Core Concepts
Predictions can be leveraged to improve Max-Cut approximation ratios, with ε-accurate predictions enhancing algorithm performance.
Abstract
Introduction to Graph Cuts:
Graph cuts are pivotal in algorithm design.
They bridge theory and practice, exploring beyond worst-case scenarios.
Research delves into random instances and optimal cuts resilient to noise.
Impact of Predictions:
Noisy and partial predictions models are studied.
Predictions aim to overcome information-theoretic and computational barriers.
Results and Techniques:
Noisy predictions model achieves an α + eΩ(ε4)-approximation.
Partial predictions model yields a β + Ω(ε)-approximation.
MaxCut Problem Description:
Weighted graph representation and Laplacian matrix usage.
Objective function formulation for MaxCut.
Noisy/Partial Predictions Framework:
Models defined for noisy and partial predictions.
Aim to capture scenarios with noisy predictions.
Data Extraction:
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