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 27 for “"Greedy Algorithms"”.
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Greedy Algorithms and Incoherent Systems
… best upper bound on the rate of convergence of greedy expansions, and explicit formulas for approximants from the Pure Greedy Algorithm.</p>
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Greedy Algorithms In Approximation Theory and Compressed Sensing
… consideration is how to construct good methods (algorithms) of approximation, and how to measure the performance of these methods. One of the most successful approaches in this area is the greedy method, which belongs to the theory of nonlinear approximation. This dissertation answers the …
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Parallelisation of greedy algorithms for compressive sensing reconstruction
… the fastest reconstruction techniques, known as greedy pursuits, reconstruction of large problems can pose a significant burden, consuming a great deal of memory as well as compute time. Parallel computing is the foundation of the field of High Performance Computing (HPC). Modern supercomputers …
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Load Balancing in NetApp’s Clustered Storage Systems
… clients’ workloads. I implement three different greedy algorithms to find a more balanced workload-node assignment that lowers the maximum number of operations across the cluster. To analyze the performance of the greedy algorithms, I compare their results with those of the evolutionary and brute …
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In pursuit of high resolution radar using pursuit algorithms
… solution. In this thesis, we focus on the greedy algorithm approach to solve the problem and show that it naturally yields a quantitative measure for radar resolution. In addition, we show that the limitations of the greedy algorithms can be attributed to the close relation between greedy …
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Additive Lebesgue-Type Inequalities for Greedy Approximation
… and focus on a class of such techniques called "greedy algorithms". A problem that we will be mostly concerned with is of measuring performance of these algorithms (specifically, Pure Greedy Algorithm and Orthogonal Greedy Algorithm). We will compare several ways to describe the quality of the …
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Greedy Inference Algorithms for Structured and Neural Models
… optimal solution extremely expensive. Thus, greedy algorithms, making trade-offs between precision and efficiency, are widely used. Unfortunately, they in general lack theoretical guarantees. In this thesis, we prove that greedy algorithms are effective and efficient to search for multiple …
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Super Greedy Type Algorithms and Applications In Compressed Sensing
<p>In this manuscript we study greedy-type algorithms such that at a greedy step we pick several dictionary elements contrary to a single dictionary element in standard greedy-type algorithms. We call such greedy algorithms super greedy type algorithms. In the general setting, we propose several …
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Analysis of approximation and uncertainty in optimization
… study a series of topics involving approximation algorithms and the presence of uncertain data in optimization. On the first theme of approximation, we derive performance bounds for rollout algorithms. Interpreted as an approximate dynamic programming algorithm, a rollout algorithm estimates the …
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Approximate Algorithms for the Combined arrival-Departure Aircraft Sequencing and Reactive Scheduling Problems on Multiple Runways
… in a reasonable amount of time. Therefore, three greedy algorithms, namely the Adapted Apparent Tardiness Cost with Separation and Ready Times (AATCSR), the Earliest Ready Time (ERT) and the Fast Priority Index (FPI) are proposed. Moreover, metaheuristics including Simulated Annealing (SA) and the …
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Dynamic online resource allocation problems
… machines, two classes of approximation algorithms, Cooperative Greedy algorithms and Prioritized Greedy algorithms, are compared using competitive ratios with respect to varying machine weight ratios. We also provide lower bounds for competitive ratios of deterministic online scheduling …
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Sparse Approximation In Banach Spaces
… physicists fascinated by the potential of new algorithms in solving the problem of sparse approximation in diverse settings, which is the primary objective of this field.</p> <p>One of the most successful approaches in this area, the greedy method, belongs to the theory of nonlinear …
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Advances in machine learning for sustainable manufacturing
… relevant for its development.<br/><br/>Several greedy algorithms for unsupervised variable selection have been proposed in the past without a systematic benchmarking. Therefore, in the second paper we review and provide a comparative study of these methods. Their application is of interest in …
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Low Complexity Scheduling in Wireless Networks
… schedulers, low-complexity schedulers such as Greedy Maximal Scheduling(GMS)that often yield good throughput performance have received significantattention in the recent past, with the performance of GMS having been characterizedusing the Local Pooling Factor (LPF) of a network graph.Unlike …
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Energy-aware Sparse Sensing of Spatial-temporally Correlated Random Fields
… as a combinatorial problem. Two low complexity greedy algorithms are developed by using analytical upper bounds of the expected estimation error probability. </p> <p>Lastly we study the distributed estimations of a spatially correlated random field with decentralized wireless sensor networks …
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Optimization problems in networks and queues
… in this domain and present polynomial-time algorithms with provable guarantees on reaching their respective global optima. This utilizes techniques from optimization theory, queueing theory, and stochastic processes to overcome challenges from non-convexity and temporal dynamics. The final …
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Learning in Human and Robot Search: Subgoal, Submodularity, and Sparsity
… (PD) with motion cost. The proof shows that greedy algorithms give near-optimal subgoals with high probability. In the last part, the approach is to learn to search through sequential perception and actions. Since the PD function and cost-to-go (CTG) function depend on the environment, the …
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Three fundamental pillars of decision-centered teamwork
… I analyze a linear combination of two greedy algorithms, outperforming both of them. This domain has a great potential for health, as I run experiments in four real-life social networks from the homeless population of Los Angeles, aiming at spreading HIV prevention information. Finally, …
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Theoretical guarantees and complexity reduction in information planning
… interestingly, it has been shown that simple greedy algorithms that choose the best measurement at each time step given past selections, provide nearly optimal solutions for submodular monotone rewards. In this thesis, we examine several challenges that arise when performing real-world …
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Meta-raps: Parameter Setting And New Applications
… is a generic, high level strategy used to modify greedy algorithms based on the insertion of a random element (Moraga, 2002). To date, Meta-RaPS had been applied to different types of combinatorial optimization problems and achieved comparable solution performance to other meta-heuristic …
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