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

WordPress大学
WordPress大学
酷 壳 – CoolShell
酷 壳 – CoolShell
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
腾讯CDC
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Jina AI
Jina AI
N
Netflix TechBlog - Medium
有赞技术团队
有赞技术团队
博客园 - 【当耐特】
MongoDB | Blog
MongoDB | Blog
P
Proofpoint News Feed
L
LangChain Blog
aimingoo的专栏
aimingoo的专栏
GbyAI
GbyAI
B
Blog
F
Fortinet All Blogs
T
Tailwind CSS Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
G
Google Developers Blog
A
About on SuperTechFans
C
Check Point Blog
Microsoft Security Blog
Microsoft Security Blog
MyScale Blog
MyScale Blog
B
Blog RSS Feed

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
An Almost Exact Linear Complexity Algorithm of the Shorte...
K. Yu. Gorbunov, V. A. Lyubetsky · 2020-04-30 · via cs.DS updates on arXiv.org

A "genome structure" is a labeled directed graph with vertices of degree 1 or 2. A set of operations over such graphs is fixed, and each of the operations has a certain cost, a strictly positive number. The transformation problem consists in the following: for given structures a and b and given costs, find a minimum total cost sequence of operations transforming a into b ("the shortest transformation of a into b"). Each operation corresponds to an "event", the latter being a change in the graph caused by executing one of the operations over it. The possibility of assigning different costs is important in applications, since it allows to distinguish between frequent and rare events. Apparently, arbitrary costs make the problem NP-hard, which results in nontriviality of passing from one restriction on costs to another, if the problem is solved by a linear or at least polynomial algorithm (assuming that P is not equal NP). We propose a novel linear time and space algorithm which constructs a sequence of operations transforming a to b with total cost close or equal to the absolute minimum. Namely, if all the so-called DCJ operations have the same cost w and if deletions and insertions have costs either both larger or both less than w, then the algorithm outputs a transformation of a into b with the total cost differing from the absolute minimum by at most 2w (in the former case) or equal to it (in the latter case). In some cases, the algorithm outputs an exact solution, e.g., in the case of circular genome structures. The condition on the costs of deletions and insertions can be omitted (although the proof described below does not include this general case).