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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
On Packing Low-Diameter Spanning Trees
Julia Chuzhoy, Merav Parter, Zihan Tan · 2020-06-13 · via cs.DS updates on arXiv.org

Edge connectivity of a graph is one of the most fundamental graph-theoretic concepts. The celebrated tree packing theorem of Tutte and Nash-Williams from 1961 states that every $k$-edge connected graph $G$ contains a collection $\cal{T}$ of $\lfloor k/2 \rfloor$ edge-disjoint spanning trees, that we refer to as a tree packing; the diameter of the tree packing $\cal{T}$ is the largest diameter of any tree in $\cal{T}$. A desirable property of a tree packing, that is both sufficient and necessary for leveraging the high connectivity of a graph in distributed communication, is that its diameter is low. Yet, despite extensive research in this area, it is still unclear how to compute a tree packing, whose diameter is sublinear in $|V(G)|$, in a low-diameter graph $G$, or alternatively how to show that such a packing does not exist. In this paper we provide first non-trivial upper and lower bounds on the diameter of tree packing. First, we show that, for every $k$-edge connected $n$-vertex graph $G$ of diameter $D$, there is a tree packing $\cal{T}$ of size $Ω(k)$, diameter $O((101k\log n)^D)$, that causes edge-congestion at most $2$. Second, we show that for every $k$-edge connected $n$-vertex graph $G$ of diameter $D$, the diameter of $G[p]$ is $O(k^{D(D+1)/2})$ with high probability, where $G[p]$ is obtained by sampling each edge of $G$ independently with probability $p=Θ(\log n/k)$. This provides a packing of $Ω(k/\log n)$ edge-disjoint trees of diameter at most $O(k^{(D(D+1)/2)})$ each. We then prove that these two results are nearly tight. Lastly, we show that if every pair of vertices in a graph has $k$ edge-disjoint paths of length at most $D$ connecting them, then there is a tree packing of size $k$, diameter $O(D\log n)$, causing edge-congestion $O(\log n)$. We also provide several applications of low-diameter tree packing in distributed computation.