CSCI5160 Approximation Algorithms — Spring 2017
This is the webpage for a previous offering of the course in 2017; latest offering available here
Siu On CHAN Office hours Tuesday 3-5pm SHB 911
Teaching assistant
- Yuzhe MA yzma@link
News
- Apr 19: Project report will be due May 12 at 23:59. Please submit your report electronically to siuon@cse.
- Apr 14: Homework 2 deadline is now postponed to Apr 23 (Sunday) 23:59.
- Apr 10: Homework 2 is now posted.
- Mar 4: Homework 1 is now posted.
- Jan 13: There will be make-up classes during the tutorial session time slots on Jan 25, Feb 8 and Feb 15
- Jan 13: Please sign up on Piazza to join our discussion forum
Information
- Lectures
- Wed 10:30-11:15pm (LSB G34) Thu 10:30-12:15pm (MMW 705)
- References
- [WS] The Design of Approximation Algorithms, David P. Williamson and David B. Shmoys (available online)
- [BV] Convex Optimization, Stephen Boyd and Lieven Vandenberghe (available online)
- [G] Foundations of Optimization, Osman Güler (accessible online at university library webpage)
- [GM] Approximation Algorithms and Semidefinite Programming, Bernd Gärtner and Jiri Matousek (accessible online at university library webpage)
- Forum
- Please sign up on Piazza
- Grading
- 50% homeworks, 50% project, 5% bonus scribe notes
Topics
This course will study the design and analysis approximation algorithms for various optimization problems. We will cover spectral algorithms, semidefinite programs, and related convex programs. Here are tentative topics:
- Semidefinite programs
- SDP duality
- Multiplicative weight update
- Graph spectrum
- Cheeger–Alon–Milman inequality
- Random walks
- Local graph partitioning
- Expanders
- Laplacian solver
- Effective resistance
- Sparsification
- Determinantal measures
- strong Rayleigh measures
- Random spanning trees
- Negative dependence
- Lasserre hierarchy
- Integrality gaps
- Extended formulations
- Hardness of approximation
- Parallel repetition
- Small-set expansion
Lectures
Date | Topic | Readings | Notes | |
---|---|---|---|---|
1 | Jan 11 | Semidefinite programs | [GM] Ch. 2 | Notes 1 |
2 | Jan 12 | Goemans–Williamson rounding for Max-Cut | [GM] Ch. 1 | Notes 1 & 2 |
Jan 18 | No classes (conference traveling) | |||
Jan 19 | No classes (conference traveling) | |||
3 | Jan 25 | Duality make-up class during tutorial |
[GM] Ch. 4 | Notes 3 |
4 | Jan 26 | SDP duality | [GM] Ch. 4 | |
Feb 1 | Lunar New Year Holiday | |||
Feb 2 | Lunar New Year Holiday | |||
5 | Feb 8 | Multiplicative weights and minimizing regrets make-up class during tutorial |
Arora–Hazan–Kale | |
6 | Feb 9 | Matrix multiplicative weights | Arora–Kale | |
7 | Feb 15 | Graph spectrum and Laplacian make-up class during tutorial |
Lau’s lecture | |
8 | Feb 16 | Cheeger–Alon–Milman inequality | Lau’s lecture | |
9 | Feb 22 | Spectral embedding | Spielman’s lecture | |
10 | Feb 23 | Random walks | Lau’s lecture | |
11 | Mar 1 | Random walks | ||
12 | Mar 2 | Expanders | Lau’s lecture | |
13 | Mar 8 | Local graph partitioning | Lau’s lecture | |
14 | Mar 9 | Local graph partitioning | ||
15 | Mar 15 | Small-set expanders and hypercontraction | O’Donnell §9.4 & Cor. 8 | |
16 | Mar 16 | Effective resistance | Spielman’s lectures 7 & 8 | |
17 | Mar 22 | Effective resistance | Lau’s lecture | |
18 | Mar 23 | Graph sparsification | Spielman’s lecture | |
19 | Mar 29 | Laplacian solver | Lau’s lecture Spielman’s lecture |
|
20 | Mar 30 | Random spanning trees | Oveis Gharan’s lecture | |
21 | Apr 5 | Random spanning trees | ||
22 | Apr 6 | Determinantal measures and negative dependence | Oveis Gharan’s lecture | |
23 | Apr 12 | Traveling salesperson | Oveis Gharan’s lecture | |
24 | Apr 13 | John ellipsoid | Lau’s lecture | |
25 | Apr 21 | John ellipsoid | ||
26 | Apr 22 | Largest simplex | Nikolov |
Homeworks
There will be two to three homeworks.
Collaboration on homework and consulting references is encouraged, but you must write up your own solutions in your own words, and list your collaborators and your references on the solution sheet.
Homework 1 due Thu Mar 16 in class
Homework 2 due Sun Apr 23 at 23:59 (updated to fix typos and a mistake in Q5(b) on Apr 17). Please submit to 9th floor assignment box in Ho Sin-Hang Engineering Building.
Project
The course project involves writing a short report (5-10 pages) related to approximation algorithms in an area of your interest. The report may be your exposition of a paper from the list below (or any similar paper), or a summary of your new follow-up results. You are encouraged include in your report any illustrative, concrete examples that help you understand the papers you read.- Fast Laplacian solver / max-flow
- A Framework for Analyzing Resparsification Algorithms, Rasmus Kyng, Jakub Pachocki, Richard Peng, Sushant Sachdeva
- Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs, Michael B. Cohen, Jonathan Kelner, John Peebles, Richard Peng, Anup Rao, Aaron Sidford, Adrian Vladu
- The Expected Norm of a Sum of Independent Random Matrices: An Elementary Approach, Joel A. Tropp
- Approximate Undirected Maximum Flows in O(m polylog(n)) Time, Richard Peng
- Computing Maximum Flow with Augmenting Electrical Flows, Aleksander Mądry
- Sparsification / expanders
- Twice-Ramanujan Sparsifiers, Joshua Batson, Daniel A. Spielman, Nikhil Srivastava
- Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates, Zeyuan Allen-Zhu, Zhenyu Liao, Lorenzo Orecchia
- Lifts, Discrepancy and Nearly Optimal Spectral Gap, Yonatan Bilu, Nathan Linial
- Ramanujan Graphs in Polynomial Time, Michael B. Cohen
- Graph partitioning
- Local Graph Partitioning using PageRank Vectors, Reid Andersen, Fan Chung, Kevin Lang
- Improved Cheeger’s Inequality: Analysis of Spectral Partitioning Algorithms through Higher Order Spectral Gap, Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee, Shayan Oveis Gharan, Luca Trevisan
- Local Flow Partitioning for Faster Edge Connectivity, Monika Henzinger, Satish Rao, Di Wang
- Minimizing discrepancy
- Deterministic Discrepancy Minimization via the Multiplicative Weight Update Method, Avi Levy, Harishchandra Ramadas, Thomas Rothvoss
- Algorithmic Discrepancy Beyond Partial Coloring, Nikhil Bansal, Shashwat Garg
- A Logarithmic Additive Integrality Gap for Bin Packing, Rebecca Hoberg, Thomas Rothvoss
- Semidefinite programs and hierarchies
- Lecture Notes on the ARV Algorithm for Sparsest Cut, Thomas Rothvoss
- On the Lovász Theta function for Independent Sets in Sparse Graphs, Nikhil Bansal, Anupam Gupta, Guru Guruganesh
- How to refute a random CSP, Sarah R. Allen, Ryan O’Donnell, David Witmer
- Sum of squares lower bounds for refuting any CSP, Pravesh K. Kothari, Ryuhei Mori, Ryan O’Donnell, David Witmer
- Machine learning / sparse approximation
- Optimal Column-Based Low-Rank Matrix Reconstruction, Venkatesan Guruswami, Ali Kemal Sinop
- A Matrix Factorization Approach for Learning Semidefinite-Representable Regularizers, Yong Sheng Soh, Venkat Chandrasekaran
- Noisy Tensor Completion via the Sum-of-Squares Hierarchy, Boaz Barak, Ankur Moitra
- Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization, Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright
- Online algorithms
- A Primal-Dual Randomized Algorithm for Weighted Paging, Nikhil Bansal, Niv Buchbinder , Joseph (Seffi) Naor
- Online Algorithms for Covering and Packing Problems with Convex Objectives, Yossi Azar, Niv Buchbinder, T-H. Hubert Chan, Shahar Chen, Ilan Reuven Cohen, Anupam Gupta, Zhiyi Huang, Ning Kang, Viswanath Nagarajan, Joseph (Seffi) Naor, Debmalya Panigrahi
- Hypercontractive inequality
- Robust Optimality of Gaussian Noise Stability, Elchanan Mossel, Joe Neeman
- Hypercontractive inequalities via SOS, and the Frankl–Rödl graph, Manuel Kauers, Ryan O’Donnell, Li-Yang Tan, Yuan Zhou