Open Access
Combinatorial Optimization Networks And Matroids
Nicole Fassbinder
- 01 Jan 2016
334
TL;DR: The combinatorial optimization networks and matroids is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can download it instantly.
read more
Abstract: combinatorial optimization networks and matroids is available in our digital library an online access to it is set as public so you can download it instantly. Our book servers hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the combinatorial optimization networks and matroids is universally compatible with any devices to read.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Dynamic Programming
TL;DR: The study of brain processes has been spurred by the development of the digital computer. Understanding the ability of the human mind to make effective decisions in complex and uncertain situations would significantly improve the effectiveness of computers.
7.3K
•Book
The Algorithm Design Manual
Steven Skiena
- 01 Jan 1980
TL;DR: This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficacy and efficiency.
1.3K
Linear-Time Approximation for Maximum Weight Matching
Ran Duan,Seth Pettie +1 more
TL;DR: This article gives an algorithm that computes a (1 − 1 − 0))-approximate maximum weight matching in O(i) time, that is, optimal linear time for any fixed ε, and should be appealing in all applications that can tolerate a negligible relative error.
Submodular maximization with cardinality constraints
Niv Buchbinder,Moran Feldman,Joseph (Seffi) Naor,Roy Schwartz +3 more
- 05 Jan 2014
TL;DR: Improved approximations for two variants of the cardinality constraint for non-monotone functions are presented and a simple randomized greedy approach is presented where in each step a random element is chosen from a set of "reasonably good" elements.
Iterative Modulo Scheduling
TL;DR: In this paper, the authors present an iterative modulo scheduling, a practical algorithm that is capable of dealing with realistic machine models and characterizes the algorithm in terms of the quality of the generated schedules as well as the computational expense incurred.
206
References
Shape optimization of thin-walled steel sections using graph theory and ACO algorithm
TL;DR: In this article, the shape and sizing optimizations of open and closed thin-walled steel sections using the graph theory are presented. But the optimization of closed sections is treated as a multi-objective all-pairs shortest path problem, while that of open sections is a multiobjective minimum mean cycle problem.
61
Fair-by-design matching
TL;DR: This paper introduces the distributional maxmin fairness framework, a polynomial-time algorithm building on techniques from minimum cuts, and edge-coloring algorithms for regular bipartite graphs, and transversal theory, which provides the strongest guarantee possible simultaneously for all individuals in terms of satisfaction probability.
31
Minimum-Cost Flows in Unit-Capacity Networks
TL;DR: It is shown that the classical blocking flow push-relabel cost-scaling algorithms of Goldberg and Tarjan for general minimum-cost flow problems achieve the best known bounds for unit-capacity problems as well.
20
•Posted Content
Parametric matroid of rough set
Yanfang Liu,William Zhu +1 more
TL;DR: This paper proposes a new matroidal structure of rough sets and calls it a parametric matroid, which is proved to be the direct sum of a partition-circuit matroid and a free matroid.
11
Fast Algorithms for the Undirected Negative Cost Cycle Detection Problem
TL;DR: These two approaches for solving the undirected negative cost cycle detection problem are formally described and the tightest known analysis of the b-matching approach is provided, showing that while the T-join algorithm has a better time bound for general graphs, theb-matched algorithm is more efficient for sparse graphs.
4
Related Papers (5)
Eric Mayer
- 01 Jan 2016
Christin Wirth
- 01 Jan 2016
Erik Kaestner
- 01 Jan 2016
Gabriele Eisenhauer
- 01 Jan 2016
Benjamin Naumann
- 01 Jan 2016