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Showing 1 to 17 of 17 for “"Sparse Graph"”.
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Constant time algorithms in sparse graph model
We focus on constant-time algorithms for graph problems in bounded degree model. We introduce several techniques to design constant-time approximation algorithms for problems such as Vertex Cover, Maximum Matching, Maximum Weighted Matching, Maximum Independent Set and Set Cover. Some of our …
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Sparse graph codes for compression, sensing, and secrecy
Sparse graph codes were first introduced by Gallager over 40 years ago. Over the last two decades, such codes have been the subject of intense research, and capacity approaching sparse graph codes with low complexity encoding and decoding algorithms have been designed for many channels. Motivated …
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Synthesis of Clock Trees with Useful Skew based on Sparse-Graph Algorithms
Computer-aided design (CAD) for very large scale integration (VLSI) involves
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Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection
Learning a suitable graph is an important precursor to many graph signal processing (GSP) tasks, such as graph signal compression and denoising. Previous graph learning algorithms either make assumptions on graph connectivity (e.g., graph sparsity), or make individual edge weight assumptions such …
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A decomposition procedure for finding the minimal Hamiltonian chain of a sparse graph
… of finding the minimal Hamiltonian chain of a graph. A single chain must traverse all 𝑛 vertices of a graph with the minimal distance. The proposed procedure reduces a large problem into several smaller problems and uses a branch and bound algorithm to find the minimal Hamiltonian chain of each …
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Similarity modeling for machine learning
… learning method, then introduce two novel sparse similarity modeling methods for high dimensional data from the perspective of manifold learning and subspace learning. Our sparse similarity modeling methods learn sparse similarity and consequently generate a sparse graph over the data. The …
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Faster generation of random spanning trees
… uniformly random spanning trees in undirected graphs. We show how to sample from a distribution that is within a multiplicative (1+6) of uniform in expected time ... . This improves the sparse graph case of the best previously known worst-case bound of O(min{mn, n2. 376}), which has stood for …
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Graph-based and algebraic codes for error-correction and erasure recovery
Expander codes are sparse graph-based codes with good decoding algorithms. We present a linear-time decoding algorithm for (C,D, alpha, gamma) expander codes based on graphs with any expansion factor given that the minimum distances of the inner codes are bounded below. We also design graph-based …
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Visual Inertial Odometry with Sparse Deep Learning
… an automated data collection system and a more sparse graph neural network to train with. We also show how the integration of this deep learning model impacts the performance of a real-time VIO system. On the inertial side, a commonly used sensor like an Inertial Measurement Unit (IMU) has noise …
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Design and analysis of a nondeterministic parallel breadth-first search algorithm
… of breadth-first search (BFS) of a sparse graph using the Cilk++ extensions to C++. My PBFS program on a single processor runs as quickly as a standard C++ breadth-first search implementation. PBFS achieves high workefficiency by using a novel implementation of a multiset data …
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Modeling and estimation in Gaussian graphical models : maximum-entropy methods and walk-sum analysis
Graphical models provide a powerful formalism for statistical signal processing. Due to their sophisticated modeling capabilities, they have found applications in a variety of fields such as computer vision, image processing, and distributed sensor networks. In this thesis we study two central …
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Graceful codes : fundamental limits and constructions
… is the introduction of a new class of nonlinear sparse-graph codes that we call Low-Density Majority Codes (LDMCs) They admit efficient decoding via belief propagation and have provably superior performance compared to the best-possible linear systematic codes, in particular LDGMs Hence, we hope …
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Geometric Decompositions and Networks - Approximation Bounds and Algorithms
… of finding a t-spanner of a complete geometric graph. The aim is to produce a sparse graph with a small number of edges and with low total weight, that is almost as "good" as a complete graph. With good we mean that for every pair of points in the graph there exists a path in the spanner graph …
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Optimization problems in network connectivity
… of all the minimum cuts in an undirected graph. -- Cut Sparsification. A cut sparsifier of an undirected graph is a sparse graph on the same set of vertices that preserves its cut values up to small errors. We give new combinatorial and algorithmic results for constructing cut sparsifiers. …
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Faster algorithms for convex and combinatorial optimization
… for solving the maximum flow problem on directed graphs with m edges and n vertices. This improves upon the previous fastest running time of achieved over 15 years ago by Goldberg and Rao. --Maximum Flow Problem: We obtain one of the first almost-linear time randomized algorithms for approximating …
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Three essays on econometrics: Network estimators with applications and assessment of the effects of Covid-19 pandemic
… network. The goal is to reconstruct a (weighted) graph when we are not able to directly observe connections among variables. The first paper focuses on random vectors with multivariate Gaussian distribution. In this specific case, a graph embedding the conditional dependencies can be obtained from …
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Cavity-Based Approaches to Stochastic Dynamics on Sparse Graphs: From Ecological Systems to Epidemiological Inference
L'abstract è presente nell'allegato / the abstract is in the attachment