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Graph Theory: Binary Optimization

N.B. Singh

This audiobook is narrated by a digital voice. "Graph Theory: Binary Optimization" introduces fundamental graph theory concepts and their practical applications in binary optimization. This beginner-friendly book explains how graphs model real-world problems like network design and scheduling, equipping readers with essential skills in optimization techniques. Duration - 3h 52m. Author - N.B. Singh. Narrator - Digital Voice Mary G. Published Date - Monday, 20 January 2025. Copyright - © 2024 N.B. Singh ©.

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Preface

1/19/2025
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Introduction to Graph Theory and Binary Optimization

1/19/2025
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Overview of Graph Theory

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Basics of Binary Optimization

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Historical Context and Applications

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Key Challenges and Opportunities

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Mathematical Foundations

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Structure of the Book

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Fundamental Concepts in Graph Theory

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Graphs and Their Representations

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Types of Graphs

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Graph Connectivity

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Graph Traversal Techniques

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Graph Coloring and Covering

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Special Classes of Graphs

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Binary Decision Variables and Optimization Problems

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Definition and Examples of Binary Variables

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Formulating Optimization Problems

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Constraint Handling

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Objective Functions in Binary Optimization

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Feasibility and Solution Space

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Complexity and Computational Considerations

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Graph Algorithms for Binary Optimization

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Graph Search Algorithms

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Matching and Flow Algorithms

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Pathfinding Algorithms

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Cutting Plane Methods

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Branch and Bound Techniques

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Heuristics and Metaheuristics

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Integer Linear Programming and Graph Models

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Formulation of ILP Problems

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Graph-Based ILP Models

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Solution Techniques for ILP

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Duality and Relaxation

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Cutting Planes and Branch-and-Cut

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Applications in Network Design

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Applications of Graph Theory in Binary Optimization

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Network Design and Analysis

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Scheduling and Timetabling

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Resource Allocation Problems

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Facility Location and Layout

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Routing and Logistics

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Case Studies and Real-World Examples

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Advanced Topics in Binary Optimization and Graph Theory

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Advanced Heuristics and Metaheuristics

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Decomposition Methods

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Dynamic and Stochastic Optimization

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Approximation Algorithms

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Graph-Theoretic Insights in Machine Learning

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Emerging Trends and Future Directions

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