Announcements   Calendar   Course Schedule and Lectures(2024)  Grades  Group Presentation Homework  Labs  Past Exam  Previous Materials  Statistics 

Course Objective

    Linear programming is the fundamental form of the mathematical programming methodology. Central to the mathematical programming is the mathematical derivation, by which the properties and the algorithms of a mathematical optimization model were developed.

The course aims

• To understand the role of linear programming in engineering design and its importance.

• To introduce the significance of simplex theory and the concept of duality, decomposition etc.  in a linear programming problem.

• To improve the capability of logical reasoning and implementation of mathematical methods in a real problem.

Students Learning Outcomes (SLO)

Student having

• Improved reasoning skills and developing linear programming models to address social/economic/engineering problems.

• Improved knowledge on the practical application of linear programming.

• Improved knowledge on using techniques like simplex method, duality, decomposition etc. to solve problems.

• Improved ability to use mathematical methods in social/economics/ engineering problems.

• Improved decision-making skills, through optimizing objectives on an environment, having constrained resources.

Time and Venue

• Mondays 13:20-15:10  Wednesdays 13:20-14:10

               Classroom:

               Engineering Builidng I Room 904 (new classroom)

Webex Link for remote access (only valid upon request)

https://nationaltsinghuauniversity-jes.my.webex.com/nationaltsinghuauniversity-jes.my-en/j.php?MTID=mdfbc3e7e71b6e86f2cd5d5f45803634d

• You need to download Webex before being able to access

使用視訊系統加入

               25593433087@webex.com

使用會議號加入 

2559 343 3087

Textbook

• Introduction to Linear Optimization,

Dimitris Bertsimas and John N. Tsitsiklis, Athena Scientific, 1997

• Linear Programming and Network Flows,

Mokhtar S. Bazaraa, John J. Jarvis, Hanif D. Sherali, Wiley, 4th Edition, 2009

Reference

• Large Scale Linear and Integer Optimization: A Unified Approach

Richard Kipp Martin, Springer, 1999

• Selected journal papers

Grading and Exams

• 4 Homework  15%

• 4 Exams 48%

• 4 Labs 24%

• Group Presentation - reading assignment 13%

A+: 90 and above 

A : 85-89  (round to the nearest integer) 

A- : 80-84 

B+: 77-79

B : 73-76 

B- : 70-72 (a pass for grad students) 

C+: 67-69 

C : 63-66

C- : 60-62 (a pass for undergrads) 

D: 50-59 

F : 1~49 

X: 0

Attendance Policy 

• No more than 3 times of absences. You will not have the chance to have the final-score adjustment at the end of the semester after 4 absences. (Official leaves, sick leaves, etc., require the official and doctor's proofs.)  

    Homework/Project Policy 

• No plagiarism (zero tolerance with more than three identical sentences). You will receive 0 once found.

  Study group Policy

• To enhance learning efficiency and effectivity, each students must enroll in a study group. Please discuss as a group before each  homework due for at least 1 hour and take a photo. Attach the group photo in your homework submission and indicate the start and end time of the discussion. The discussion can be held online. The photo of an online study group discussion can be the screenshot of everyone's face. Please form a group with 3 people.

Faculty

 Teaching Assistants