MS&E111/211 Introduction to Optimization 2023-2024 Autumn

| Announcements (Updated Frequently) | General Info | Course Info | Handouts | Assignments |

• Class Times and Locations
  • Lectures: TuTh 12:00PM - 1:20PM.
  • Lecture Room: Gates B1.
  • Extra Session (Discussion, Make-Up Lectures): Friday 12:30-1:20, same room

• Prerequisites : MATH 51 or equivalent.

• Text: Linear and Nonlinear Programming, 5th Edition, Springer, by Luenberger and Ye. All other lecture notes will be distributed via the course website. Also click for Various Matlab Solvers, such as hsdLPsolver.m

Course Staff

• Professor: Yinyu Ye
  • Office Room: Huang 308 or ZOOM: https://stanford.zoom.us/my/yinyuye
  • Office hours: Tuesdays 1:30 - 2:30pm (Check the announcement site for any possible change), or by appointment
  • Email: yinyu-ye@stanford.edu
  • Phone: 723-7262
• Course Assistant: ?
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• Staff Support: Jackie Nguyen
  • Office: Huang
  • Email: jackie.nguyen@stanford.edu
  • Phone:

Grading and Exams

Your grade in this course would be based on a take-home midterm exam and a teacm project.

• Homework: 40%; graded and discussed in problem sessions;
• Midterm exam: 11/9 in class, 30%;
• A project (up to four students): report due 12/11 30%.

MS&E 111/211 requires no prior course in optimization, but it does have just one prerequisite: Mathematics 51 (Linear algebra & multivariate differential calculus) or equivalent. This means that students should, at the very least, be familiar with the concept of multi-variables calculus and basic manipulation of vectors and matrices, the elementary handling of inequalities, and a good grasp of such analytic concepts as continuity, differentiability, the gradient, etc.

Course Content
Mathematical and quantitative optimization theory and modeling such as linear programming, reinforced learning, etc. The role of prices, duality, optimality conditions, and algorithms in finding and recognizing solutions. Perspectives: problem formulation, analytical theory, computational methods, and recent applications in engineering, finance, and economics. Theoretical concepts: finite dimensional derivatives, convexity, optimality, duality, and sensitivity. Algorithms: gradient-type, Newton-type. This course emphasizes data-driven modeling, intuitions and numerical algorithms for optimization with real variables.
The field of optimization is concerned with the study of maximization and minimization of mathematical functions. Very often the arguments of (i.e., variables in) these functions are subject to side conditions or constraints. By virtue of its great utility in such diverse areas as applied science, engineering, economics, finance, healthcare, and statistics, optimization holds an important place in the practical world and the scientific world. Indeed, as far back as the Eighteenth Century, the famous Swiss mathematician and physicist Leonhard Euler (1707-1783) proclaimed that … nothing at all takes place in the Universe in which some rule of maximum or minimum does not appear. The subject is so pervasive that we even find some optimization terms in our everyday language. Optimization often goes by the name Mathematical Programming. The latter name tends to be used in conjunction with finite-dimensional optimization problems, which in fact are what we shall be studying here. The word “Programming” should not be confused with computer programming which in fact it antedates. As originally used, the term refers to the timing and magnitude of actions to be carried out so as to achieve a goal in the best possible way.
A diversified group of scholars had made contributions to this field, such as John von Neumann, George Dantzig, David Blackwell, …

FAQs
Q: What is the difference between MS&E 211 and MS&E 211X?
A: The latter course moves at a faster pace with a stronger math exposure.

Q: I am enrolled through SCPD, how do I login to view my course lectures?
A: Please log into your mystanfordconnection account at scpd.stanford.edu. For login issues please contact technical support, scpdsupport@stanford.edu.

Q: What is a remote student?
A: Remote students are students that are (i) non-SCPD and (ii) off campus for some (or all) of the quarter. For exams and midterm and final exam it is important that remote students organize a proctor ahead of time. More details on this will be released during the first week of class.

Other Courses in Optimization
The MS&E Department has several other courses in optimization and related topics. Those focusing primarily on optimization as such are:
MS&E 111 (=E62), 212, 310, 311, 312, 313.
Courses emphasizing applied settings in which optimization plays a major role are:
MS&E 251, 302, 322, 339, 334, 344, 351, 361.