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Optimal trade execution aims at balancing price impact and timing risk. With respect to the mathematical formulation of the optimization problem, we primarily focus on Mean Variance (MV) optimization, in which the two conflicting objectives are maximizing expected revenue (the flip side of trading impact) and minimizing variance of revenue (a measure of timing risk). We also consider the use of expected quadratic variation of the portfolio value process as an alternative measure of timing risk, which leads to Mean Quadratic Variation (MQV) optimization. We demonstrate that MV-optimal strategies are quite different from MQV-optimal strategies in many aspects. These differences are in stark contrast to the common belief that MQV-optimal strategies are similar to, or even the same as, MV-optimal strategies. These differences should be of interest to practitioners since we prove that the classic Almgren-Chriss strategies (industry standard) are MQV-optimal, in contrary to the common belief that they are MV-optimal. From a computational point of view, we extend theoretical results in the literature to prove that the mean variance efficient frontier computed using our method is indeed the complete Pareto-efficient frontier. First, we generalize the result in Li (2000) on the embedding technique and develop a post-processing algorithm that guarantees Pareto-optimality of numerically computed efficient frontier. Second, we extend the convergence result in Barles (1990) to viscosity solution of a system of nonlinear Hamilton Jacobi Bellman partial differential equations (HJB PDEs). On the numerical aspect, we combine the techniques of similarity reduction, non-standard interpolation, and careful grid construction to significantly improve the efficiency of our numerical methods for solving nonlinear HJB PDEs.

The primary value of the government lies in responding to different requests of people, and the government reform must be in increasing the execution.The execution of organization has been one of the focuses of government and public affair management. Therefore, the execution is bound to the result of effort and job performance the staff invest, and how to optimize the result is still need to rely on the work environment of justice, mutual confidence, and voluntary involvement that the organization created. This paper aimed to explore the relationship between the awareness of organizational justice and the execution by questionnaire survey., and the samples for the quantitative research were the staff of the land administration in Tainan City. The conclusions were that the execution was predicted by the awareness of organizational justice, the awareness of organizational justice had some significant difference in different individual variables of the staff, and the execution had some significant difference in different individual variables of the staff. Finally, based on the results from this paper, the suggestions are made for administration and the following related researches.

In most techniques to improve performance for loops or nested loops, they are often to do pipelined scheduling according to know loop execution times at compiler time. But it is hardly to pipeline the execution of loop whose execution length is unknown before loop execution at compiler time. In this thesis, a Common-Case based approach is proposed to improve the performance of loops with variable execution length. Besides, we use the modulo schedule to achieve the goal under resource constraint and without violating data dependence. The Common-Case approach not only reduces the execution time efficiently, but also reduces the power consumption largely due to the reduction of execution time. Through the searching for good Common-Case, we can surely supply a better solution to the loops whose execution length is unknown before loop execution at compiler time. Moreover, since the loop is divided into Common-Case and exception parts, we can further reduce power consumption by using Clock-Gating and Finite State Machine decomposition techniques. Finally, experiments on three real examples (Morris-Pratt Algorithm, Insertion Sort, Running Length Encoding) are used to demonstrate that our flow is correct and can achieve the goal of performance and energy optimizations.

Business transformation has become the sole means of survival for today¡¦s traditional businesses in Taiwan that were, once upon a time, star performers. So challenging has the business environment become that even today¡¦s star performers¡Xthe high tech and advanced industries¡Xwill someday become yesterday's news as well. The lessons of business transformation, therefore, are applicable to all businesses. Old school thinkers used to emphasize the impact company strategies had over the operation performance of a company, that the right strategies could alone produce effectively and efficiently performing companies. However, it has been proven over the years that the same strategies adopted by different companies could produce vastly different results. Execution has proven to be a bigger deciding factor in the success or failure of an enterprise. This study aims to demonstrate that proper and effective execution of the right strategies plays a critical role in deciding the future of a company, and success of its transformation. This study utilizes case studies, investigating how the petrochemical companies in study choose their transformation strategies, and how the choice of execution methods affects their operating performance after effecting transformation strategies. It also aims to understand the cause and effect relationship between the choice of transformation strategies, execution methods and the operating performance. Interviews and analysis have been carried out in the conduct of this study. The following is a list of major conclusions: 1.A continuously competitive company is able to deliver a high operation performance. 2.Strategies towards business transformation impact the company¡¦s operation performance. In the petrochemicals industry, the most effective strategies towards business transformation include, in order of impact, vertical integration, globalization, multi-faceted strategy. Horizontal integration is not a recommended strategy. 3.Execution affects the operation performance. Petrochemical companies with effective execution deliver high operation performance. 4.Manpower is any company¡¦s important resource. A high operation performance is also a result of effective manpower. 5.Companies that have been successful in its business transformation are equipped with leadership abilities, high quality of manpower and astute leadership by top management are critical factors to a successful company.

Software maintenance is a significant phase of a software life-cycle. Once a system is developed the main focus shifts to maintenance to keep the system up to date. A system may be changed for various reasons such as fulfilling customer requirements, fixing bugs or optimizing existing code. Code needs to be studied and understood before any modification is done to it. Understanding code is a time intensive and often complicated part of software maintenance that is supported by documentation and various tools such as profilers, debuggers and source code analysis techniques. However, most of the tools fail to assist in locating the portions of the code that implement the functionality the software developer is focusing. Mining execution traces can help developers identify parts of the source code specific to the functionality of interest and at the same time help them understand the behaviour of the code. We propose a use-driven hybrid framework of static and dynamic analyses to mine and manage execution traces to support software developers in understanding how the system's functionality is implemented through feature analysis. We express a system's use as a set of tests. In our approach, we develop a set of uses that represents how a system is used or how a user uses some specific functionality. Each use set describes a user's interaction with the system. To manage large and complex traces we organize them by system use and segment them by user interface events. The segmented traces are also clustered based on internal and external method types. The clusters are further categorized into groups based on application programming interfaces and active clones. To further support comprehension we propose a taxonomy of metrics which are used to quantify the trace. To validate the framework we built a tool called TrAM that implements trace mining and provides visualization features. It can quantify the trace method information, mine similar code fragments called active clones, cluster methods based on types, categorise them based on groups and quantify their behavioural aspects using a set of metrics. The tool also lets the users visualize the design and implementation of a system using images, filtering, grouping, event and system use, and present them with values calculated using trace, group, clone and method metrics. We also conducted a case study on five different subject systems using the tool to determine the dynamic properties of the source code clones at runtime and answer three research questions using our findings. We compared our tool with trace mining tools and profilers in terms of features, and scenarios. Finally, we evaluated TrAM by conducting a user study on its effectiveness, usability and information management.

A criminal is thrown from a high cliff into the sea. He has been covered in feathers, live birds attached to him to slow his fall. Fishermen wait below, hopeful of being able to carry him safely away. The people are punishing the criminal with death, yet simultaneously rooting for his survival. This startling image from Strabo, with its delicious ironic tension, is the center‐piece of “Civic Poetics.” The thesis consists of a cycle of poems imagining life in a city where this bizarre ritual is performed, coupled with a number of essays written for several Religious Studies courses on related themes. The interplay of poetry and essay aims to illuminate the experience of my own journey from criminal outsider to re‐integrated citizen. The lenses of (1) my own experiences in 21st century Atlanta and (2) poetic imaginative reconstruction of this ancient ritual reveal a startling picture: a criminal’s relations with the divine, as mediated by his state, and a state’s relations with the divine as mediated by its criminals.

In dynamic domains, the state of the world may change in unexpected ways during the generation or execution of plans. Regardless of the cause of such changes, they raise the question of whether they interfere with ongoing planning efforts. Unexpected changes during plan generation may invalidate the current planning effort, while discrepancies between expected and actual state of the world during execution may render the executing plan invalid or sub-optimal, with respect to previously identified planning objectives. In this thesis we develop a general monitoring technique that can be used during both plan generation and plan execution to determine the relevance of unexpected changes and which supports recovery. This way, time intensive replanning from scratch in the new and unexpected state can often be avoided. The technique can be applied to a variety of objectives, including monitoring the optimality of plans, rather then just their validity. Intuitively, the technique operates in two steps: during planning the plan is annotated with additional information that is relevant to the achievement of the objective; then, when an unexpected change occurs, this information is used to determine the relevance of the discrepancy with respect to the objective. We substantiate the claim of broad applicability of this relevance-based technique by developing four concrete applications: generating optimal plans despite frequent, unexpected changes to the initial state of the world, monitoring plan optimality during execution, monitoring the execution of near-optimal policies in stochastic domains, and monitoring the generation and execution of plans with procedural hard constraints. In all cases, we use the formal notion of regression to identify what is relevant for achieving the objective. We prove the soundness of these concrete approaches and present empirical results demonstrating that in some contexts orders of magnitude speed-ups can be gained by our technique compared to replanning from scratch.

Optimal trade execution aims at balancing price impact and timing risk. With respect to the mathematical formulation of the optimization problem, we primarily focus on Mean Variance (MV) optimization, in which the two conflicting objectives are maximizing expected revenue (the flip side of trading impact) and minimizing variance of revenue (a measure of timing risk). We also consider the use of expected quadratic variation of the portfolio value process as an alternative measure of timing risk, which leads to Mean Quadratic Variation (MQV) optimization. We demonstrate that MV-optimal strategies are quite different from MQV-optimal strategies in many aspects. These differences are in stark contrast to the common belief that MQV-optimal strategies are similar to, or even the same as, MV-optimal strategies. These differences should be of interest to practitioners since we prove that the classic Almgren-Chriss strategies (industry standard) are MQV-optimal, in contrary to the common belief that they are MV-optimal. From a computational point of view, we extend theoretical results in the literature to prove that the mean variance efficient frontier computed using our method is indeed the complete Pareto-efficient frontier. First, we generalize the result in Li (2000) on the embedding technique and develop a post-processing algorithm that guarantees Pareto-optimality of numerically computed efficient frontier. Second, we extend the convergence result in Barles (1990) to viscosity solution of a system of nonlinear Hamilton Jacobi Bellman partial differential equations (HJB PDEs). On the numerical aspect, we combine the techniques of similarity reduction, non-standard interpolation, and careful grid construction to significantly improve the efficiency of our numerical methods for solving nonlinear HJB PDEs.

Diss. Uppsala : Univ., 2002.