為了提高同學們的學習動機，提供以下相關的資訊，以幫助同學們找到方向。也和暑期實習和未來就業中，決策支援系統中的演算法有密切關聯。以下許多的內容屬於碩博士階段的課程，也可以增加同學們就讀研究所的動機：
- Journals:
- INFORMS Journal on Applied Analytics
- INFORMS is the leading international association for Operations Research & Analytics professionals.
- The mission of INFORMS Journal on Applied Analytics is to publish manuscripts focusing on the practice of operations research and management science and the impact this practice has on organizations throughout the world.
- Good topics to be explored for the final project
- Ramayya Krishnan and Pascal Van Hentenryck, editors, Advances in Integrating AI & O.R., INFORMS EC2021, Volume 16, April 19, 2021.
- Philip M. Morse, Methods Of Operations Research, 1951. (Morse: the father of operations research in the U.S.)
- Undergraduate courses
- Prof. James Orlin and Dr. Ebrahim Nasrabadi, 15.053 Undergraduate Optimization Methods In Management Science, Spring 2013, MIT (book, MIT OCW)
- B. Stellato, ORF307: Optimization, Princeton University. (Many applications and Python codes in CVXPY) (OR at Princeton)
- Applications in business and engineering:
- Sunil Chopra, Supply Chain Management: Strategy, Planning, and Operation, Pearson, 7th edition, 2018.
- Quantitative approaches to SCM
- Management by the Numbers
- Gérard Cornuéjols, Javier Peña, and Reha Tütüncü, Optimization Methods in Finance, Cambridge University Press, 2nd edition, 2018.
- Finance is a hugh industry (as in 0050).
- R. Sioshansi and A.J. Conejo, Optimization in Engineering, Springer, 2017. (ebook at CYCU)
- Offers a problem-solving approach and a large number of illustrative examples leading to a step-by-step formulation and solving of optimization problems
- Discussions are based on real-world examples and case studies
- Wayne L. Winston, Marketing Analytics: Data-Driven Techniques with Microsoft Excel, Wiley, 2014.
- Good marketing topics to be explored for the final project
- Classic and nice textbooks:
- Dimitri P. Bertsekas, Nonlinear Programming, 3rd Edition, Athena Scientific, 2016.
- Dimitris Bertsimas and John N. Tsitsiklis, Introduction to Linear Optimization, Athena Scientific, 1997.
- Stephen Boyd and Lieven Vandenberghe, Convex Optimization, Cambridge University Press, 2004.
- Book, A MOOC, codes
- 3 chapters on applications
- Giuseppe C. Calafiore and Laurent El Ghaoui, Optimization Models, Cambridge University Press, 2014.
- Accompanied by numerous end-of-chapter problems, an online solutions manual for instructors, and relevant examples from diverse fields including data science, engineering design, economics, finance, and management, this is the perfect introduction to optimization for undergraduate and graduate students.
- Mykel J. Kochenderfer and Tim A. Wheeler, Algorithms for Optimization, MIT Press, 2019.
- Free book, Julia codes
- Many examples to illustrate the idea and the algorithms in the whole book
- David G. Luenberger and Yinyu Ye, Linear and Nonlinear Programming, Springer, 5th edition, 2021. (ebook at CYCU)
- Although the book covers primarily material that is now fairly standard, this edition emphasizes methods that are both state-of-the-art and popular in emerging fields such as Data Sciences, Machine Learning, and Decision Analytics.
- Highly related to data science and machine learning
- Dimitris Bertsimas and Jack Dunn, Machine Learning Under a Modern Optimization Lens, Dynamic Ideas LLC, 2019.
- Required course for the Master of Business Analytics at MIT
- Dimitris Bertsimas and Dick Den Hertog, Robust and Adaptive Optimization, Dynamic Ideas, 2022.
- Decision making under uncertain environment (in OR2)
- Justin J. Boutilier and Timothy C. Y. Chan, Introducing and Integrating Machine Learning in an Operations Research Curriculum: An Application-Driven Course, INFORMS Transactions on Education, 22 Sep 2021. (IE at Wisconsin–Madison and Toronto)
- Roman Garnett, Bayesian Optimization, Cambridge University Press, 2023.
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016.
- Math is important to understand AI and machine learning.
- Yoshua Bengio received the 2018 ACM A.M. Turing Award (often referred to as the "Nobel Prize of Computing"), together with Geoffrey Hinton and Yann LeCun, for their work on deep learning.
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, Second Edition, February 2009.
- Free book and more
- Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray, Algorithms for Decision Making, MIT Press, 2022.
- Free book, Julia codes
- Decision making under uncertain environment (in OR2)
- John Wright and Yi Ma, High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications, Cambridge University Press, 2022.
- Free: a Preproduction Version and Lecture Slides
- Each chapter in Part I starts with many application examples to motivate the study.
- 7 chapters on applications to real-world problems in Part III
- Stephen Wright and Benjamin Recht, Optimization for Data Analysis, Cambridge University Press, 2022. (Full book accesible by using CYCU account)
- More books by seaching "optimization python" or "data science optimization" at Amazon
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