Cheung, Sin Shuen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 55-59). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Preliminaries --- p.5 / Chapter 2.1 --- Heuristics by Hoeffding and Janson --- p.5 / Chapter 2.2 --- Facts in Matrix Theory --- p.10 / Chapter 2.3 --- Facts in Probability --- p.11 / Chapter 3 --- General Chance-Constrained LMIs --- p.18 / Chapter 3.1 --- Probability Inequalities --- p.18 / Chapter 3.2 --- Safe Tractable Approximations --- p.22 / Chapter 4 --- Chance-Constrained Quadratically Perturbed LMIs --- p.27 / Chapter 4.1 --- Exact Proper Fractional Covers --- p.27 / Chapter 4.2 --- Bounding the Matrix Moment Generating Functions --- p.32 / Chapter 5 --- Computational Study --- p.39 / Chapter 5.1 --- Update Procedures for Safe Tractable Approximations --- p.39 / Chapter 5.2 --- A Numerical Example and Comparisons --- p.44 / Chapter 6 --- Conclusion --- p.54 / Bibliography --- p.55

Wong Siu Ham. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 131-138). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgments --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation for Integration --- p.2 / Chapter 1.2 --- Thesis Overview --- p.4 / Chapter 2 --- Preliminaries --- p.5 / Chapter 2.1 --- Constraint Programming --- p.5 / Chapter 2.1.1 --- Constraint Satisfaction Problems (CSP's) --- p.6 / Chapter 2.1.2 --- Satisfiability (SAT) Problems --- p.10 / Chapter 2.1.3 --- Systematic Search --- p.11 / Chapter 2.1.4 --- Local Search --- p.13 / Chapter 2.2 --- Linear Programming --- p.17 / Chapter 2.2.1 --- Linear Programming Problems --- p.17 / Chapter 2.2.2 --- Simplex Method --- p.19 / Chapter 2.2.3 --- Mixed Integer Programming Problems --- p.27 / Chapter 3 --- Integration of Constraint Programming and Linear Program- ming --- p.29 / Chapter 3.1 --- Problem Definition --- p.29 / Chapter 3.2 --- Related works --- p.30 / Chapter 3.2.1 --- Illustrating the Performances --- p.30 / Chapter 3.2.2 --- Improving the Searching --- p.33 / Chapter 3.2.3 --- Improving the representation --- p.36 / Chapter 4 --- A Scheme of Integration for Solving Constraint Satisfaction Prob- lem --- p.37 / Chapter 4.1 --- Integrated Algorithm --- p.38 / Chapter 4.1.1 --- Overview of the Integrated Solver --- p.38 / Chapter 4.1.2 --- The LP Engine --- p.44 / Chapter 4.1.3 --- The CP Solver --- p.45 / Chapter 4.1.4 --- Proof of Soundness and Completeness --- p.46 / Chapter 4.1.5 --- Compared with Previous Work --- p.46 / Chapter 4.2 --- Benchmarking Results --- p.48 / Chapter 4.2.1 --- Comparison with CLP solvers --- p.48 / Chapter 4.2.2 --- Magic Squares --- p.51 / Chapter 4.2.3 --- Random CSP's --- p.52 / Chapter 5 --- A Scheme of Integration for Solving General Constrained Opti- mization Problem --- p.68 / Chapter 5.1 --- Integrated Optimization Algorithm --- p.69 / Chapter 5.1.1 --- Overview of the Integrated Optimizer --- p.69 / Chapter 5.1.2 --- The CP Solver --- p.74 / Chapter 5.1.3 --- The LP Engine --- p.75 / Chapter 5.1.4 --- Proof of the Optimization --- p.77 / Chapter 5.2 --- Benchmarking Results --- p.77 / Chapter 5.2.1 --- Weighted Magic Square --- p.77 / Chapter 5.2.2 --- Template design problem --- p.78 / Chapter 5.2.3 --- Random GCOP's --- p.79 / Chapter 6 --- Conclusions and Future Work --- p.97 / Chapter 6.1 --- Conclusions --- p.97 / Chapter 6.2 --- Future work --- p.98 / Chapter 6.2.1 --- Detection of implicit equalities --- p.98 / Chapter 6.2.2 --- Dynamical variable selection --- p.99 / Chapter 6.2.3 --- Analysis on help of linear constraints --- p.99 / Chapter 6.2.4 --- Local Search and Linear Programming --- p.99 / Appendix --- p.101 / Proof of Soundness and Completeness --- p.101 / Proof of the optimization --- p.126 / Bibliography --- p.130

by Lui Oi Lun Irene. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 128-132). / Abstracts in English and Chinese. / List of Figures --- p.vii / List of Tables --- p.viii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Satisfiability Problem --- p.1 / Chapter 1.2 --- Motivation of the Research --- p.1 / Chapter 1.3 --- Overview of the Thesis --- p.2 / Chapter 2 --- Constraint Satisfaction Problem and Satisfiability Problem --- p.4 / Chapter 2.1 --- Constraint Programming --- p.4 / Chapter 2.2 --- Satisfiability Problem --- p.6 / Chapter 2.3 --- Methods in Solving SAT problem --- p.7 / Chapter 2.3.1 --- Davis-Putnam-Loveland Procedure --- p.7 / Chapter 2.3.2 --- SATZ by Chu-Min Li --- p.8 / Chapter 2.3.3 --- Local Search for SAT --- p.11 / Chapter 2.3.4 --- Integer Linear Programming Method for SAT --- p.12 / Chapter 2.3.5 --- Semidefinite Programming Method --- p.13 / Chapter 2.4 --- Softwares for SAT --- p.15 / Chapter 2.4.1 --- SAT01 --- p.15 / Chapter 2.4.2 --- "SATZ and SATZ213, contributed by Chu-Min Li" --- p.15 / Chapter 2.4.3 --- Others --- p.15 / Chapter 3 --- Integer Programming --- p.17 / Chapter 3.1 --- Introduction --- p.17 / Chapter 3.1.1 --- Formulation of IPs and BIPs --- p.18 / Chapter 3.1.2 --- Binary Search Tree --- p.19 / Chapter 3.2 --- Methods in Solving IP problem --- p.19 / Chapter 3.2.1 --- Branch-and-Bound Method --- p.20 / Chapter 3.2.2 --- Cutting-Plane Method --- p.23 / Chapter 3.2.3 --- Duality in Integer Programming --- p.26 / Chapter 3.2.4 --- Heuristic Algorithm --- p.28 / Chapter 3.3 --- Zero-one Optimization and Continuous Relaxation --- p.29 / Chapter 3.3.1 --- Introduction --- p.29 / Chapter 3.3.2 --- The Roof Dual expressed in terms of Lagrangian Relaxation --- p.30 / Chapter 3.3.3 --- Determining the Existence of a Duality Gap --- p.31 / Chapter 3.4 --- Software for solving Integer Programs --- p.33 / Chapter 4 --- Integer Programming Formulation for SAT Problem --- p.35 / Chapter 4.1 --- From 3-CNF SAT Clauses to Zero-One IP Constraints --- p.35 / Chapter 4.2 --- From m-Constrained IP Problem to Singly-Constrained IP Problem --- p.38 / Chapter 4.2.1 --- Example --- p.39 / Chapter 5 --- A Basic Branch-and-Bound Algorithm for the Zero-One Polynomial Maximization Problem --- p.42 / Chapter 5.1 --- Reason for choosing Branch-and-Bound Method --- p.42 / Chapter 5.2 --- Searching Algorithm --- p.43 / Chapter 5.2.1 --- Branch Rule --- p.44 / Chapter 5.2.2 --- Bounding Rule --- p.46 / Chapter 5.2.3 --- Fathoming Test --- p.46 / Chapter 5.2.4 --- Example --- p.47 / Chapter 6 --- Revised Bound Rule for Branch-and-Bound Algorithm --- p.55 / Chapter 6.1 --- Revised Bound Rule --- p.55 / Chapter 6.1.1 --- CPLEX --- p.57 / Chapter 6.2 --- Example --- p.57 / Chapter 6.3 --- Conclusion --- p.65 / Chapter 7 --- Revised Branch Rule for Branch-and-Bound Algorithm --- p.67 / Chapter 7.1 --- Revised Branch Rule --- p.67 / Chapter 7.2 --- Comparison between Branch Rule and Revised Branch Rule --- p.69 / Chapter 7.3 --- Example --- p.72 / Chapter 7.4 --- Conclusion --- p.73 / Chapter 8 --- Experimental Results and Analysis --- p.80 / Chapter 8.1 --- Experimental Results --- p.80 / Chapter 8.2 --- Statistical Analysis --- p.33 / Chapter 8.2.1 --- Analysis of Search Techniques --- p.83 / Chapter 8.2.2 --- Discussion of the Performance of SATZ --- p.85 / Chapter 9 --- Concluding Remarks --- p.87 / Chapter 9.1 --- Conclusion --- p.87 / Chapter 9.2 --- Suggestions for Future Research --- p.88 / Chapter A --- Searching Procedures for Solving Constraint Satisfaction Problem (CSP) --- p.91 / Chapter A.1 --- Notation --- p.91 / Chapter A.2 --- Procedures for Solving CSP --- p.92 / Chapter A.2.1 --- Generate and Test --- p.92 / Chapter A.2.2 --- Standard Backtracking --- p.93 / Chapter A.2.3 --- Forward Checking --- p.94 / Chapter A.2.4 --- Looking Ahead --- p.95 / Chapter B --- Complete Results for Experiments --- p.96 / Chapter B.1 --- Complete Result for SATZ --- p.96 / Chapter B.1.1 --- n =5 --- p.95 / Chapter B.1.2 --- n = 10 --- p.98 / Chapter B.1.3 --- n = 30 --- p.99 / Chapter B.2 --- Complete Result for Basic Branch-and-Bound Algorithm --- p.101 / Chapter B.2.1 --- n二5 --- p.101 / Chapter B.2.2 --- n = 10 --- p.104 / Chapter B.2.3 --- n = 30 --- p.107 / Chapter B.3 --- Complete Result for Revised Bound Rule --- p.109 / Chapter B.3.1 --- n = 5 --- p.109 / Chapter B.3.2 --- n = 10 --- p.112 / Chapter B.3.3 --- n = 30 --- p.115 / Chapter B.4 --- Complete Result for Revised Branch-and-Bound Algorithm --- p.118 / Chapter B.4.1 --- n = 5 --- p.118 / Chapter B.4.2 --- n = 10 --- p.121 / Chapter B.4.3 --- n = 30 --- p.124 / Bibliography --- p.128

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Advanced technologies, a world-wide trend to deregulation of power systems and environmental constraints have attracted increasing interest in the operation of electric power systems with multiple storage resources. Under the competitive pressure of the deregulation, new efficient solution techniques to adapt quickly to the changing marketplace are in demand. This thesis presents a new evolutionary method, Constructive Evolutionary Programming (CEP), for minimising the system operational cost of scheduling electric power systems with multiple storage resources. The method combines the advantages of Constructive Dynamic Programming and Evolutionary Programming. Instead of evolving the "primal" variables such as storage content releases and thermal generator outputs, CEP evolves the piecewise linear convex cost-to-go functions (i.e. the storage content value curves). The multi-stage problem of multi-storage power system scheduling is thus decomposed into many smaller one-stage subproblems with evolved cost-to-go functions. For each evolutionary individual, linear programming is used in the forward pass process to solve the dispatch subproblems and the total system operational cost over the scheduling period is assigned to its fitness. Case studies demonstrate that the proposed method is robust and efficient for multi-storage power systems, particularly large complex hydrothermal system with cascaded and pumped storages. Although the proposed method is in the early stage of development, relying on assumptions of piecewise linear convexity in a deterministic environment, methods for the incorporation of stochastic models, electrical network and nonlinear, non-convex and non-smooth models are discussed. In addition, a number of possible improvements are also outlined. Due to its simplicity but robustness and efficiency, there are potential research directions for the further development of this evolutionary optimisation method.

Software systems are getting larger and more complex, while the requirements are becoming vaguer and more rapidly changing. These trends make current software development more and more likely a team work. To integrate multiple developers into a coherent structured management process and make team software development a positive-sum game for both higher productivity and better quality, many team soft ware development methodologies have been proposed and practised. An emerging methodology is collaborative programming, which allows a group of programmers to work together on the same source code for design, implementation of individual components, and integration of individual components. Compared with other team software methodologies that only address needs in some phases or situations, collaborative programming is more adaptive to the variety of different phases or situations in a team software development process. A core technical component in collaborative programming is collaborative editing, which allows a group of programmers to view and edit the same source code. To support different phases or situations in an Internet-based collaborative programming process, collaborative editing must meet the requirements of supporting unconstrained, responsive, real-time collaborative editing; unconstrained, syncretic, non-real-time collaborative editing; and smooth, flexible switching between real-time and non-real-time collaborative editing. This thesis research contributes several novel techniques to address these requirements, and an Internet-based collaborative programming environment to integrate those techniques. These research contributions have advanced state-of-the-art technologies on col laborative editing for supporting Internet-based collaborative programming. First, we contribute a collaborative highlighting gestural communication technique for unconstrained, responsive, real-time collaborative editing. This technique is particularly effective in improving the quality of real-time interaction on text-based source code documents. The contribution to the operational transformation technology is the extension of the technology to support group awareness. It includes a package of operational transformation functions and transformation control algorithms for consistency maintenance in collaborative highlighting, and a flexible undo solution that has the capability of undoing any highlighting operation at any time. Second, we contribute a flexible operation-based merging technique for unconstrained, syncretic, non-real-time collaborative editing, which is efficient and has the capability of textually integrating all changes, and automatically detecting and resolving syntactic conflicts according to application-dependent user-specified policies. The contribution to the operational transformation technology is the extension of the technology to support unconstrained, syncretic, non-real-time collaborative editing. Its includes a log compression algorithm, a textual merging algorithm, and a syntactic merging algorithm. Moreover, we contribute a flexible notification technique to support flexible collaborative editing: unconstrained, responsive, real-time collaborative editing; uncon strained, syncretic, non-real-time collaborative editing; and smooth, flexible switching between them. The contribution to the operational transformation technology is the extension of the technology to support flexible collaborative editing. It includes a new transformation control algorithm that has a linear time complexity, two notification algorithms that support propagation and acceptance of any notifications at any time, and a notification propagation protocol that is efficient for both real-time and non-real-time collaborative editing.

The aim of the research is to investigate factors that may explain success in elementary computer programming at the tertiary level. The first phase of the research included the identification of possible explanatory factors through a literature review, a survey of students studying introductory computing, a focus-group session with teachers of computer programming and interviews with programming students. The second phase of the research that was called the main study, involved testing the identified factors. Two different groups of programming students - one group majoring in business computing and another majoring in computer science - completed a survey questionnaire. The findings of the research are as follows. Gender is of little significance for business students but there is an adverse gender penalty for females in computer science. Secondary school assessment is inversely related to outcomes in business computing but directly influences outcomes in the first programming unit in the computer science course. As in prior research, previous knowledge and experience were demonstrated to matter, A range of other variables was found to be of little importance. The research suggests that different problem-solving techniques might be relevant in business compared with those of use in computer science.

Thesis (M.S.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed December 7, 2007) Includes bibliographical references (p. 28).

Advanced technologies, a world-wide trend to deregulation of power systems and environmental constraints have attracted increasing interest in the operation of electric power systems with multiple storage resources. Under the competitive pressure of the deregulation, new efficient solution techniques to adapt quickly to the changing marketplace are in demand. This thesis presents a new evolutionary method, Constructive Evolutionary Programming (CEP), for minimising the system operational cost of scheduling electric power systems with multiple storage resources. The method combines the advantages of Constructive Dynamic Programming and Evolutionary Programming. Instead of evolving the "primal" variables such as storage content releases and thermal generator outputs, CEP evolves the piecewise linear convex cost-to-go functions (i.e. the storage content value curves). The multi-stage problem of multi-storage power system scheduling is thus decomposed into many smaller one-stage subproblems with evolved cost-to-go functions. For each evolutionary individual, linear programming is used in the forward pass process to solve the dispatch subproblems and the total system operational cost over the scheduling period is assigned to its fitness. Case studies demonstrate that the proposed method is robust and efficient for multi-storage power systems, particularly large complex hydrothermal system with cascaded and pumped storages. Although the proposed method is in the early stage of development, relying on assumptions of piecewise linear convexity in a deterministic environment, methods for the incorporation of stochastic models, electrical network and nonlinear, non-convex and non-smooth models are discussed. In addition, a number of possible improvements are also outlined. Due to its simplicity but robustness and efficiency, there are potential research directions for the further development of this evolutionary optimisation method.