惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

MongoDB | Blog
MongoDB | Blog
J
Java Code Geeks
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
D
DataBreaches.Net
腾讯CDC
GbyAI
GbyAI
I
InfoQ
博客园 - Franky
G
Google Developers Blog
Last Week in AI
Last Week in AI
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
V
Visual Studio Blog
Vercel News
Vercel News
博客园_首页
MyScale Blog
MyScale Blog
Martin Fowler
Martin Fowler
N
Netflix TechBlog - Medium
V
V2EX
T
The Blog of Author Tim Ferriss
M
MIT News - Artificial intelligence
雷峰网
雷峰网
H
Hackread – Cybersecurity News, Data Breaches, AI and More
大猫的无限游戏
大猫的无限游戏
The GitHub Blog
The GitHub Blog

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
Supermodular Maximization with Cardinality Constraints
Xujin Chen, Xiaodong Hu, Changjun Wang, Qingjie Ye · 2025-10-22 · via cs.DS updates on arXiv.org

Let $V$ be a finite set of $n$ elements, $f: 2^V \rightarrow \mathbb{R}_+$ be a nonnegative monotone supermodular function, and $k$ be a positive integer no greater than $n$. This paper addresses the problem of maximizing $f(S)$ over all subsets $S \subseteq V$ subject to the cardinality constraint $|S| = k$ or $|S|\le k$. Let $r$ be a constant integer. The function $f$ is assumed to be {\em $r$-decomposable}, meaning there exist $m\,(\ge1)$ subsets $V_1, \dots, V_m$ of $V$, each with a cardinality at most $r$, and a corresponding set of nonnegative supermodular functions $f_i : 2^{V_i} \rightarrow \mathbb{R}_+$, $i=1,\ldots,m$ such that $f(S) =\sum_{i=1}^m f_i(S \cap V_i)$ holds for each $S \subseteq V$. Given $r$ as an input, we present a polynomial-time $O(n^{(r-1)/2})$-approximation algorithm for this maximization problem, which does not require prior knowledge of the specific decomposition. When the decomposition $(V_i,f_i)_{i=1}^m$ is known, an additional connectivity requirement is introduced to the problem. Let $G$ be the graph with vertex set $V$ and edge set $\cup_{i=1}^m \{uv:u,v\in V_i,u\neq v\}$. The cardinality constrained solution set $S$ is required to induce a connected subgraph in $G$. This model generalizes the well-known problem of finding the densest connected $k$-subgraph. We propose a polynomial time $O(n^{(r-1)/2})$-approximation algorithm for this generalization. Notably, this algorithm gives an $O(n^{1/2})$-approximation for the densest connected $k$-subgraph problem, improving upon the previous best-known approximation ratio of $O(n^{2/3})$.