Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Stochastic Weighted Matching - 1-Epsilon Approximation

IEEE via YouTube

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a 29-minute IEEE conference talk on stochastic weighted matching, presented by Soheil Behnezhad and Mahsa Derakhshan from UMD. Delve into the (1-epsilon) approximation algorithm, starting with an introduction to the problem definition and its pictorial representation. Examine various algorithms, including Monte Carlo analysis and the greedy approach. Investigate key concepts such as the Weighted "Vertex-Independent Matching Lemma" and the challenges posed by low-probability high-weight edges. Gain insights into the lack of a "Sparsification Lemma" and conclude with a high-level overview of the final analysis, providing a comprehensive understanding of this complex topic in algorithmic graph theory.

Syllabus

Intro
Problem Definition
The Problem, Pictorially
Let's See Some Algorithms.
Analysis of Monte Carlo
The Weighted "Vertex-Independent Matching Lemma"
Low-Probability High-Weight Edges (cont'd)
Lack of "Sparsification Lemma"
The Greedy Algorithm
High-Level Overview of the Final Analysis
Conclusion

Taught by

IEEE FOCS: Foundations of Computer Science

Reviews

Start your review of Stochastic Weighted Matching - 1-Epsilon Approximation

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.