Browsing by Subject "Programming (Mathematics)"
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Item Approximation algorithms for NP-hard clustering problems(2002) Mettu, Ramgopal Reddy; Plaxton, C. GregItem Parallelization of scientific legacy code(Texas Tech University, 2003-12) Hernández, Karem BriceñoPresently, The National Aeronautics and Space Administration (NASA) conducts research through its Earth Observation System (EOS) to answer questions about global dynamics. The most comprehensive EOS instrument is MODIS (The Moderate-resolution Imaging Spectroradiometer) which has several data products. One of these products is MODIS LAI/FPAR (MOD 15). The algorithm for this product consists of two modules: M0DI5A1 which produces series of daily candidates LAI/FPAR data products and MOD15A2 which produces a 8-day composite to be archived. The purpose of the present work is to demonstrate that in spite of the elevated cost implied, parallelization for particular EOS applications might be worthwhile. For this, a previous analysis four years ago of tae serial M0D15A1 code was completed showing that the time may be reduced significantly. For that reason, some changes were made to the code with tae purpose of miming it on parallel. With these changes, an experimental run performed with threads demonstrated that m fact the time reduces to half of the time by using two processors m comparison with the serial version that used only one processor. Given that platforms with more than 2 processors were not readily available at that time, because of price, it is until now that we can easily perform those tests. Therefore, parallelization of this code was motivated to experiment with more processors m order to verify the level of performance that this code could attain, which is the main objective of the present work. With these experiments, it is confirmed that parallelization does improve the execution tune for this particular product but it does not scale up as it is estimated.Item Prioritization and optimization in stochastic network interdiction problems(2008-12) Michalopoulos, Dennis Paul, 1979-; Barnes, J. Wesley; Morton, David P.The goal of a network interdiction problem is to model competitive decision-making between two parties with opposing goals. The simplest interdiction problem is a bilevel model consisting of an 'adversary' and an interdictor. In this setting, the interdictor first expends resources to optimally disrupt the network operations of the adversary. The adversary subsequently optimizes in the residual interdicted network. In particular, this dissertation considers an interdiction problem in which the interdictor places radiation detectors on a transportation network in order to minimize the probability that a smuggler of nuclear material can avoid detection. A particular area of interest in stochastic network interdiction problems (SNIPs) is the application of so-called prioritized decision-making. The motivation for this framework is as follows: In many real-world settings, decisions must be made now under uncertain resource levels, e.g., interdiction budgets, available man-hours, or any other resource depending on the problem setting. Applying this idea to the stochastic network interdiction setting, the solution to the prioritized SNIP (PrSNIP) is a rank-ordered list of locations to interdict, ranked from highest to lowest importance. It is well known in the operations research literature that stochastic integer programs are among the most difficult optimization problems to solve. Even for modest levels of uncertainty, commercial integer programming solvers can have difficulty solving models such as PrSNIP. However, metaheuristic and large-scale mathematical programming algorithms are often effective in solving instances from this class of difficult optimization problems. The goal of this doctoral research is to investigate different methods for modeling and solving SNIPs (optimization) and PrSNIPs (prioritization via optimization). We develop a number of different prioritized and unprioritized models, as well as exact and heuristic algorithms for solving each problem type. The mathematical programming algorithms that we consider are based on row and column generation techniques, and our heuristic approach uses adaptive tabu search to quickly find near-optimal solutions. Finally, we develop a group of hybrid algorithms that combine various elements of both classes of algorithms.Item Quadratic programming techniques using matrix pseudoinverses(Texas Tech University, 1969-12) Nelson, David L.Not available