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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
The Limits of Local Search for the Maximum Weight Indepen...
Meike Neuwohner · 2021-06-07 · via cs.DS updates on arXiv.org

We consider the Maximum Weight Independent Set Problem (MWIS) in $d$-claw free graphs, i.e. the task of computing an independent set of maximum weight in a given $d$-claw free graph $G=(V,E)$ equipped with a positive weight function $w:V\rightarrow\mathbb{R}_{>0}$. For $k\geq 1$, the MWIS in $k+1$-claw free graphs generalizes the weighted $k$-Set Packing Problem. Given that for $k\geq 3$, this problem does not permit a polynomial time $o(\frac{k}{\log k})$-approximation unless $P=NP$, most previous algorithms for both weighted $k$-Set Packing and the MWIS in $d$-claw free graphs rely on local search. For the last twenty years, Berman's algorithm SquareImp, which yields a $\frac{d}{2}+ε$-approximation for the MWIS in $d$-claw free graphs, has remained unchallenged for both problems. Recently, it was improved by Neuwohner, obtaining an approximation guarantee slightly below $\frac{d}{2}$, and inevitably raising the question of how far one can get by using local search. In this paper, we finally answer this question asymptotically in the following sense: By considering local improvements of logarithmic size, we obtain approximation ratios of $\frac{d-1+ε_d}{2}$ for the MWIS in $d$-claw free graphs for $d\geq 3$ in quasi-polynomial time, where $0\leq ε_d\leq 1$ and $\lim_{d\rightarrow\infty}ε_d = 0$. By employing the color coding technique, we can use the previous result to obtain a polynomial time $\frac{k+ε_{k+1}}{2}$-approximation for weighted $k$-Set Packing. On the other hand, we provide examples showing that no local improvement algorithm considering local improvements of size $\mathcal{O}(\log(|\mathcal{S}|))$ with respect to some power $w^α$ of the weight function, where $α\in\mathbb{R}$ is chosen arbitrarily, but fixed, can yield an approximation guarantee better than $\frac{k}{2}$ for the weighted $k$-Set Packing Problem with $k\geq 3$.