專題和論文的製作與報告 (tips for the final project and your thesis)

• (at the bottom) Avoid common phenomena, final written report (for your ppt content)
• (大學生) 重要任務

• 人生困境：

• 計算機程式補考後的人生 (1/2) 

• 我的方法：運動，音樂 (馬友友)，閱讀 (哲學，堅忍的挑戰，老莊)

• Tal Ben-Shahar, Happier: Learn the Secrets to Daily Joy and Lasting Fulfillment, McGraw Hill, 2007. (譚家瑜譯，更快樂：哈佛最受歡迎的一堂課，天下雜誌，2012)

Some information and papers related to the seminar

(4/9/2025) 許志華 (The original speaker needs to take care of emergency affairs.)

• Dimitris Bertsimas and Michael Lingzhi Li, Scalable holistic linear regression, Operations Research Letters, Volume 48, Issue 3, May 2020, Pages 203-208.
• We could not reproduce their results in the paper, so I asked Prof. Li for his code. He told me he finished the paper as a course project (MIT style?!😂), and the codes were stored on another obsolete hard drive. 
  

人工智慧研究得到 2024 諾貝爾物理學和化學獎

2024 諾貝爾物理學獎新聞

Chip Placement with Diffusion

Vint Lee, Chun Deng, Leena Elzeiny, Pieter Abbeel, and John Wawrzynek, Chip Placement with Diffusion, arXiv:2407.12282.

Fluid approximations for stochastic optimization

When one encounters a stochastic optimization/control problem, one popular approach is to transform it into a deterministic problem by fluid approximation. The following highly-cited classic papers illustrate the applications of this approach:   

10 個改變世界的統計觀念

Leda Liang, Ten Statistical Ideas that Changed the World, (YouTube, Stanford course, Tibshirani's GitHub)

學習數學的四個層次：(3) 在許多行業的應用

學習數學的四個層次：(0) 如何學數學，(1) 代表具備基礎的知識與能力，(2) 邏輯推理和抽象思考的能力，(3) 在許多行業的應用，(4) 純粹滿足好奇心或求知慾。

2015/12/1 初稿，持續更新中。

一般性說明

• 數學是科學之母，科學則是工業的基礎，所以大學工學院的數理化課程總學分超過 1/3。可以參考如何選填大學志願。
• 應用在不同的領域 (理工商醫農、教育)，如財務工程、設計電腦、貨物產銷、工程師、使用統計學分析學習成效等等。
• 抽象的模式與思考的方式，適用於現在與未來的應用，以微分為例，物理學的距離微分是速度，經濟學中成本的微分是邊際成本，電子學的電荷微分是電流。也就是說，可以使用函數表示任何待解的問題，函數的微分便可以研究其變化和極值的情況，例如機器學習中，超參數 (hyperparameter) 的學習 。
• 基本的原則變動不大，微積分、機率和統計學、和線性代數已經有 200 年以上的歷史，可幫助未來的自我學習。許多人說學校學的東西，畢業後立即過時或沒用，我覺得很疑惑。大學只是基礎教育，必須不斷地學習新的東西，以因應產業和職務的變化；最近熱門的大數據 (big data) 和人工智慧 (artificial intelligence)，其數學基礎正是這些課程。
  

Queueing Theory: Classical and Modern Methods

Dimitris Bertsimas and David Gamarnik, Queueing Theory: Classical and Modern Methods, ‎Dynamic Ideas, 2022.

STRUCTURE OF THE BOOK:

• Part I describes single and multi-server queues.
• Part II treats single and multiclass queueing networks (MQNETs).
• Part III introduces asymptotic methods, including queueing networks in heavy traffic, large deviations, call centers, queues in space, and the supermarket model.
• Part IV outlines the use of optimization in queueing networks.
• Part V presents Markov chains and processes, Brownian motion, and weak convergence in the Appendix.

Optimization in Online Content Recommendation Services

Omar Besbes, Yonatan Gur, Assaf Zeevi, Optimization in Online Content Recommendation Services: Beyond Click-Through Rates, 18(1), pp. 15–33, Manufacturing & Service Operations Management, Volume 18, Issue 1, Winter 2016. 

A new class of online services allows Internet media sites to direct users from articles they are currently reading to other content they may be interested in. This process creates a “browsing path” along which there is potential for repeated interaction between the user and the provider, giving rise to a dynamic optimization problem. A key metric that often underlies this recommendation process is the click-through rate (CTR) of candidate articles. Whereas CTR is a measure of instantaneous click likelihood, we analyze the performance improvement that one may achieve by some lookahead that accounts for the potential future path of users. To that end, by using some data of user path history at major media sites, we introduce and derive a representation of content along two key dimensions: clickability, the likelihood to click to an article when it is recommended; and engageability, the likelihood to click from an article when it hosts a recommendation. We then propose a class of heuristics that leverage both clickability and engageability, and provide theoretical support for favoring such path-focused heuristics over myopic heuristics that focus only on clickability (no lookahead). We conduct a live pilot experiment that measures the performance of a practical proxy of our proposed class, when integrated into the operating system of a worldwide leading provider of content recommendations, allowing us to estimate the aggregate improvement in clicks per visit relative to the CTR-driven current practice. The documented improvement highlights the importance and the practicality of efficiently incorporating the future path of users in real time.

A Practical End-to-End Inventory Management Model with Deep Learning

Meng Qi, Yuanyuan Shi, Yongzhi Qi, Chenxin Ma, Rong Yuan, Di Wu, Zuo-Jun (Max) Shen (2023) A Practical End-to-End Inventory Management Model with Deep Learning. Management Science 69(2):759-773. (Data and Python codes) 

We investigate a data-driven multiperiod inventory replenishment problem with uncertain demand and vendor lead time (VLT) with accessibility to a large quantity of historical data. Different from the traditional two-step predict-then-optimize (PTO) solution framework, we propose a one-step end-to-end (E2E) framework that uses deep learning models to output the suggested replenishment amount directly from input features without any intermediate step. The E2E model is trained to capture the behavior of the optimal dynamic programming solution under historical observations without any prior assumptions on the distributions of the demand and the VLT. By conducting a series of thorough numerical experiments using real data from one of the leading e-commerce companies, we demonstrate the advantages of the proposed E2E model over conventional PTO frameworks. We also conduct a field experiment with JD.com, and the results show that our new algorithm reduces holding cost, stockout cost, total inventory cost, and turnover rate substantially compared with JD’s current practice. For the supply chain management industry, our E2E model shortens the decision process and provides an automatic inventory management solution with the possibility to generalize and scale. The concept of E2E, which uses the input information directly for the ultimate goal, can also be useful in practice for other supply chain management circumstances.

Data-driven research in retail operations

M. Qi, H.Y. Mak, and Z.J.M. Shen, Data‐driven research in retail operations—A review, Naval Research Logistics, 2020, 67 (8), 595-616. (Open access)

We review the operations research/management science literature on data-driven methods in retail operations. This line of work has grown rapidly in recent years, thanks to the availability of high-quality data, improvements in computing hardware, and parallel developments in machine learning methodologies. We survey state-of-the-art studies in three core aspects of retail operations—assortment optimization, order fulfillment, and inventory management. We then conclude the paper by pointing out some interesting future research possibilities for our community.

Global and Robust Optimization for Engineering Design

 Berk Öztürk, Global and Robust Optimization for Engineering Design, Ph.D. Thesis, MIT, 2022. (thesis, code, talk)

There is a need to adapt and improve conceptual design methods through better optimization, in order to address the challenge of designing future engineered systems. Aerospace design problems are tightly-coupled optimization problems, and require all-at-once solution methods for design consensus and global optimality. Although the literature on design optimization has been growing, it has generally focused on the use of gradient-based and heuristic methods, which are limited to local and low-dimensional optimization respectively. There are significant benefits to leveraging structured mathematical optimization instead. Mathematical optimization provides guarantees of solution quality, and is fast, scalable, and compatible with using physics-based models in design. More importantly perhaps, there has been a wave of research in optimization and machine learning that provides new opportunities to improve the engineering design process. This thesis capitalizes on two such opportunities.

7 real-world applications of reinforcement learning

 Joy Zhang, 7 real-world applications of reinforcement learning, gocoder, February 17, 2022

1. Autonomous driving with Wayve

2. Personalizing your Netflix recommendations

3. Optimizing inventory levels for Walmart

4. Improving search engine results with search.io

5. Improving language models with OpenAI's WebGPT

6. Trading on the financial markets with IBM's DSX platform

7. Robotics with the University of California, Berkeley

Guessing Random Additive Noise Decoding

 Prof. Muriel Médard, Guessing Random Additive Noise Decoding (GRAND), Rice U ECE, 2020年9月7日

洞悉市場的人

林錦慧譯，洞悉市場的人：量化交易之父吉姆‧西蒙斯與文藝復興公司的故事，天下文化，2020

Gregory Zuckerman, The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution, Penguin Group, 2019

在投資界，沒有哪個傳奇大師比吉姆‧西蒙斯更加神祕，他的前半生是頂尖數學家，不但曾幫助美國政府破解蘇聯密碼，還成功打造世界級的數學系。

但四十歲後，他轉戰投資界，開啟量化投資的風潮。他旗下的大獎章基金1988年至2018年的平均年化報酬率高達66.1％，就算扣除基金收取的各項費用，平均年化報酬率也高達39.1％ (*)，遠勝過巴菲特、彼得‧林區、雷‧達利歐、喬治‧索羅斯等股票大師。

他聘請頂尖數學家、物理學家與電腦工程師，藉由蒐集市場各項數據發展出一套演算法，找到買進與賣出的交易訊號，讓機器自動交易，他在華爾街掀起量化交易革命。不但成功打敗市場，還躲過歷年來的重大股災，持續擁有優異績效。

西蒙斯和他的同事擁有的財富已經富可敵國，現在開始轉往科學研究、教育與政治界發展。其中公司的前執行長羅伯特‧莫瑟還成為川普的大金主，莫瑟的女兒更是劍橋分析公司的主要投資人，在美國政壇發揮影響力。

本書作者古格里‧佐克曼採訪西蒙斯多位現任與離職員工後，寫下這個金融史上的傳奇故事，幫助我們一窺這位頂尖數學家如何掀起量化交易革命，顛覆華爾街的傳統模式。

Probability in Electrical Engineering and Computer Science

Jean Walrand, Probability in Electrical Engineering and Computer Science: An Application-Drive Course, Springer, 2021. (pdf, Python)

Showcases techniques of applied probability with applications in EE and CS

Presents all topics with concrete applications so students see the relevance of the theory

Illustrates methods with Jupyter notebooks that use widgets to enable the users to modify parameters

This book is open access, which means that you have free and unlimited access.

Algorithms for Decision Making

Mykel Kochenderfer, Tim Wheeler, and Kyle Wray, Algorithms for Decision Making, MIT Press, 2022. (pdf available online)

A broad introduction to algorithms for optimal decision making under uncertainty. We cover a wide variety of topics related to decision making, introducing the underlying mathematical problem formulations and the algorithms for solving them. Figures, examples, and exercises are provided to convey the intuition behind the various approaches. This text is intended for advanced undergraduates and graduate students as well as professionals. The book requires some mathematical maturity and assumes prior exposure to multivariable calculus, linear algebra, and probability concepts. Some review material is provided in the appendix. Disciplines where the book would be especially useful include mathematics, statistics, computer science, aerospace, electrical engineering, and operations research. Fundamental to this textbook are the algorithms, which are all implemented in the Julia programming language.

Prof. Kochenderfer also teaches 2 related courses at Stanford. 

Revenue Management and Pricing Analytics

Guillermo Gallego and Huseyin Topaloglu, Revenue Management and Pricing Analytics, Springer, 2019.

The book is divided into three parts: traditional revenue management, revenue management under customer choice, and pricing analytics. Each part is approximately of the same length and written in a self-contained way, so readers can read them independently, although reading the first part may make the second part easier to understand. Each chapter ends with bibliographical notes where the reader can find the sources of the material covered as well as many useful references. Proofs of some important technical results can be found in the appendix of each chapter. Solving the end-of-chapter problems helps reinforce the material in the book, with some of the questions expanding on the subject....

There is enough material in the book for a full-semester course for advanced undergraduate or master’s students. Parts I and II can be covered in about 9 weeks and Part III in about 4weeks excluding the last two chapters on online learning and competition, which can be assigned as independent readings. 

Analytics and Bikes: Riding Tandem with Motivate to Improve Mobility

Daniel Freund, S. G. Henderson, E. O’Mahony, and D. B. Shmoys, Analytics and Bikes: Riding Tandem with Motivate to Improve Mobility, INFORMS Journal on Applied Analytics, Vol. 49, No. 5, 2019, Pages 307-396. (First Prize: Wagner Prize for Excellence in Operations Research Practice)

Bike-sharing systems are now ubiquitous across the United States. We have worked with Motivate, the operator of the systems in, for example, New York, Chicago, and San Francisco, to both innovate a data-driven approach to managing their day-to-day operations and provide insight on several central issues in the design of its systems. This work required the development of a number of new optimization models, characterization of their mathematical structure, and use of this insight in designing algorithms to solve them. Here, we focus on two particularly high-impact projects: an initiative to improve the allocation of docks to stations and the creation of an incentive scheme to crowdsource rebalancing. Both of these projects have been fully implemented to improve the performance of Motivate’s systems across the country; for example, the Bike Angels program in New York City yields a system-wide improvement comparable with that obtained through Motivate’s traditional rebalancing efforts at far less financial and environmental cost.

Probabilistic Machine Learning

 "Probabilistic Machine Learning" - a book series by Kevin Murphy

Kevin Patrick Murphy, Probabilistic Machine Learning: An Introduction, MIT Press, 2021. (Python codes)

新的版本，作者花了很多時間整理。以第一部分的數學基礎為例，使用機器學習說明相關的概念。也納入時事，例如 2.3.1 節的 Testing for COVID-19。