Smoothed Analysis of Belief Propagation for Minimum-Cost Flow and Matching

Smoothed analysis of belief propagation and minimum-cost flow algorithms

Acknowledgments At the start of a Ph.D. project, you never know where it will take you. Though the research plan for my project contained several paragraphs on the planned research for the first two years, the plan for the last two years consisted of only a couple of lines. Even this concise plan turned out to be too detailed, since some research directions turned out to be less promising than ...

Smoothed Analysis of Belief Propagation for Minimum-Cost Flow and Matching

Belief propagation (BP) is a message-passing heuristic for statistical inference in graphical models such as Bayesian networks and Markov random fields. BP is used to compute marginal distributions or maximum likelihood assignments and has applications in many areas, including machine learning, image processing, and computer vision. However, the theoretical understanding of the performance of B...

Belief Propagation for Minimum Convex-Cost Integral Network Flow

A Novel Experimental Analysis of the Minimum Cost Flow Problem

In the GA approach the parameters that influence its performance include population size, crossover rate and mutation rate. Genetic algorithms are suitable for traversing large search spaces since they can do this relatively fast and because the mutation operator diverts the method away from local optima, which will tend to become more common as the search space increases in size. GA’s are base...

Belief Propagation for Min-Cost Network Flow: Convergence and Correctness

Distributed, iterative algorithms operating with minimal data structure while performing little computation per iteration are popularly known as message-passing in the recent literature. Belief Propagation (BP), a prototypical message-passing algorithm, has gained a lot of attention across disciplines including communications, statistics, signal processing and machine learning as an attractive ...