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CSC 5160 - Topics in Algorithms: Combinatorial Optimization and Approximation Algorithms

CSC 5160 - Topics in Algorithms: Combinatorial Optimization and Approximation Algorithms. Lecture 1: Jan 10. Basic Information. Course homepage : http://www.cse.cuhk.edu.hk/~chi/csc5160/ Blog : http://csc5160.blogspot.com/ Instructor : Lau, Lap Chi ( 劉立志 ) Office hour : by appointment

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CSC 5160 - Topics in Algorithms: Combinatorial Optimization and Approximation Algorithms

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  1. CSC 5160 - Topics in Algorithms:Combinatorial Optimization andApproximation Algorithms Lecture 1: Jan 10

  2. Basic Information • Course homepage: http://www.cse.cuhk.edu.hk/~chi/csc5160/ • Blog: http://csc5160.blogspot.com/ • Instructor: Lau, Lap Chi (劉立志) • Office hour: by appointment • Lectures: H6 (ERB 703), F2-3 (ERB 703) • Tutor: Le Jilin, Jerry • Tutorial: H5

  3. Course Material • No textbook. • See course information page. Extra lecture H5 (ERB 803).

  4. Outcome • Distinguish polynomial time solvable problems and NP-complete problems. • Learn the basic of linear programming (e.g. duality), and integer programming. • Learn different techniques to design heuristics that are provably “good”. • Use LP and SDP to design approximation algorithms.

  5. Course Requirement • 3 Homework, 37.5% • Notes taking, 12.5% • Project, 50%

  6. Homework • See last year homeworks. • 3 out of 8. • Encourage discussion, can use references, but write your own solutions. Bonus questions!

  7. Notes Taking • Each student takes notes for one lecture. • Use latex to typeset it. See examples. • Due next Monday. • Discussion after class, provide references.

  8. Project • For your research, algorithmic topic relevant to your area. • See course project page. • 1-2 students a group.

  9. Project Requirement • Read 3 papers, write a report, and a 15-mins presentation. • Meet 3 times during the semester to discuss the progress. • Choose a topic (Feb 14-15), discussion (Feb 18-22) • Outline (Mar 13-14), discussion (Mar 17-20) • Presentation (Apr 24-25), discussion (Apr 28-30) • Report (early May)

  10. Project Ideas • Belief propagation and its applications • Graph minor theory and its applications • Computational limitations on unsupervised learning • Graph partitioning problems and automatic news story segmentation • Pricing selfish users in multicommodity networks • PC-tree and its applications • Relevant ranking on multi-class data • Spectral graph theory and its applications • Graph labeling and image processing • Spectral clustering and semi-supervised learning • Optimizing in non-cooperative environment via duality of LP • Approximation algorithms for facility location problems • Nearest neighbour search

  11. Project Ideas • Surface simplification in computer graphics • Network coding • Fixed parameter algorithms and approximation algorithms • Pattern matching algorithms • Delaunay triangulation and its application • Approximate string matching • Approximating max-k-cut using semidefinite programming • Semidefinite programming and machine learning • Winner determination problem in combinatorial auctions • Decentralized search algorithms in small world graphs • Searching techniques in peer-to-peer networks • Sparsest cut and its applications • The Multiplicative Weights Update Method and Its Applications

  12. Course Requirement • 3 Homework, 37.5% • Notes taking, 12.5% • Project, 50% workload, grades…

  13. Blog • Discuss lectures • Discuss homework • Discuss course notes

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