The MILP Optimization Handbook: A Comprehensive Guide to Mixed-Integer Linear Programming, Algorithms, and Real-World Applications - Tapa blanda

Sinopsis

A complete, modern guide to Mixed-Integer Linear Programming (MILP)—from fundamentals to advanced modeling and real-world applications.

Whether you’re a student, data scientist, or optimization professional, this handbook provides a clear, practical roadmap through every aspect of MILP modeling and algorithm design.
Written in accessible language, it combines rigorous mathematics with hands-on examples using open-source tools like Pyomo, CBC, and OR-Tools.

Inside you’ll find:

Learn the essentials of linear and integer programming, model formulation, and convexity.
Understand how constraints, objectives, and relaxations form the mathematical core of decision-making.

Explore the inner workings of branch-and-bound, cutting planes, Lagrangian relaxation, and decomposition methods.
Includes pseudocode, solver strategies, and step-by-step explanations of how MILP engines think.

Translate theory into practice across real domains:
scheduling, facility location, logistics, finance, energy planning, and even machine learning.

Discover how modern MILP handles uncertainty, large-scale data, and hybrid optimization with heuristics and machine learning.
Includes introductions to decomposition, stochastic MILP, robust optimization, and metaheuristics.

Quick-reference installation guides for major solvers, reusable Pyomo templates, a complete notation index, and a curated bibliography of essential MILP references.

By combining clarity, rigor, and real-world focus, The MILP Optimization Handbook bridges the gap between theory and implementation—making it an indispensable reference for anyone working in operations research, applied mathematics, AI optimization, or industrial analytics.

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