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A new evolutionary optimisation method for the operation of power systems with multiple storage resourcesThai, Cau Doan Hoang, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW January 2000 (has links)
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.
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New adaptive interior point algorithms for linear optimizationSalahi, Maziar. Terlaky, Tamás. January 1900 (has links)
Thesis (Ph.D.)--McMaster University, 2006. / Supervisor: Tamás Terlaky. Includes bibliographical references (p. 181-190).
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A Skeleton library for Cell Broadband Engine / Ett Skelettbibliotek för Cell Broadband EngineÅlind, Markus January 2008 (has links)
<p>The Cell Broadband Engine processor is a powerful processor capable of over 220 GFLOPS. It is highly specialized and can be controlled in detail by the programmer. The Cell is significantly more complicated to program than a standard homogeneous multi core processor such as the Intel Core2 Duo and Quad. This thesis explores the possibility to abstract some of the complexities of Cell programming while maintaining high performance. The abstraction is achieved through a library of parallel skeletons implemented in the bulk synchronous parallel programming environment NestStep. The library includes constructs for user defined SIMD optimized data parallel skeletons such as map, reduce and more. The evaluation of the library includes porting of a vector based scientific computation program from sequential C code to the Cell using the library and the NestStep environment. The ported program shows good performance when compared to the sequential original code run on a high-end x86 processor. The evaluation also shows that a dot product implemented with the skeleton library is faster than the dot product in the IBM BLAS library for the Cell processor with more than two slave processors.</p><p> </p>
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Advanced separation of concerns and the compatibility of aspect-orientationDechow, Doug 18 March 2005 (has links)
The appropriate separation of concerns is a fundamental engineering principle. A
concern, for software developers, is that which must be represented by code in a
program; by extension, separation of concerns is the ability to represent a single
concern in a single appropriate programming language construct. Advanced
separation of concerns is a relatively recent technique in software development for
dealing with the complexity of systems that contain crosscutting concerns, namely
those individual concerns that cut across programs. Aspect-oriented programming
(AOP), which is the area of this dissertation, offers a form of advanced separation of
concerns in which primary and crosscutting concerns can be separated during
problem solving. An aspect gathers into one place a concern that is or would
otherwise be scattered throughout an object-oriented program or system. The
primary aim of this dissertation-the AOPy project-is to investigate the usefulness
of advanced separation of concerns that aspect-oriented programming offers. In other
words, the AOPy Project determines whether the potential usefulness of aspect-oriented
programming is currently actualized in practice. In determining its current
practical usefulness, this dissertation also determines characteristics of and obstacles
to usefulness of aspect-orientation in software development. Perhaps the most
important contribution to understanding and addressing the problem of complexity in
software systems that this dissertation makes is that the AOPy research project
establishes a definition of compatibility of aspect-orientation and provides an
analysis of sample instances during problem solving that indicate evidence of
compatibility between object-orientation and aspect-orientation. Compatibility, as
defined by the AOPy Project, exists when aspect-oriented ideas, terminology, and
techniques are appropriately employed in the experimental problem-solving session.
The primary scientific contribution of this dissertation, therefore, is a narrative
description of the actual use of aspect-oriented programming in a series of
controlled, problem-solving scenarios. Theories describing the use of aspect-oriented
ideas, terminology, and techniques are generated and refined by means of Grounded
Theory, a qualitative data analysis technique. Because this dissertation 1)
analytically explores areas of compatibility of aspect-orientation with object-orientation
and 2) defines areas of compatibility thwarted in practice, this research
project can serve as a foundation for the development of aspect-oriented
programming-based design methodologies that encourage compatibility and
discourage non-compatibility. Therefore, the AOPy Project establishes a foundation
for future research in both its methodology and its results and for future software
development in practice. By contributing a definition of aspect-oriented
compatibility and a framework within which it can be understood, this dissertation
fosters the progression toward a seamless use of aspect-orientation between
developer and task. / Graduation date: 2005
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Similarity inheritance : a model of inheritance for declarative visual programming languagesDjang, Rebecca W. (Rebecca Walpole) 17 December 1998 (has links)
Declarative visual programming languages (VPLs), including spreadsheets, make
up a large portion of both research and commercial VPLs. Spreadsheets in particular
enjoy a wide audience, including end users. Unfortunately, spreadsheets and most other
declarative VPLs still suffer from some of the problems that have been solved in other
languages, such as ad-hoc (cut-and-paste) reuse of code which has been remedied in
object-oriented languages, for example, through the code-reuse mechanism of
inheritance. We believe spreadsheets and other declarative VPLs can benefit from the
addition of an inheritance-like mechanism for fine-grained code reuse. This dissertation
first examines the opportunities for supporting reuse inherent in declarative VPLs, and
then introduces similarity inheritance and describes a prototype of this model in the
research spreadsheet language Forms/3. Similarity inheritance is very flexible, allowing
multiple granularities of code sharing and even mutual inheritance; it includes explicit
representations of inherited code and all sharing relationships, and it subsumes the
current spreadsheet mechanisms for formula propagation, providing a gradual migration
from simple formula reuse to more sophisticated uses of inheritance among objects.
Since the inheritance model separates inheritance from types, we investigate what notion
of types is appropriate to support reuse of functions on different types (operation
polymorphism). Because it is important to us that immediate feedback, which is
characteristic of many VPLs, be preserved, including feedback with respect to type
errors, we introduce a model of types suitable for static type inference in the presence of
operation polymorphism with similarity inheritance. / Graduation date: 1999
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Lifting TransformationsMcAllester, David, Siskind, Jeffrey 01 December 1991 (has links)
Lifting is a well known technique in resolution theorem proving, logic programming, and term rewriting. In this paper we formulate lifting as an efficiency-motivated program transformation applicable to a wide variety of nondeterministic procedures. This formulation allows the immediate lifting of complex procedures, such as the Davis-Putnam algorithm, which are otherwise difficult to lift. We treat both classical lifting, which is based on unification, and various closely related program transformations which we also call lifting transformations. These nonclassical lifting transformations are closely related to constraint techniques in logic programming, resolution, and term rewriting.
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Trust-Region Algorithms for Nonlinear Stochastic Programming and Mixed Logit ModelsBastin, Fabian 12 March 2004 (has links)
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.
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Advances in Portfolio Selection Under Discrete Choice Constraints: A Mixed-integer Programming Approach and HeuristicsStoyan, Stephen J. 03 March 2010 (has links)
Over the last year or so, we have witnessed the global effects and repercussions related to the field of finance. Supposed blue chip
stocks and well-established companies have folded and filed for bankruptcy, an event that might have thought to been absurd two
years ago. In addition, finance and investment science has grown over the past few decades to include a plethora of investment options and regulations. Now more than ever, developments in the field are carefully examined and researched by potential investors. This thesis involves an investigation and quantitative analysis of key money management problems. The primary area of interest is Portfolio Selection, where we develop advanced financial models that are designed for
investment problems of the 21st century.
Portfolio selection is the process involved in making large investment decisions to generate a collection of assets. Over the
years the selection process has evolved dramatically. Current portfolio problems involve a complex, yet realistic set of
managing constraints that are coupled to general historic risk and return models. We identify three well-known portfolio problems
and add an array of practical managing constraints that form three different types of Mixed-Integer Programs. The product is
advanced mathematical models related to risk-return portfolios, index tracking portfolios, and an integrated stock-bond portfolio selection model. The numerous sources of uncertainty are captured
in a Stochastic Programming framework, and Goal Programming techniques are used to facilitate various portfolio goals. The designs require the consideration of modelling elements and variables with respect to problem solvability. We
minimize trade-offs in modelling and solvability issues found in the literature by developing problem specific algorithms. The algorithms are tailored to each portfolio design and involve decompositions and heuristics that improve solution speed and quality. The result is the generation of portfolios that have intriguing financial outcomes and perform well with respect to the market.
Portfolio selection is as dynamic and complex as the recent economic situation. In this thesis we present and further develop
the mathematical concepts related to portfolio construction. We investigate the key financial problems mentioned above, and
through quantitative financial modelling and computational implementations we introduce current approaches and advancements in field of Portfolio Optimization.
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Advances in Portfolio Selection Under Discrete Choice Constraints: A Mixed-integer Programming Approach and HeuristicsStoyan, Stephen J. 03 March 2010 (has links)
Over the last year or so, we have witnessed the global effects and repercussions related to the field of finance. Supposed blue chip
stocks and well-established companies have folded and filed for bankruptcy, an event that might have thought to been absurd two
years ago. In addition, finance and investment science has grown over the past few decades to include a plethora of investment options and regulations. Now more than ever, developments in the field are carefully examined and researched by potential investors. This thesis involves an investigation and quantitative analysis of key money management problems. The primary area of interest is Portfolio Selection, where we develop advanced financial models that are designed for
investment problems of the 21st century.
Portfolio selection is the process involved in making large investment decisions to generate a collection of assets. Over the
years the selection process has evolved dramatically. Current portfolio problems involve a complex, yet realistic set of
managing constraints that are coupled to general historic risk and return models. We identify three well-known portfolio problems
and add an array of practical managing constraints that form three different types of Mixed-Integer Programs. The product is
advanced mathematical models related to risk-return portfolios, index tracking portfolios, and an integrated stock-bond portfolio selection model. The numerous sources of uncertainty are captured
in a Stochastic Programming framework, and Goal Programming techniques are used to facilitate various portfolio goals. The designs require the consideration of modelling elements and variables with respect to problem solvability. We
minimize trade-offs in modelling and solvability issues found in the literature by developing problem specific algorithms. The algorithms are tailored to each portfolio design and involve decompositions and heuristics that improve solution speed and quality. The result is the generation of portfolios that have intriguing financial outcomes and perform well with respect to the market.
Portfolio selection is as dynamic and complex as the recent economic situation. In this thesis we present and further develop
the mathematical concepts related to portfolio construction. We investigate the key financial problems mentioned above, and
through quantitative financial modelling and computational implementations we introduce current approaches and advancements in field of Portfolio Optimization.
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Assessing the Impact of Computer Programming in Understanding Limits and Derivatives in a Secondary Mathematics Classroomde Castro, Christopher H 07 May 2011 (has links)
This study explored the development of student’s conceptual understanding of limit and derivative when specific computational tools were utilized. Fourteen students from a secondary Advanced Placement Calculus AB course explored the limit and derivative concepts from calculus using computational tools in the Maple computer algebra system. Students worked in pairs utilizing the pair-programming collaborative model. Four groups of student pairs constructed computational tools and used them to explore the limit and derivative concepts. The remaining four student pairs were provided similar tools and asked to perform identical explorations.
A multiple embedded case design was utilized to explore ways students in two classes, a programming class P and a non-programming class N, constructed understandings focusing upon their interactions with each other and with the computational tools. The Action-Process-Object-Schema (APOS) conceptual model and Constructionist framework guided design and construction of the tools, outlined developmental goals and milestones, and provided interpretive context for analysis.
Results provided insights into the effective design and use of computational tools in fostering conceptual understanding. The study found the additional burden of programming redirected students’ attention away from the intended conceptual understandings. The study additionally found, however, that pre-constructed tools effectively promote conceptual understanding of the limit concept when coupled with a mature conceptual model of development. Four themes influencing development of these understandings emerged: An instructional focus on skills over concepts, the instructional sequence, the willingness and ability of students to adopt and utilize computational tools, and the ways cognitive conflict was mediated.
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