Linear Programming and AI Optimization Models VOL-1
Foundations Algorithms and Modern Applications in Operations Research and Machine Learning
Unlock the power of optimization in the age of AI.
"Linear Programming and AI Optimization Models" delivers a masterful blend of classical Operations Research and modern Machine Learning applications. From the elegant Simplex Method to advanced decomposition techniques and KKT conditions, this Volume-1 builds a rock-solid foundation while demonstrating how these powerful algorithms .
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About the Book
"Linear Programming and AI Optimization Models: Foundations, Algorithms, and Modern Applications in Operations Research and Machine Learning (VOL-1)" is a comprehensive, self-contained guide that bridges classical optimization techniques with cutting-edge applications in Artificial Intelligence and Machine Learning.
This volume provides a rigorous yet accessible treatment of Linear Programming (LP), Integer Programming, and Nonlinear Programming, while highlighting their growing relevance in modern AI systems, operations research, supply chain optimization, logistics, resource allocation, and data-driven decision making.
Through clear explanations, detailed mathematical formulations, algorithmic walkthroughs, and real-world case studies, readers will master both the theoretical foundations and practical implementation of optimization models. The book emphasizes the synergy between traditional Operations Research (OR) methods and contemporary AI/ML techniques, making it an essential resource for professionals and academics navigating the intersection of these fields.
Key Features:
- Progressive structure from foundational mathematics to advanced algorithms
- Detailed coverage of the Simplex Method, duality, decomposition techniques, Branch-and-Bound, and KKT conditions
- Rich collection of numerical examples, modeling exercises, and industry applications
- Explicit connections between classical optimization and modern AI/ML models
- Ideal for self-study, classroom use, and professional reference
Whether you are a student, researcher, data scientist, or operations professional, this book equips you with the tools to formulate, solve, and interpret complex optimization problems in today's AI-powered world.
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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.
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