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- Title: Optimization for Decision Making
- Author(s) Víctor Yepes, José M. Moreno-Jiménez, Katta G. Murty
- Publisher: Mdpi AG (October 8, 2020); eBook (Creative Commons Licensed)
- License(s): Creative Commons License (CC)
- Hardcover: 290 pages
- eBook: PDF
- Language: English
- ISBN-10: 3039432206
- ISBN-13: 978-3039432202
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Bring together a collection of inter-/multi-disciplinary works applied to the optimization for decision making in a coherent manner, give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools.
About the Authors- N/A
- Operations Research (OR), Linear Programming, Optimization, Approximation, etc.
- Machine Learning
- Algorithms and Data Structures
- Computational and Algorithmic Mathematics

- Optimization for Decision Making (Víctor Yepes, et al.)
- Optimization Models For Decision Making (Katta G. Murty)
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Optimization Problems in Transportation and Logistics
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Algorithms for Decision Making (Mykel Kochenderfer, et al)
This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them.
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Convex Optimization for Machine Learning (Changho Suh)
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Convex Optimization (Stephen Boyd, et al.)
On recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples in fields such as engineering, computer science, mathematics, statistics, finance, and economics.
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Introduction to Online Convex Optimization (Elad Hazan)
This book presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed.
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The LION Way: Machine Learning Plus Intelligent Optimization
This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties.
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Convex Bodies and Algebraic Geometry: Theory of Toric Varieties
This book is an accessible introduction to current algebraic geometry of toric varieties gives new insight into continued fractions as well as their higher-dimensional analogues, the isoperimetric problem and other questions on convex bodies.
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Global Optimization Algorithms - Theory and Application
This book is devoted to global optimization algorithms, which are methods to find optimal solutions for given problems. It especially focuses on Evolutionary Computation by discussing evolutionary algorithms, genetic algorithms, Genetic Programming, etc.
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Operations Research - the Art of Making Good Decisions
This book is dedicated to operations research of broad applications, it provides a tool for efficient use of natural resources. Both theory and practice of operations research and its related concepts are covered in the book.
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Engineering Design Optimization (Joaquim R. Martins, et al)
The philosophy of this book is to provide a detailed enough explanation and analysis of optimization methods so that readers can implement a basic working version. Practical tips are included for common issues encountered in practical engineering design optimization.
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