CSE 565: Algorithm Design and Analysis
Fall 2010

General Information

Course Announcement

Instructor: Adam Smith

Office hours: Wednesdays, 3pm-5pm, IST 338K

Syllabus, Lecture Notes, Reading

DateSyllabusReadingHandouts/Homework
Mon, Aug 23 Introduction, stable matching (slides) KT, Chap. 1 Course Announcement
Wed, Aug 25 Stable matching analysis, asymptotic growth (slides) KT, Chap. 2
Fri, Aug 27 Asymptotic growth, basic data structures (slides) KT, Chap. 2 Homework 1 out (pdf, tex)
Mon, Aug 30Graphs (slides) KT, Chap. 3
Wed, Sep 1 No lecture.
Fri, Sep 3 Greedy algorithms: interval scheduling (slides) KT, Chap. 4.1
Mon, Sep. 6Labor day -- no lecture.
Wed, Sep 8 Greedy algorithms: interval partitioning (slides) KT, Chap. 4.2 Homework 2 out (pdf, tex)
Fri, Sep 10 Greedy graph algorithms: topological sort, Dijkstra (slides) KT, Chap. 4
Mon, Sep. 13 Greedy graph algorithms: implementing Dijsktra, MST (slides) KT, Chap. 4 Homework 3 out (pdf, tex)
Wed, Sep. 15More on MST. Huffman codes. (slides) KT, Chap. 4, Erickson greedy notes
Fri, Sep. 17Divide and Conquer, recurrences review (slides) KT, Chap. 5.1, 5.2Homework 4 out (pdf, tex)
Mon, Sep. 20Divide and Conquer: master method, Strassen (see slides from lecture 10) KT, Chap. 5
Wed, Sep. 22Backtracking: independent set, knapsack, linear recurrences Erickson notes 2, B, Recurrences
Fri, Sep. 24Dynamic programming: Fibonacci, Weighted Interval Cover, fire alarm! (slides) KT, Chap. 6.1, 6.2
Mon, Sep. 27Dynamic programming: knapsack, grid graphs (slides) KT, Chap. 6.4
Wed, Sep. 29Dynamic programming: segmented least squares, homework problem dscussion (slides) KT, Chap. 6.4
Thu, Sep. 30Midterm 1, 08:15-10:15pm, Willard 75
Fri, Oct. 1Dynamic programming: least common subsequence in linear space (slides) KT, Chap. 6.4Homework 5 out (pdf, tex)
Mon, Oct. 4Dynamic programming: Bellman-Ford (slides) KT, Chap. 6 Homework 6 (pdf, tex)
Wed, Oct. 6Bellman-Ford review, Flows and Cuts (slides) KT, Chap. 7
Fri, Oct. 8Flows and Cuts, part II (slides linked above) KT, Chap. 7
Mon, Oct. 11Maximum Bipartite Matching (slides) KT, Chap. 6
Wed, Oct. 13Faster algorithms: capacity scaling (slides) KT, Chap. 7
Fri, Oct. 15Applications of maxmimum flow (slides) KT, Chap. 7
Mon, Oct. 18Min-weight perfect matching (summary) KT, Chap. 7. See also Erickson "Extensions to max flow"
Wed, Oct. 20Linear programming: definitions, examples Erickson "Linear programming"
Fri, Oct. 22Linear programming: duality Erickson "Linear programming"
Oct. 25-29 No class. Homework 7 (pdf, tex)
Mon, Nov. 1Undecidability and hardness, part 1 (slides for whole week)
Wed, Nov. 3Undecidability and hardness, part 2 (slides for whole week)
Fri, Nov. 5NP and reductions among NP problems (slides for whole week) Homework 8 (pdf, tex)
Mon, Nov. 8No class.
Wed, Nov. 10Review for midterm
Thu, Nov. 11Midterm 2, 08:15-10:15pm, 165 Willard bldg.
Fri, Nov. 12NP-completeness, part 1 (slides)
Mon, Nov. 15NP-completness, part 2 (slides above)
Wed, Nov. 17Example reductions: 3D-matching (slides)
Fri, Nov. 19More reductions (no slides)
Mon, Nov. 29Beyond NP-completness: Exact Algorithms (slides for whole week)
Wed, Dec. 1Beyond NP-completness: Approximation Algorithms Part 1
Fri, Dec. 3Beyond NP-completness: Approximation Algorithms Part 2
Mon, Dec. 6Randomized Algorithms Part 1 (slides)
Wed, Dec. 1Randomized Algorithms Part 2 (slides)
Fri, Dec. 3Randomized Algorithms Part 3 (slides above)
Monday, Dec. 13Final exam, 08:00A - 09:50A, 322 HHD EAST

The development of these course materials was partly supported by the National Science Foundation under award 0729171.

Adam Smith
Last modified: Wed Sep 15 15:00:10 EDT 2010