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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
Faster Algorithms for Graph Monopolarity
Geevarghese Philip, Shrinidhi Teganahally Sridhara · 2024-10-09 · via cs.DS updates on arXiv.org

A graph $G = (V,E)$ is $\textit{monopolar}$ if its vertex set admits a partition $V = (C \uplus{} I)$ where $G[C]$ is a $\textit{cluster graph}$ and $I$ is an $\textit{independent set}$ in $G$; this is a \textit{monopolar partition} of $G$. The MONOPOLAR RECOGNITION problem -- deciding whether an input graph is monopolar -- is known to be NP-Hard in very restricted graph classes such as sub-cubic planar graphs. We derive a polynomial-time algorithm that takes (i) a graph $G=(V,E)$ and (ii) a vertex modulator $S$ of $G$ to chair-free graphs as inputs, and checks whether $G$ has a monopolar partition $V=(C\uplus{}I)$ where set $S$ is contained in the cluster part. We build on this algorithm to develop fast exact exponential-time and parameterized algorithms for MONOPOLAR RECOGNITION. Our exact algorithm solves MONOPOLAR RECOGNITION in $\mathcal{O}^{\star}(1.3734^{n})$ time on input graphs with $n$ vertices, where the $\mathcal{O}^{\star}()$ notation hides polynomial factors. In fact, we solve the more general problems MONOPOLAR EXTENSTION and LIST-MONOPOLAR PARTITION in $\mathcal{O}^{\star}(1.3734^{n})$ time. These are the first improvements over the trivial $\mathcal{O}^{\star}(2^{n})$-time algorithms for all these problems. It is known that -- assuming ETH -- these problems cannot be solved in $\mathcal{O}^{\star}(2^{o(n)})$ time. Our FPT algorithms solve MONOPOLAR RECOGNITION in $\mathcal{O}^{\star}(3.076^{k_{v}})$ and $\mathcal{O}^{\star}(2.253^{k_{e}})$ time where $k_{v}$ and $k_{e}$ are, respectively, the sizes of the smallest vertex and edge modulators of the input graph to claw-free graphs. These results are a significant addition to the small number of FPT algorithms currently known for MONOPOLAR RECOGNITION.