Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 83 for “"discrete optimization"”.
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Problems in discrete optimization
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1993.
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Discrete optimization of upgrade scheduling
A primary objective of the mission to meet climate change goals of reducing greenhouse gas (GHG) emissions is to transition from fossil fuels to zero-emission energy. Fossil fuel production and transportation account for approximately half of the GHG emissions in Canada, making transitioning to …
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Towards GPU-accelerated discrete optimization
Discrete optimization plays a crucial role in solving complex decision-making problems across a wide range of applications in operations research. These problems often involve selecting an optimal subset or sequence of decisions from a finite set, a task that becomes exponentially harder as the …
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School choice : a discrete optimization approach
… and transportation costs. Facing this intricate optimization problem, school districts often utilize to stable-matching techniques which only produce stable matchings that do not incorporate these different objectives; this can be expensive and inequitable. We present a new optimization model for …
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An approximate dynamic programming approach to discrete optimization
… We explore an integrated approach to solve discrete optimization problems by unifying optimization techniques with statistical learning. Overall, this research illustrates that the ADP is a promising technique by providing near-optimal solutions within reasonable amount of computation time …
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Generalized hill climbing algorithms for discrete optimization problems
… are introduced, as a tool to address difficult discrete optimization problems. Particular formulations of GHC algorithms include simulated annealing (SA), local search, and threshold accepting (T A), among. others. A proof of convergence of GHC algorithms is presented, that relaxes the …
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Machine learning and combinatorial methods for discrete optimization problems
Combinatorial optimization is a central field of discrete mathematics, concerned with finding optimal solutions to problems over combinatorial structures such as graphs or set systems. However, while classical combinatorial optimization assumes complete knowledge of all problem parameters, …
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Discrete Optimization in Early Vision - Model Tractability Versus Fidelity
… the current state of the art by introducing new discrete methods for image segmentation and other problems of early vision. The first part studies pseudo-boolean optimization, both from a theoretical perspective as well as a practical one by introducing new algorithms. The main result is the …
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Implicit enumeration and constraint ordering for discrete optimization problems
Includes bibliographical references.
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Mother Tree Optimization for Solving Continuous and Discrete Optimization Problems
Continuous and discrete optimization problems play a signi cant role in di erent academic and industrial disciplines. The main objective of a constraint optimization process is to nd a solution for a problem, that satis es a set of constraints while optimizing a given objective function. The exact …
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Sparse Learning using Discrete Optimization: Scalable Algorithms and Statistical Insights
… learning problems can be naturally modeled using discrete optimization, computational challenges have historically shifted the focus towards alternatives based on continuous optimization and heuristics. Recently, growing evidence suggests that discrete optimization methods can obtain more …
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Tractability through approximation : a study of two discrete optimization problems
(cont.) algorithm, at one extreme, and complete enumeration, at the other extreme. We derive worst-case approximation guarantees on the solution produced by such an algorithm for matroids. We then define a continuous relaxation of the original problem and show that some of the derived bounds apply …
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Improving Efficiency and Fairness in Machine Learning: a Discrete Optimization Approach
… machine learning systems. We borrow tools from discrete and robust optimization to develop models and algorithms for such systems. The first part of this thesis focuses on developing novel methodologies to enhance performance of specific predictive models. In particular, in the first chapter we …
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Integer Programming for Discrete Optimization of the Agile Supply Chain Configuration Problem
… search engine by incorporating combinatorial optimization techniques. In particular, this research is aimed at creating an integer programming formulation to efficiently and effectively solve the supply chain configuration problem by maximizing the technological competencies of the assigned …
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Algorithmic advancements in discrete optimization : applications to machine learning and healthcare operations
… since orchestrating care requires the concurrent optimization of multiple resources, services, and time scales. Third, real-time personalized decisions, to respond to the increasingly closer monitoring of patients. To support this transition and transform our healthcare system towards better …
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Simultaneous Generalized Hill Climbing Algorithms for Addressing Sets of Discrete Optimization Problems
… local search algorithms to address intractable discrete optimization problems. Many well-known local search algorithms can be formulated as GHC algorithms, including simulated annealing, threshold accepting, Monte Carlo search, and pure local search (among others). This dissertation develops a …
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A Local Search Algorithm Approach to Analyzing the Complexity of Discrete Optimization Problems
The properties of polynomially computable neighborhood functions are also examined. The global verification of several problems is proven to be NP-complete. These results are extended to show that polynomially computable neighborhood functions have arbitrarily poor local optima.
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A Discrete Optimization Approach to Solve a Reader Location Problem for Estimating Travel Times
… This problem is formulated as a quadratic 0-1 optimization problem. The objective function parameters in the optimization problem represent certain benefit factors resulting from the ability to measure travel time variability along various origin-destination paths. A simulation study using the …
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Enhanced Formulations for Minimax and Discrete Optimization Problems with Applications to Scheduling and Routing
… formulations associated with such minimax optimization problems. Next, we explore novel continuous nonconvex as well as lifted discrete formulations for the notoriously challenging class of job-shop scheduling problems with the objective of minimizing the maximum completion time (i.e., …
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Tactical Network Flow and Discrete Optimization Models and Algorithms for the Empty Railcar Transportation Problem
… a network flow problem with side-constraints and discrete side-variables. We show how the resulting mixed-integer-programming formulation can be enhanced via some partial convex hull constructions using the Reformulation-Linearization Technique (RLT). This tightening of the underlying linear …
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