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

IT之家
IT之家
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
大猫的无限游戏
大猫的无限游戏
美团技术团队
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园_首页
MyScale Blog
MyScale Blog
N
Netflix TechBlog - Medium
I
InfoQ
Jina AI
Jina AI
Martin Fowler
Martin Fowler
Recent Announcements
Recent Announcements
量子位
月光博客
月光博客
罗磊的独立博客
雷峰网
雷峰网
The Cloudflare Blog
V
V2EX
小众软件
小众软件
人人都是产品经理
人人都是产品经理
博客园 - Franky
T
Tailwind CSS Blog
有赞技术团队
有赞技术团队
S
SegmentFault 最新的问题

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
Simultaneously Satisfying Linear Equations Over $\mathbb{...
R. Crowston, M. Fellows, G. Gutin, M. Jones, F. Rosamond, S. Tho · 2011-04-07 · via cs.DS updates on arXiv.org

In the parameterized problem \textsc{MaxLin2-AA}[$k$], we are given a system with variables $x_1,...,x_n$ consisting of equations of the form $\prod_{i \in I}x_i = b$, where $x_i,b \in \{-1, 1\}$ and $I\subseteq [n],$ each equation has a positive integral weight, and we are to decide whether it is possible to simultaneously satisfy equations of total weight at least $W/2+k$, where $W$ is the total weight of all equations and $k$ is the parameter (if $k=0$, the possibility is assured). We show that \textsc{MaxLin2-AA}[$k$] has a kernel with at most $O(k^2\log k)$ variables and can be solved in time $2^{O(k\log k)}(nm)^{O(1)}$. This solves an open problem of Mahajan et al. (2006). The problem \textsc{Max-$r$-Lin2-AA}[$k,r$] is the same as \textsc{MaxLin2-AA}[$k$] with two differences: each equation has at most $r$ variables and $r$ is the second parameter. We prove a theorem on \textsc{Max-$r$-Lin2-AA}[$k,r$] which implies that \textsc{Max-$r$-Lin2-AA}[$k,r$] has a kernel with at most $(2k-1)r$ variables improving a number of results including one by Kim and Williams (2010). The theorem also implies a lower bound on the maximum of a function $f:\ \{-1,1\}^n \rightarrow \mathbb{R}$ of degree $r$. We show applicability of the lower bound by giving a new proof of the Edwards-Erd{\H o}s bound (each connected graph on $n$ vertices and $m$ edges has a bipartite subgraph with at least $m/2 + (n-1)/4$ edges) and obtaining a generalization.