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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
Minimum+1 Steiner Cuts and Dual Edge Sensitivity Oracle: ...
Koustav Bhanja · 2024-06-21 · via cs.DS updates on arXiv.org

Let $G=(V,E)$ be an undirected multi-graph on $n=|V|$ vertices and $S\subseteq V$ be a Steiner set. Steiner cut is a fundamental concept; moreover, global cut $(|S|=n)$, as well as (s,t)-cut $(|S|=2)$, is just a special case of Steiner cut. We study Steiner cuts of capacity minimum+1, and as an important application, we provide a dual edge Sensitivity Oracle for Steiner mincut. A compact data structure for cuts of capacity minimum+1 has been designed for both global cuts [STOC 1995] and (s,t)-cuts [TALG 2023]. Moreover, both data structures are also used crucially to design a dual edge Sensitivity Oracle for their respective mincuts. Unfortunately, except for these two extreme scenarios of Steiner cuts, no generalization of these results is known. Therefore, to address this gap, we present the following first results on Steiner cuts. 1. Data Structure: There is an $O(n(n-|S|+1))$ space data structure that can determine in $O(1)$ time whether a given pair of vertices is separated by a Steiner cut of capacity at least minimum+1. It can report such a cut, if it exists, in $O(n)$ time. 2. Sensitivity Oracle: (a) There is an $O(n(n-|S|+1))$ space data structure that, after the failure/insertion of any pair of edges, can report the capacity of Steiner mincut in $O(1)$ time and a Steiner mincut in $O(n)$ time. (b) If we are interested in reporting only the capacity, there is a more compact data structure that occupies $O((n-|S|)^2+n)$ space and reports the capacity in $O(1)$ time after the failure/insertion of any pair of edges. 3. Lower Bound: For undirected multi-graphs, for every Steiner set $S$, any data structure that, after the failure or insertion of any pair of edges, can report the capacity of Steiner mincut must occupy $Ω((n-|S|)^2)$ bits of space, irrespective of the query time.