SKU/Artículo: AMZ-B0G5J35H4T

Linear Programming and AI Optimization Models VOL-2 (AI AND MATH NEW)

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  • Optimization is one of the oldest intellectual pursuits of humankind. It is embedded in every decision we make—how we allocate resources, how we plan operations, how we build efficient systems, and how we learn from data. From the earliest attempts to minimize cost and maximize profit to today’s intelligent systems that learn autonomously, the field of optimization has evolved into one of the most powerful scientific disciplines shaping modern society. In this book, “Linear Programming and AI Optimization Models: Foundations, Algorithms, and Modern Applications in Operations Research and Machine Learning,” I aim to provide a comprehensive, unified, and highly accessible resource that bridges the classical foundations of operations research with the emerging trends in artificial intelligence, machine learning, and computational optimization. While countless materials exist on these subjects individually, very few connect them in a structured, practical, and academically rigorous way. This book attempts to fill that gap. The idea for this book emerged from more than 18 years of teaching experience, research engagements, and interaction with thousands of students from undergraduate, postgraduate, and doctoral programs. Over the years, I have seen that learners struggle with one central difficulty: optimization is everywhere, but its understanding is scattered. Linear programming is taught in one course. Machine learning appears in another. Constraint solving comes from artificial intelligence. Neural networks rely on optimization, yet rarely is that optimization explained in mathematical detail. Real-world problems, however, do not come divided into chapters. They demand a holistic understanding. A supply chain optimization problem may require linear programming, reinforcement learning, constraint programming, and heuristic search simultaneously. A robotics navigation system might blend probabilistic optimization, nonlinear programming, and deep learning. A modern machine learning model may require convex optimization foundations and stochastic gradient methods. This book is my effort to bring these perspectives together into a single coherent structure. 1. Purpose of the Book The primary purpose of this book is to serve as a comprehensive guide that covers:The theoretical foundations of optimization, especially linear programming. This includes mathematical modeling, simplex methods, duality, sensitivity analysis, and advanced LP techniques. These foundations are essential because modern AI optimization builds heavily upon classical OR theory.The integration of optimization with machine learning and artificial intelligence. Machine learning models—from logistic regression to deep neural networks—rely fundamentally on optimization formulations. Reinforcement learning, probabilistic graphical models, Bayesian optimization, and evolutionary algorithms all represent different branches of optimization thinking.Practical implementations using modern tools. Students and professionals often ask: “How do I implement linear programming in Python?” “How do I solve MILP using OR-Tools?” “How do I apply gradient descent in real ML models?” “How do I design optimization-based AI systems with real datasets?” This book answers all of these through illustrative Python examples.Application-oriented case studies drawn from real industry domains. Today’s organizations—from logistics to healthcare to finance—rely on optimization models. This book includes structured case studies that reflect real industry scenarios, such as:airline schedulingvehicle routing and last-mile deliveryproduction planningrobotics autonomyenergy demand forecasting
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AR$66.897
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