Related Experiment Video
Updated: Jul 7, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
A parallel improvement algorithm for the bipartite subgraph problem
K C Lee1, N Funabiki, Y Takefuji
1Cirrus Logic Inc., Fremont, CA.
Abstract:
The authors propose the first parallel improvement algorithm using the maximum neural network model for the bipartite subgraph problem. The goal of this NP-complete problem is to remove the minimum number of edges in a given graph such that the remaining graph is a bipartite graph. A large number of instances have been simulated to verify the proposed algorithm, with the simulation result showing that the algorithm finds a solution within 200 iteration steps and the solution quality is superior to that of the best existing algorithm. The algorithm is extended for the K-partite subgraph problem where no algorithm has been proposed.
Related Concept Videos
Parallel-axis Theorem
Graphical Representation of Inequalities
Theorems of Pappus and Guldinus: Problem Solving
Parallel-Axis Theorem for an Area
For a flywheel approximated as a solid disc, consider an infinitesimal differential element with an arbitrary distance...
Solving Inequalities Graphically
Block Diagram Reduction
The first step in this process is the identification and relocation of a branch point. A branch point, where a...