Instructor: Barna Saha
Office Hour: After class and by appointment.
The area of approximation algorithms was traditionally designed to come up with polynomial time algorithms for NP-Hard problems by allowing returning approximate solution. With the advent of Big Data area, the need for approximation algorithms goes far beyond of tackling NP Hard problems. We may need approximation algorithms even for polynomial time problems to allow for faster running time, improved space complexity and changing data. This course will cover some of the new emergent areas of approximation algorithms. The course topics are subject to change with each offering.
Following is a list of tentative topics for this semester.
- Traditional Theory of Approximation Algorithms
- Fine-Grained Approximation Algorithms
- Dynamic/Online Algorithms
- Conditional Hardness & Hardness of Approximation
Time & Venue: The course will meet every Monday and Wednesday from 9:00 am to 10:00 am at Dwinelle 109.
Grading: Each student will be required to present one/two papers and do a research project individually or in a group of two. Class presentations account for 30% of the grade and the project accounts for 70%. Project will have several components: (i) project proposal (less than 1 pg) due on Sept 18, (ii) intermediate progress report (less than 2 pgs) due on Oct 23, (iii) in-class presentation during the last two weeks of the class, and (iv) a final project report (less than 10 pgs) due on Dec 2.
Books: There is no required textbook. Following will be used for reference in the first part of the course.
- The Design of Approximation Algorithms, David Williamson & David Shmoys.
- Approximation Algorithms, Vijay Vazirani.
Prerequisite: Students must have taken one graduate level courses on algorithms or combinatorial optimization such as IND ENG 269, IND ENG 266, IND ENG 262A, CS 270 and CS 271. If you do not satisfy the prerequisite but want to take the course, please talk to the instructor.
Goal: The goal of the course is to make students enough interested in the course topics to do further exploration.
|Aug 28||Overview of the course. |
The whats and whys of approximation algorithms.
Brief introduction to linear programming and LP duality.
|Chapter 1 of Williamson-Shmoys, Chapter 12 of Vazirani.|
|Sept 4||An introduction to the techniques through the set cover problem: |
deterministic rounding, primal-dual, randomized rounding.
|Chapter 1 of Williamson-Shmoys.|
|Sept 9||An introduction to the techniques through the set cover problem: |
greedy algorithm, dual fitting.
|Chapter 1 of Williamson-Shmoys, Chapter 13 of Vazirani.|
|Sept 11||The uncapacitated facility location problem: randomized rounding.||Chapter 5.8 of Williamson-Shmoys.|
|Sept 16||The uncapacitated facility location: primal-dual method.||Chapter 7.6 of Williamson-Shmoys|
|Sept 18||Introduction to semi-definite programming. Randomized rounding of semi-definite programming.||Chapter 6 of Williamson-Shmoys|
|Sept 23||Student presentation on choice papers under traditional approximation algorithms|
|Sept 25||Student presentation on choice papers under traditional approximation algorithms|
|Sept 30||Techniques in proving hardness of approximation: a brief introduction.||Chapter 10 of Williamson-Shmoys.|
|Oct 2||A fine-grained approach to hardness: ETH, SETH, OV. SETH to OV, OV to 2-3 Diameter.||Reference: https://people.csail.mit.edu/virgi/6.s078/|
|Oct 7||A fine-grained approach to hardness: More on SETH and ETH.||Reference: https://people.csail.mit.edu/virgi/6.s078/|
|Oct 9||OV to Sequence Alignment Problems: Longest Common Subsequence, Edit Distance.||References: https://arxiv.org/abs/1412.0348|
|Oct 14|| Amnesic Dynamic Programming: Longest Increasing Subsequence |
& RNA Folding.
|Oct 16||——–Buffer ———|
|Oct 21||Instructor travel, no class|
|Oct 23||Edit Distance Approximation|| References: |
APSP, (min,+) matrix multiplication and Equivalences
|Oct 30||Fast Algorithm for APSP in non-uniform model.||References:|
|Nov 4||Fast Approximation Algorithms for APSP.||References:|
|Nov 6||Fast Exact Algorithm for (min,+) matrix multiplication on Lipschitz matrices.||References:|
|Nov 13||Student Presentation of choice papers under fine-grained complexity|
|Nov 18||Student Presentation of choice papers under fine-grained complexity|
|Nov 20||Dynamic Problems & Hardness||Reference:|
|Nov 25||Dynamic Algorithms: design of dynamic algorithms via primal-dual||Reference:|
|Dec 2||Project Presentation|
|Dec 4||Project Presentation|