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

Hugging Face - Blog
Hugging Face - Blog
V
Visual Studio Blog
Last Week in AI
Last Week in AI
Stack Overflow Blog
Stack Overflow Blog
The GitHub Blog
The GitHub Blog
Recent Announcements
Recent Announcements
博客园 - Franky
D
DataBreaches.Net
B
Blog
Y
Y Combinator Blog
T
The Blog of Author Tim Ferriss
Microsoft Azure Blog
Microsoft Azure Blog
人人都是产品经理
人人都是产品经理
WordPress大学
WordPress大学
P
Proofpoint News Feed
J
Java Code Geeks
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Martin Fowler
Martin Fowler
月光博客
月光博客
宝玉的分享
宝玉的分享
Engineering at Meta
Engineering at Meta
阮一峰的网络日志
阮一峰的网络日志
F
Fortinet All Blogs
博客园 - 【当耐特】

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
Deterministic Truncation of Linear Matroids
Daniel Lokshtanov, Pranabendu Misra, Fahad Panolan, Saket Saurab · 2014-04-17 · via cs.DS updates on arXiv.org

Let $M=(E,{\cal I})$ be a matroid. A {\em $k$-truncation} of $M$ is a matroid {$M'=(E,{\cal I}')$} such that for any $A\subseteq E$, $A\in {\cal I}'$ if and only if $|A|\leq k$ and $A\in {\cal I}$. Given a linear representation of $M$ we consider the problem of finding a linear representation of the $k$-truncation of this matroid. This problem can be abstracted out to the following problem on matrices. Let $M$ be a $n\times m$ matrix over a field $\mathbb{F}$. A {\em rank $k$-truncation} of the matrix $M$ is a $k\times m$ matrix $M_k$ (over $\mathbb{F}$ or a related field) such that for every subset $I\subseteq \{1,\ldots,m\}$ of size at most $k$, the set of columns corresponding to $I$ in $M$ has rank $|I|$ if and only of the corresponding set of columns in $M_k$ has rank $|I|$. Finding rank $k$-truncation of matrices is a common way to obtain a linear representation of $k$-truncation of linear matroids, which has many algorithmic applications. A common way to compute a rank $k$-truncation of a $n \times m$ matrix is to multiply the matrix with a random $k\times n$ matrix (with the entries from a field of an appropriate size), yielding a simple randomized algorithm. So a natural question is whether it possible to obtain a rank $k$-truncations of a matrix, {\em deterministically}. In this paper we settle this question for matrices over any finite field or the field of rationals ($\mathbb Q$). We show that given a matrix $M$ over a field $\mathbb{F}$ we can compute a $k$-truncation $M_k$ over the ring $\mathbb{F}[X]$ in deterministic polynomial time.