical and practical point of view. Multiple examples are given throughout the essay for clarification of the salient points.

45 
Interior point based continuous methods for linear programmingSun, Liming 01 January 2012 (has links)
No description available.

46 
An application of linear programming to the scheduling of toll collectorsByrne, John Leonard January 1970 (has links)
iv, 115 leaves : ill. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.1972) from the Dept. of Mathematics, University of Adelaide

47 
Computer Solution of Linear Programming Problems Using the Decomposition AlgorithmKraslawsky, Walter Paul 01 January 1973 (has links)
No description available.

48 
The linear programming problem /Dessouky, Mohamed Ibrahim January 1956 (has links)
No description available.

49 
Algorithms for the handcomputation solution of the transhipment problem and maximum flow in a restricted network /Brown, Edward A. January 1959 (has links)
No description available.

50 
On a special class of problems in integer linear programming /Nelson, Larry Dean January 1965 (has links)
No description available.

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