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Design and Analysis of Algorithms

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Design and Analysis of Algorithms Lecture 1, Jan. 14th 2011 Maria-Florina (Nina) Balcan Introductions and Admin Introductions Course web page: http://www.cc.gatech ... – PowerPoint PPT presentation

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Title: Design and Analysis of Algorithms


1
Design and Analysis of Algorithms
Lecture 1, Jan. 14th 2011
Maria-Florina (Nina) Balcan
2
Introductions and Admin
3
Introductions
  • Meeting Time Mon, Wed, Fri, 1100 1200
  • Course Staff
  • Instructor Maria Florina (Nina) Balcan
  • Office hours Mon and Wed, 1200 100, Klaus
    2144.
  • TA Jacob Robertson
  • Office hours Fri, 1200 100.
  • Grader Nishant Kothari
  • Office hours TBA
  • Course web page

http//www.cc.gatech.edu/ninamf/Algos11/
4
Additional Resources
  • Sections on Tue/Wed for all Algos courses.
  • Starting next week

Location and Time TBA
  • Tas Jacob Robertson, Chris Bowen, Michael Qin

5
Prerequisites
  • Constructing Proofs CS 1050 (Min Grade of C)
  • Intro-Object Orient Prog CS 1331 (Min Grade of C)
  • Applied Combinatorics MATH 3012 (Min Grade of D)

6
References
  • Official Text Book CLRS
  • Introduction to Algorithms
  • Cormen, Leiserson, Rivest, Stein
  • Other Useful Resources
  • Algorithm Design
  • Kleinberg, Tardos
  • Algorithms
  • Dasgupta, Papadimitriou, Vazirani

7
Lectures in general
  • On the board
  • Ocasionally, will use slides

8
Goals of the Course
Course is about the design and analysis of algos
  • How to design correct, efficient algos, and how
    to think clearly about analyzing correctness and
    running time.

What is an algorithm?
  • A method for solving a computational problem
    (e.g. sorting or shortest paths).

Main goal provide intellectual tools for
designing and analyzing your own algorithms for
problems you need to solve in the future.
9
Why care about algorithms
10
Why care about algorithms
Decide which emails are spam and which are
important.
Supervised classification
Not spam
spam
Goal use emails seen so far to produce good
prediction rule for future data.
11
Why care about algorithms
Google searches
12
Structure of the Class
  • Divide and conquer algorithms Randomized
    algorithms
  • Examples, recurrences, the master theorem,
    probabilistic analysis, Quicksort, Median
    selection
  • Graph Algorithms
  • DFS, topological sorting, strongly connected
    components, BFS, Shortest paths and Dijkstra's
    Algorithm, Minimum spanning trees, Min-Heaps,
    Union Find
  • Greedy Algorithms, Dynamic Programming.
  • Longest common subsequence, Knapsack, The
    Bellman Ford Algorithm, All pairs shortest paths
    Floyd-Warshall.
  • NP-completeness, Reductions. Approximation
    Algorithms

13
Structure of the Class
  • Divide and conquer algorithms Randomized
    algorithms

Exam 1 February 11
  • Graph Algorithms

Exam 2 March 9th
  • Greedy Algorithms, Dynamic Programming.

Exam 3 April 8th
  • NP-completeness, Reductions. Approximation
    Algorithms

Exam 4 April 22nd
14
Grading Scheme
  • 4 Exams 50
  • Final 25
  • Homeworks 25
  • Class participation to adjust borderline scores
  • Exercises/problems (pencil-and-paper
    problem-solving variety).

15
Homework
  • 8-9 weekly homeworks
  • Collaboration generally allowed
  • Work in groups of size at most 3
  • Write up your own solutions
  • Acknowledge your collaborators
  • Breaking these rules will be considered as
    cheating
  • NO late submission
  • Lowest two (2) homework scores will be dropped

16
Important to Attend the Class
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