Derandomization of Online Assignment Algorithms for Dynamic Graphs
Ankur Sahai
TL;DR
This paper evaluates online algorithms for dynamic bipartite graph edge assignment, focusing on derandomizing a randomized approach to improve predictability and reliability in cost minimization under changing conditions.
Contribution
It introduces a derandomization method for an existing randomized online algorithm in dynamic graph assignment problems.
Findings
Simulation results demonstrate improved performance of the derandomized algorithm.
The derandomization enhances algorithm predictability without sacrificing efficiency.
The approach is applicable to real-time dynamic graph scenarios.
Abstract
This paper analyzes different online algorithms for the problem of assigning weights to edges in a fully-connected bipartite graph that minimizes the overall cost while satisfying constraints. Edges in this graph may disappear and reappear over time. Performance of these algorithms is measured using simulations. This paper also attempts to derandomize the randomized online algorithm for this problem.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOptimization and Search Problems · Distributed systems and fault tolerance · Caching and Content Delivery