Lecture Videos:
This is a distance-learning course. The lecture videos are available in the course in eCampus, under "Course Materials".
Instructor:
Prof. Anxiao (Andrew) Jiang, 309B Bright Building. Email: ajiang@cse.tamu.edu
TA and Grader:
TA: Pulakesh Upadhyaya. Email: pulakesh@tamu.edu
Grader: Muhammad Emad-ud-din. Email: emaad22@tamu.edu
Venkata Sameer Kumar Betana Bhotla. Email: vbetan3@tamu.edu
Office Hours:
Time of office hour: 8:00-9:00pm Central Time (Texan time) Monday through Friday.
Venue of office hour: the office hour is over Zoom.
Meeting link: https://tamu.zoom.us/j/3460352058?pwd=MFhJcTdTRURHZGFGY1FVVzJDWXlOdz09
Meeting ID: 346 035 2058
Password: CSCE411
Course Materials:
Textbook: Introduction to Algorithms (3rd Edition), by Thomas Cormen, Charles Leiserson, Ronald Rivest and Clifford Stein.
Grading and Requirements:
Homework: 65%.
Project: 35%.
Grading: 90 to 100 for A; 80 to 89 for B; 70 to 79 for C; 60 to 69 for D; 0 to 59 for F.
Homework/Project Policy: An electronic copy should be turned in in eCampus. No late homework or project will be accepted.
Homework:
1. Homework one. Due: 10pm (Central Time) on Monday 7/6 in eCampus.
(1) Textbook page 370, Exercise 15.1-2. (2) Textbook page 370, Exercise 15.1-3. (3) Textbook page 378, Exercise 15.2-1. (4) Textbook page 378, Exercise 15.2-6.
2. Homework two. Due: 10pm (Central Time) on Friday 7/10 in eCampus.
(1) Textbook page 422, Exercise 16.1-3. (2) Textbook page 436, Exercise 16.3-3. (3) Textbook page 446, Problem 16-1 (a).
3. Homework three. Due: 10pm (Central Time) on Tuesday 7/14 in eCampus.
(1) Textbook page 602, Exercise 22.2-7. (2) Textbook page 614, Exercise 22.4-2. (3) Textbook page 623, Problem 22-3. (4) Textbook page 630, Exercise 23.1-9.
4. Homework four. Due: 10pm (Central Time) on Sunday 7/19 in eCampus.
(1) Textbook page 679, Problem 24-3. (2) Textbook page 858, Exercise 29.1-5. (3) Textbook page 878, Exercise 29.3-5. (4) Textbook page 879, Exercise 29.3-6.
5. Homework five. Due: 10pm (Central Time) on Friday 7/24 in eCampus.
(1) Textbook page 885, Exercise 29.4-1. (2) Textbook page 893, Exercise 29.5-5. (3) Textbook page 893, Exercise 29.5-6.
6. Homework six. Due: 10pm (Central Time) on Wednesday 7/29 in eCampus.
(1) Textbook page 1066, Exercise 34.2-10. (2) Textbook page 1100, Exercise 34.5-1. (3) Textbook page 1101, Problem 34-1 (a), (b).
7. Homework seven. Due: 10pm (Central Time) on Sunday 8/2 in eCampus.
(1) Textbook page 1117, Exercise 35.2-3. (2) Textbook page 1127, Exercise 35.4-3. (3) Textbook page 1128, Exercise 35.4-4. (4) Textbook page 730, Exercise 26.2-6.
Project:
The project file is accessible in eCampus, under the name "Project File" in "Course Materials". It has 35 exercises in total, covering the various topics that we are learning. Each problem is worth 10 points. They together account for 35% of the final grade. (So each exercise accounts for 1% of the final grade.)
The exercises are not all easy or all hard. Do your best to answer them, and use them as opportunities to refresh what we learned in class.
There are two opportunties to submit your solutions to the project exercises:
Submission 1: Due 10pm (Central Time) on Tuesday 7/21 in eCampus.
In this submission, please submit all the exercises you have already solved. (For exercises that you have not solved yet, there is no need to submit them here.) As encouragement, you will get 20% extra points for the solutions submitted here. For example, if you submit solutions to 18 exercises and receive 160 points out of their 180 points, you will receive 160 x 1.2 = 192 points here.
However, for every project exercise, there is only one opportunity to submit its solution: either here in Submission 1, or at the end of the semester in Submission 2. So if you submit your solution to an exercise here, you will not be allowed to submit your solution to the same exercise again at the end of the semester in Submission 2 (even if you have updated your solution by then). Therefore, submit your solution to an exercise here only if you have sufficient confidence in its correctness!
Submission 2: Due 10pm (Central Time) on Monday 8/3 in Campus.
This is the final chance to submit your project. So please submit the solutions to all those exercises that you did not cover in Submission 1.
Syllabus: as posted in Howdy.
Lecture schedule:
6/30/2019 Tuesday: Dynamic programming. (Watch video of Lecture 1. Read Chapter 15 of textbook.)
7/1/2019 Wednesday: Dynamic programming. (Watch video of Lecture 2. Read Chapter 15 of textbook.)
7/2/2019 Thursday: Greedy algorithms. (Watch video of Lecture 3. Read Chapter 16 of textbook.)
7/3/2019 Friday: Greedy algorithms. (Watch video of Lecture 4. Read Chapter 16 of textbook.)
7/6/2019 Monday: Greedy algorithms. (Watch video of Lecture 4. Read Chapter 16 of textbook.)
7/7/2019 Tuesday: Amortized analysis. (Watch video of Lecture 5. Read Chapter 17 of textbook.)
7/8/2019 Wednesday: Elementary graph algorithms. (Watch video of Lecture 6, Lecture 7. Read Chapter 22 of textbook. )
7/9/2019 Thursday: Elementary graph algorithms. Minimum Spanning Tree. (Watch video of Lecture 8, Lecture 9. Read Chapters 22, 23 of textbook.)
7/10/2019 Friday: Single-Source Shortest Paths. (Watch video of Lecture 10. Read Chapter 24 of textbook.)
7/13/2019 Monday: Single-Source Shortest Paths. (Watch video of Lecture 10. Preview the chapter "Linear Programming" yourself. Read Chapters 24, 29 of textbook.)
7/14/2019 Tuesday: Linear Programming. (Watch video of Lecture 11. Read Chapter 29 of textbook.)
7/15/2019 Wednesday: Linear Programming. (watch video of Lecture 12. Read Chapter 29 of textbook.)
7/16/2019 Thursday: Linear Programming. (Watch video of Lecture 13. Read Chapter 29 of textbook.)
7/17/2019 Friday: Linear Programming. (Watch video of Lecture 14. Read Chapter 29 of textbook.)
7/20/2019 Monday: NP-completeness. (Watch video of Lecture 15. Read Chapter 34 of textbook.)
7/21/2019 Tuesday: NP-completeness. (Watch video of Lecture 16. Read Chapter 34 of textbook.)
7/22/2019 Wednesday: NP-completeness. (Watch video of Lecture 17. Read Chapter 34 of textbook.)
7/23/2019 Thursday: NP-completeness. (Watch video of Lecture 18. Read Chapter 34 of textbook.)
7/24/2019 Friday: Approximation Algorithms. (Watch video of Lecture 19. Read Chapter 35 of textbook.)
7/27/2019 Monday: Approximation Algorithms. (Watch video of Lecture 20. Read Chapter 35 of textbook.)
7/28/2019 Tuesday: Maximum Flow. (Watch video of Lecture 21. Read Chapter 26 of textbook.)
7/29/2019 Wednesday: Maximum Flow. (Watch video of Lecture 22. Read Chapter 26 of textbook.)
7/30/2019 Thursday: Maximum Flow. (Watch video of Lecture 22. Read Chapter 26 of textbook.)
7/31/2018 Friday: Review all lecture videos. Complete project.
8/3/2019 Monday: Review all lecture videos. Complete project.
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