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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
Layered graphs: a class that admits polynomial time solut...
Bhadrachalam Chitturi · 2017-05-18 · via cs.DS updates on arXiv.org

The independent set on a graph $G=(V,E)$ is a subset of $V$ such that no two vertices in the subset have an edge between them. The MIS problem on $G$ seeks to identify an independent set with maximum cardinality, i.e. maximum independent set or MIS. $V* \subseteq V$ is a vertex cover $G=(V,E)$ if every edge in the graph is incident upon at least one vertex in $V*$. $V* \subseteq V$ is dominating set of $G=(V,E)$ if forall $v \in V$ either $v \in V*$ or $\exists u \in V*$ and $(u,v) \in E$. A connected dominating set, CDS, is a dominating set that forms a single component in $G$. The MVC problem on $G$ seeks to identify a vertex cover with minimum cardinality, i.e. minimum vertex cover or MVC. Likewise, CVC seeks a connected vertex cover (CVC) with minimum cardinality. The problems MDS and CDS seek to identify a dominating set and a connected dominating set respectively of minimum cardinalities. MVC, CVC, MDS, and CDS on a general graph are known to be NP-complete. On certain classes of graphs they can be computed in polynomial time. Such algorithms are known for bipartite graphs, chordal graphs, cycle graphs, comparability graphs, claw-free graphs, interval graphs and circular arc graphs for some of these problems. In this article we introduce a new class of graphs called a layered graph and show that if the number of vertices in a layer is $O(\log \mid V \mid)$ then MIS, MVC, CVC, MDS and CDC can be computed in polynomial time. The restrictions that are employed on graph classes that admit polynomial time solutions for hard problems, e.g. lack of cycles, bipartiteness, planarity etc. are not applicable for this class. \\ Key words: Independent set, vertex cover, dominating set, dynamic programming, complexity, polynomial time algorithms.