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.
Results
Showing 1 to 20 of 48 for “"Scalable algorithms"”.
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Practical, scalable algorithms for Byzantine agreement
… there has been a push toward designing reliable algorithms that scale effectively in terms of latency, bandwidth and other computational resources. Scalability has been a serious problem especially with peer-to-peer (p2p) networks which may have sizes of more than a million nodes. An important …
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Scalable algorithms for Bayesian variable selection
This Dissertation was approved for publication on 2016-07-14 at 08:43.
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Scalable algorithms for correlation clustering on large graphs
… between clusters). Many of the state-of-the-art algorithms for solving correlation clustering rely on subroutines that cause significant memory and run time bottlenecks when applied to larger graphs. Several algorithms with the best theoretical guarantees for clustering quality need to first …
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Efficient and scalable algorithms for phylogenomics and overlapping communities
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms
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Classification with Large Sparse Datasets: Convergence Analysis and Scalable Algorithms
… sparse data. Towards this goal, we develop novel scalable feature mappings that can achieve better accuracy than linear classification. We demonstrate that the proposed methods not only outperform linear classification but is also scalable to large and sparse datasets with moderate memory and …
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Sparse Learning using Discrete Optimization: Scalable Algorithms and Statistical Insights
… statistical properties. This thesis develops scalable discrete optimization methods and presents new statistical insights for a fundamental class of sparse learning problems. In the first chapter, we consider the L0-regularized linear regression problem, which aims to select a subset of …
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On the analysis of complex networks : fundamental limits, scalable algorithms, and applications
… this combinatorial problem, we present a new scalable spectral algorithm which creates an eigenvector relaxation for the underlying optimization. We prove the optimality of the method under certain technical conditions, and show its effectiveness over various synthetic networks as well as in …
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Scalable algorithms for semi-automatic segmentation of electron microscopy images of the brain tissue
I present a set of fast and scalable algorithms for segmenting very large 3D images of brain tissue. Currently, light and electron microscopy can now produce terascale 3D images within hours. Extracting the information about the shapes and connectivity of the neurons require fast and accurate image …
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Algorithms for Large-scale Data Analytics and Applications to the COVID-19 Pandemic
… modern world, to generate impact, we need both scalable algorithms that allow us to extract insights from an ever-increasing amount of data, and also important applications to apply our insights to the world. In this thesis, we demonstrate both sides of the coin. In the first part of the thesis, …
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Efficient data collection strategies for rapid learning in physical environments
… sensors. We are interested in deriving scalable algorithms and system architectures that facilitate efficient data collection, maximizing inference quality under limited resource budget. In this work, we consider efficient data collection strategies in several applications involving …
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Analysis of relative navigation architectures for formation flying spacecrafts
… a challenge, especially for the navigation algorithms, which must provide an accurate estimate of the state of the fleet, with minimum requirements. Furthermore, as the number of satellites in the fleet increases, the computations to be performed increase dramatically, as well as the …
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Scalable Inference Algorithms for Determinantal Point Processes
… we need to design new primitives and inference algorithms that can be run efficiently in these settings. This thesis focuses on two inference tasks for DPPs: In the first part, we study sampling algorithms for DPPs and offer efficient MCMC based algorithms which can be applied in both discrete …
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Modeling, predicting, and guiding users' temporal behaviors
… statistical models, state-of-the-arts learning algorithms to tackle several canonical problems in theory and practice, including: (1) provable nonparametric learning of temporal point processes, (2) a generic embedding framework for continuous-time evolving graphs, (3) scalable algorithms for …
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Use of deterministic execution in testing and debugging of distributed software
… dynamic environments. We design efficient and scalable algorithms for both control and data traffics in modern wide-area networks. In addition, we further describe several interactive primitives to reduce the time and effort on testing and debugging of large-scale distributed software. We …
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Fast and stable smoothing spline analysis of variance models for large samples with applications to electroencephalography data analysis
… thesis, I propose various approximations and algorithms to stabilize and speed-up the fitting of two-way (or higher-way) SSANOVA models. In particular, I propose (a) an efficient reparameterization of the smoothing parameters in SSANOVA models, (b) using strategically-selected knot grids …
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Fast and scalable solvers for the Helmholtz equation
… this thesis we develop a new family of fast and scalable algorithms to solve the 2D high-frequency Helmholtz equation in heterogeneous medium. The algorithms rely on a layered domain decomposition and a coupling between subdomains using the Green's representation formula, which reduces the …
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Robust stability analysis for large-scale power systems
… In the second part of this thesis, we develop scalable algorithms to solve robust stability analysis problems on large-scale power systems. We use preconditioned iterative methods to solve the Newton direction computation in the interior-point method, in order to avoid the O(n6) time complexity …
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Efficient Algorithms for Vector Similarities
… local similarity computation: we give faster algorithms for individual similarity computations, such as calculating notions of similarity between collections of vectors, as well as dimensionality reduction techniques which preserve similarities. In addition to computational efficiency, other …
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Resource-Efficient and Robust Distributed Computing
… have become so large that they require highly scalable algorithms; algorithms that have asymptotically-small communication, computation, and latency costs with respect to the network size. Moreover, systems with thousands or even millions of parties distributed throughout the world is likely in …
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Scalable, Efficient, and Fair Algorithms for Structured Convex Optimization Problems
… the development of provably fast and scalable algorithms that incorporate ethical requirements. In this thesis, we present algorithms for fundamental optimization algorithms with theoretical guarantees on approximation quality and running time. We analyze the bit complexity and …
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