Linear Programming and AI Optimization Models VOL-2
Foundations Algorithms and Modern Applications in Operations Research and Machine Learning
Take your optimization skills to the next level with AI.
Linear Programming and AI Optimization Models (VOL-2) dives deep into the algorithms driving modern Machine Learning and Intelligent Systems. Master Gradient Descent, Adam, Reinforcement Learning, Genetic Algorithms, Particle Swarm Optimization, Constraint Satisfaction, and advanced Network Models.
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About
About the Book
Linear Programming and AI Optimization Models: Foundations, Algorithms, and Modern Applications in Operations Research and Machine Learning (VOL-2) continues the journey from classical optimization into the dynamic realm of Artificial Intelligence and advanced computational techniques.
This volume explores how optimization lies at the heart of modern Machine Learning, Deep Learning, Reinforcement Learning, and intelligent systems. It provides an in-depth treatment of gradient-based methods, metaheuristics, evolutionary algorithms, constraint satisfaction problems, and network optimization models, with strong emphasis on their practical applications in AI.
Readers will gain both theoretical understanding and implementation insights into algorithms that power today’s intelligent systems — from training large neural networks to solving complex scheduling, routing, and decision-making problems.
Key Features:
- Detailed coverage of Gradient Descent variants, Adam, Backpropagation, and optimization in Deep Learning
- Comprehensive exploration of Reinforcement Learning through an optimization lens
- Advanced heuristic and evolutionary methods (Genetic Algorithms, PSO, Simulated Annealing, etc.)
- Constraint Satisfaction Problems and their AI applications
- Network optimization models with real-world relevance
- Clear connections between classical OR and cutting-edge AI techniques
Perfect as a standalone advanced reference or as a natural continuation of Volume-1, this book is designed for graduate students, researchers, data scientists, AI engineers, and operations professionals seeking to master optimization in the AI era.
Author
About the Author
Anshuman Kumar Mishra, M.Tech (Computer Science) Assistant Professor, Doranda College, Ranchi University
Prolific Author of 50+ Books on AI, Machine Learning & Computer Science | 20+ Years Experience
Anshuman Kumar Mishra is a dedicated educator, researcher, and highly prolific author with over 20 years of experience in Computer Science and Information Technology. Holding an M.Tech in Computer Science from BIT Mesra, he brings a rare combination of academic depth and practical teaching expertise.
Currently serving as Assistant Professor at Doranda College under Ranchi University, he has mentored thousands of students, helping them build strong foundations in programming, data science, and artificial intelligence. His student-centric teaching style emphasizes conceptual clarity, hands-on practice, and real-world application.
Anshuman is a prolific author with more than 50 books published across a wide spectrum of computer science and emerging technology domains. From foundational programming languages to advanced topics in Artificial Intelligence, Machine Learning, Reinforcement Learning, Decision Theory, and Computer Vision — his books are widely appreciated by students, educators, and professionals for their clear explanations, strong theoretical foundation, and practical approach.
His extensive body of work reflects his deep commitment to making complex subjects accessible and meaningful for learners at all levels. He is particularly recognized for creating well-structured learning paths that help readers progress from beginner to advanced levels with confidence.
Driven by the mission to democratize quality technical education, Anshuman continues to write and update books that bridge the gap between academic theory and industry practice.
When not teaching or writing, he actively follows and explores new developments in AI, Quantum Machine Learning, and Ethical Intelligence systems.
Contents
Table of Contents
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