Photocopy of typescript. / Digitized by Kansas Correctional Industries

No description available.

This thesis considers the design and implementation of a Declarative Debugger for Haskell. At its core is a tree which captures the logical dependencies between function calls in a given execution of the program being debugged (the debuggee). The debuggee is transformed into a new Haskell program which produces the tree in addition to its normal value. A bug is identified in the tree when a call returns the wrong result but all the calls it depends upon are correct.

The purpose of this dissertation was to provide a review of the theory of Optimization, in particular quadratic programming, and the algorithms suitable for solving both convex and non-convex quadratic programming problems. Optimization problems arise in a wide variety of fields and many can be effectively modeled with linear equations. However, there are problems for which linear models are not sufficient thus creating a need for non-linear systems. This dissertation includes a literature study of the formal theory necessary for understanding optimization and an investigation of the algorithms available for solving a special class of the non-linear programming problem, namely the quadratic programming problem. It was not the intention of this dissertation to discuss all possible algorithms for solving the quadratic programming problem, therefore certain algorithms for convex and non-convex quadratic programming problems were selected for a detailed discussion in the dissertation. Some of the algorithms were selected arbitrarily, because limited information was available comparing the efficiency of the various algorithms. Algorithms available for solving general non-linear programming problems were also included and briefly discussed as they can be used to solve quadratic programming problems. A number of algorithms were then selected for evaluation, depending on the frequency of use in practice and the availability of software implementing these algorithms. The evaluation included a theoretical and quantitative comparison of the algorithms. The quantitative results were analyzed and discussed and it was shown that the results supported the theoretical comparison. It was also shown that it is difficult to conclude that one algorithm is better than another as the efficiency of an algorithm greatly depends on the size of the problem, the complexity of an algorithm and many other implementation issues. Optimization problems arise continuously in a wide range of fields and thus create the need for effective methods of solving them. This dissertation provides the fundamental theory necessary for the understanding of optimization problems, with particular reference to quadratic programming problems and the algorithms that solve such problems. Keywords: Quadratic Programming, Quadratic Programming Algorithms, Optimization, Non-linear Programming, Convex, Non-convex.

Optimization plays an important role in many methods routinely used in statistics, machine learning and data science. Often, implementations of these methods rely on highly specialized optimization algorithms, designed to be only applicable within a specific application. However, in many instances recent advances, in particular in the field of convex optimization, make it possible to conveniently and straightforwardly use modern solvers instead with the advantage of enabling broader usage scenarios and thus promoting reusability. This paper introduces the R Optimization Infrastructure which provides an extensible infrastructure to model linear, quadratic, conic and general nonlinear optimization problems in a consistent way. Furthermore, the infrastructure administers many different solvers, reformulations, problem collections and functions to read and write optimization problems in various formats. / Series: Research Report Series / Department of Statistics and Mathematics

Programming practices are used to improve various attributes of the coding process. Pair and Mob Programming are two practices that involve multiple developers collaboratively working on the same tasks and share multiple advantages and disadvantages. The aim of this project is to identify common advantages and disadvantages of the two practices as well as some attributes that differentiate the two and help in the process of deciding which programming practice should be used for a task. The first method used to answer the research questions was a literature review that should find and list the pros and cons of Mob and Pair Programming. A second method used were interviews with industry practitioners, whose perspectives and experiences will validate the previous results, add new attributes to the practices and identify differences and factors that encourage the use of one or the other practice. The findings of the project consist of positive and negative aspects of using any of the two programming practices and a set of attributes that should be considered when deciding whether to adopt Mob or Pair Programming for the task at hand.

This dissertation presents a study on the extent and limits of network transparency in distributed programming languages. This property states that the result of a distributed program is the same as if it were executed on a single computer, in the case when no failure occurs. The programming language may also be network aware if it allows the programmer to control how a program is distributed and how it behaves on the network. Both aim at simplifying distributed programming, by making non-functional aspects of a program more modular. We show that network transparency is not only possible, but also practical: it can be efficient, and smoothly extended in the case of partial failure. We give a proof of concept with the programming language Oz and the system Mozart, of which we have reimplemented the distribution support on top of the Distribution Subsystem (DSS). We have extended the language to control which distribution algorithms are used in a program, and reflect partial failures in the language. Both extensions allow to handle non-functional aspects of a program without breaking the property of network transparency.

In this thesis we study how to solve some nonconvex optimization problems by using methods that capitalize on the success of Linear Programming (LP) based solvers for Mixed Integer Linear Programming (MILP). A common aspect of our solution approaches is the use, development and analysis of small but strong extended LP/MILP formulations and approximations. In the first part of this work we develop an LP based branch-and-bound algorithm for mixed integer conic quadratic programs. The algorithm is based on a lifted polyhedral relaxation of conic quadratic constraints by Ben-Tal and Nemirovski. We test the algorithm on a series of portfolio optimization problems and show that it provides a significant computational advantage. In the second part we study the modeling of a class of disjunctive constraints with a logarithmic number of variables. For specially structured disjunctive constraints we give sufficient conditions for constructing MILP formulations with a number of binary variables and extra constraints that is logarithmic in the number of terms of the disjunction. Using these conditions we introduce formulations with these characteristics for SOS1, SOS2 constraints and piecewise linear functions. We present computational results showing that they can significantly outperform other MILP formulations. In the third part we study the modeling of non-convex piecewise linear functions as MILPs. We review several new and existing MILP formulations for continuous piecewise linear functions with special attention paid to multivariate non-separable functions. We compare these formulations with respect to their theoretical properties and their relative computational performance. In addition, we study the extension of these formulations to lower semicontinuous piecewise linear functions. Finally, in the fourth part we study the strength of MILP formulations for LPs with Probabilistic Constraints. We first study the strength of existing MILP formulations that only considers one row of the probabilistic constraint at a time. We then introduce an extended formulation that considers more than one row of the constraint at a time and use it to computationally compare the relative strength between formulations that consider one and two rows at a time.

Thesis (M.S.)--Kutztown University of Pennsylvania, 1993. / Source: Masters Abstracts International, Volume: 45-06, page: 3184. Typescript. Includes bibliographical references (leaves 55-56).

Thesis (Ph. D.)--OGI School of Science & Engineering at OHSU, 2004. / Includes bibliographical references (leaves 229-239).