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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
Reconfiguration of Connected Graph Partitions via Recombi...
Hugo A. Akitaya, Matias Korman, Oliver Korten, Diane L. Souvaine · 2020-11-15 · via cs.DS updates on arXiv.org

Motivated by applications in gerrymandering detection, we study a reconfiguration problem on connected partitions of a connected graph $G$. A partition of $V(G)$ is \emph{connected} if every part induces a connected subgraph. In many applications, it is desirable to obtain parts of roughly the same size, possibly with some slack $s$. A \emph{Balanced Connected $k$-Partition with slack $s$}, denoted \emph{$(k,s)$-BCP}, is a partition of $V(G)$ into $k$ nonempty subsets, of sizes $n_1,\ldots , n_k$ with $|n_i-n/k|\leq s$, each of which induces a connected subgraph (when $s=0$, the $k$ parts are perfectly balanced, and we call it \emph{$k$-BCP} for short). A \emph{recombination} is an operation that takes a $(k,s)$-BCP of a graph $G$ and produces another by merging two adjacent subgraphs and repartitioning them. Given two $k$-BCPs, $A$ and $B$, of $G$ and a slack $s\geq 0$, we wish to determine whether there exists a sequence of recombinations that transform $A$ into $B$ via $(k,s)$-BCPs. We obtain four results related to this problem: (1) When $s$ is unbounded, the transformation is always possible using at most $6(k-1)$ recombinations. (2) If $G$ is Hamiltonian, the transformation is possible using $O(kn)$ recombinations for any $s \ge n/k$, and (3) we provide negative instances for $s \leq n/(3k)$. (4) We show that the problem is PSPACE-complete when $k \in O(n^{\varepsilon})$ and $s \in O(n^{1-\varepsilon})$, for any constant $0 < \varepsilon \le 1$, even for restricted settings such as when $G$ is an edge-maximal planar graph or when $k=3$ and $G$ is planar.