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 10 of 10 for “"Graph Sparsification"”.
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SUBLINEAR GRAPH SPARSIFICATION WITH APPLICATIONS TO CUTS, MATCHINGS, AND FLOWS
… sublinear algorithms for datasets with graph structures, such as social networks, biological networks and Web networks, where pairwise relationship between data points is captured. We obtain close-to-optimal results for the sublinear computation of several fundamental graph problems - …
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Dimensionality reduction for sparse and structured matrices
… directly to accelerating linear regression and graph sparsification and we discuss connections and possible extensions to low-rank approximation, k-means clustering, and several other ubiquitous matrix problems.
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Tackling Algorithmic Problems on Massive Graphs
… algorithmic approaches for processing massive graphs under these constraints. Specifically, we focus on algorithms for the following graph problems. Motif Counting and Sampling: This involves developing efficient algorithms for counting and sampling small motifs (constant sized subgraphs) like …
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Development and evaluation of machine learning algorithms for biomedical applications
… suitable for link prediction in gene networks; a graph sparsification method for network sampling; (iii) combined supervised and unsupervised methods to infer gene networks; and (iv) sampling and boosting techniques for reverse engineering gene networks. For drug sensitivity prediction problem, …
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Faster algorithms for convex and combinatorial optimization
… thesis, we revisit three algorithmic techniques: sparsification, cutting and collapsing. We use them to obtain the following results on convex and combinatorial optimization: --Linear Programming: We obtain the first improvement to the running time for linear programming in 25 years. The …
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Topics in quantum information theory and quantum many-body physics
… use of quantum walks to traverse the welded tree graph, due to Childs, Cleve, Deotto, Farhi, Gutmann, and Spielman. We show how to generalize this to a large class of hierarchical graphs in which the vertices are grouped into “supervertices” that are arranged according to a d-dimensional lattice. …
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Lifelong, learning-augmented robot navigation
… we propose an efficient algorithm for graph sparsification capable of reducing the computational burden of SLAM methods without significantly degrading SLAM solution quality. Taken together, these contributions improve the robustness and efficiency of robot perception approaches in the …
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Lifelong, Learning-Augmented Robot Navigation
… we propose an efficient algorithm for graph sparsification capable of reducing the computational burden of SLAM methods without significantly degrading SLAM solution quality. Taken together, these contributions improve the robustness and efficiency of robot perception approaches in the …
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Algorithmic advances in learning from large dimensional matrices and scientific data
… rank approximation, column subset selection, and graph sparsification. We present a new approach based on multilevel coarsening to compute these approximations for large sparse matrices and graphs. Lastly, on the linear algebra front, we devise a novel algorithm based on rank shrinkage for the …
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Application of nearly linear solvers to electric power system computation
… new theoretical method that is based on ideas in graph theory and combinatorics. The technique builds a chain of progressively smaller approximate systems with preconditioners based on the system's low stretch spanning tree. The method is compared to traditional linear solvers and shown to reduce …