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Introduction to optimization-based decision making /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Introduction to optimization-based decision making /Joao Luis de Miranda.
作者:	Miranda, João Luis de,
面頁冊數:	pages cm.
標題:	Decision makingMathematical models.
ISBN:	9781138712164

Introduction to optimization-based decision making /
Miranda, João Luis de,

Introduction to optimization-based decision making /Joao Luis de Miranda. - First edition. - pages cm. - Chapman & Hall/CRC series in operations research.

Includes bibliographical references and index.

First notes on optimization for decision support -- Linear algebra -- Linear programming basics -- Duality -- Calculus optimization -- Optimality analysis -- Integer linear programming -- Game theory -- Decision making under uncertainty -- Robust optimization.

"The large and complex challenges the world is facing, the growing prevalence of huge data sets, and new and developing ways for addressing them (artificial intelligence, data science, machine learning etc.), means that it is increasingly vital that academics and professionals from across disciplines have a basic understanding of the mathematical underpinnings of effective, optimized decision making. Without it, decision makers risk being overtaken by those who better understand the models and methods, which can best inform strategic and tactical decisions. "Introduction to Optimization-Based Decision Making" provides an elementary and self-contained introduction to the basic concepts involved in making decisions in an optimization-based environment. The mathematical level of the text is directed to the post-secondary reader, or university students in the initial years. The pre-requisites are therefore minimal, and necessary mathematical tools are provided as needed. This lean approach is complemented with a problem-based orientation and a methodology of generalization/reduction. In this way, the book can be useful for students from STEM fields, economics and enterprise sciences, social sciences and humanities, as well as for the general reader interested in multi/trans-disciplinary approaches. Features Collects and discusses the ideas underpinning decision making through optimization tools in a simple and straightforward manner. Suitable for an undergraduate course in optimization-based decision making, or as a supplementary resource for courses in operations research and management science. Self-contained coverage of traditional and more modern optimization models, while not requiring a previous background in decision theory"--

ISBN: 9781138712164

LCCN: 2021021796Subjects--Topical Terms:

190791
Decision making
--Mathematical models.

LC Class. No.: T57.95 / .M565 2022

Dewey Class. No.: 658.4/03

Introduction to optimization-based decision making /
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490 0 $a Chapman & Hall/CRC series in operations research
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520 $a "The large and complex challenges the world is facing, the growing prevalence of huge data sets, and new and developing ways for addressing them (artificial intelligence, data science, machine learning etc.), means that it is increasingly vital that academics and professionals from across disciplines have a basic understanding of the mathematical underpinnings of effective, optimized decision making. Without it, decision makers risk being overtaken by those who better understand the models and methods, which can best inform strategic and tactical decisions. "Introduction to Optimization-Based Decision Making" provides an elementary and self-contained introduction to the basic concepts involved in making decisions in an optimization-based environment. The mathematical level of the text is directed to the post-secondary reader, or university students in the initial years. The pre-requisites are therefore minimal, and necessary mathematical tools are provided as needed. This lean approach is complemented with a problem-based orientation and a methodology of generalization/reduction. In this way, the book can be useful for students from STEM fields, economics and enterprise sciences, social sciences and humanities, as well as for the general reader interested in multi/trans-disciplinary approaches. Features Collects and discusses the ideas underpinning decision making through optimization tools in a simple and straightforward manner. Suitable for an undergraduate course in optimization-based decision making, or as a supplementary resource for courses in operations research and management science. Self-contained coverage of traditional and more modern optimization models, while not requiring a previous background in decision theory"-- $c Provided by publisher.
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