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cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Improved Algorithm for Min-Cuts in Distributed Networks
Mohit Daga · 2020-02-29 · via cs.DS updates on arXiv.org

In this thesis, we present fast deterministic algorithm to find small cuts in distributed networks. Finding small min-cuts for a network is essential for ensuring the quality of service and reliability. Throughout this thesis, we use the CONGEST model which is a typical message passing model used to design and analyze algorithms in distributed networks. We survey various algorithmic techniques in the CONGEST model and give an overview of the recent results to find cuts. We also describe elegant graph theoretic ideas like cut spaces and cycle spaces that provide useful intuition upon which our work is built. Our contribution is a novel fast algorith to find small cuts. Our algorithm relies on a new characterization of trees and cuts introduced in this thesis. Our algorithm is built upon several new algorithmic ideas that, when coupled with our characterization of trees and cuts, help us to find the required min-cuts. Our novel techniques include a tree restricted semigroup function (TRSF), a novel sketching technique, and a layered algorithm. TRSF is defined with respect to a spanning tree and is based on a commutative semigroup. This simple yet powerful technique helps us to deterministically find min-cuts of size one (bridges) and min-cuts of size two optimally. Our sketching technique samples a small but relevant vertex set which is enough to find small min-cuts in certain cases. Our layered algorithm finds min-cuts in smaller sub-graphs pivoted by nodes at different levels in a spanning tree and uses them to make the decision about the min-cuts in the complete graph. This is interesting because it enables us to show that even for a global property like finding min-cuts, local information can be exploited in a coordinated manner.