Transcript Introduction

ALGORITHMS
Lecturer: Daniela Zaharie
• Room: 047 (ground floor)
• http: //www.info.uvt.ro/~dzaharie
• e-mail: [email protected]
• Schedule:
Lecture: every Monday at 13:00 (room 045C)
Seminar:Tuesday at 9:40, room 102
(Flavia Micota, [email protected])
Lab:
(Flavia Micota, [email protected])
• Subgroup 1: Monday, odd weeks, 9:40h, room 031
• Subgroup 2: Tuesday, odd weeks, 8:00h, room F45
• Subgroup 3: Tuesday, even weeks, 8:00h, room F45
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What is this course about ?
• In our daily activity we :
– Use a search engine (e.g. Google)
– An e-mail application which (hopefully) has a anti-spam filter
– Find news about friends using a social network tool (e.g.
Facebook)
• What is behind these tools?
– Algorithms for searching, keywords matching, sorting,
frequency computation, correlations identification etc.
Examples: PageRank algorithm (Google), EdgeRank algorithm
(Facebook)
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What is this course about ?
PageRank – algorithm used by the
Google search engine to rank the web
pages [Larry Page, 1997]
Basic idea of ranking:
rank(P0)=(1-d)+d*(rank(P1)+…rank(Pk ))
P0 – current page
P1,…, Pk – pages which contain links
toward P0
d in (0,1) – damping factor (models the
influence of time)
Web = graph
Ranking criteria = probabilistic
scores
Rank computing = iterative
algorithm or algebraic
compution (solving as linear
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What is this course about ?
EdgeRank – algorithm used in Facebook
news feed (selection of news to be
posted on the wall of a user)
Basic idea:
• The interaction between a user and a
facebook “object” (e.g. info, comment
http://techcrunch.com/2010/04/22/
etc) defines an edge
facebook-edgerank/
• Each edge is characterized by 3 factors http://www.youtube.com/watch?v=kI4
which influence the importance of each
YIYInou0
edge: affinity (between the user and
Some of the research topics at
object creator), weight, age.
Facebook :
• As a edge is more important the
- “Algorithmic game theory”
probability to be included in News is
- “Algorithms around social networks”
higher.
Algorithms – Lecture- 1 “Search algorithms”
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What is this course about ?
• This course is about:
– designing and analyzing algorithms
– abstract thinking and solving problems
• This course is NOT:
– a programming course
(however the algorithms we design and analyze will be
implemented in a programming language during the labs;
the language which we shall use is Python
http://www.python.org)
– a math course
(however we shall use some basic math stuff: concepts as
set, function, relation; some combinatorics; some
mathematical logic, proof techniques like mathematical
induction or proof
by contradiction)
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Why such a course could be
useful for you?
• You want to become a computer scientist
• Then you should know that:
at the heart of every programming task is the
– selection,
All these need a good
– adaptation,
understanding of
– discovery
algorithms
of algorithms.
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Why such a course could be
useful for you?
A computer scientist must be prepared for tasks like:
” … This is the problem. Solve it ...”
In such a situation it does not suffice to know
how to code a given algorithm
You must be able to find an adequate algorithm or even
develop a new one to solve the problem
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Why such a course could be
useful for you?
The future belongs to the computer scientists who have:
• Content: An up to date grasp of fundamental problems and
solutions
• Method: Principles and techniques to solve the vast array of
unfamiliar problems that arise in a rapidly changing field
[Jeff Edmonds, York University, Canada]
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Syllabus
Fourteen lectures on:
1.
2.
3.
4.
5.
6.
Introduction to algorithmic problem solving
Description of algorithms
Verification of algorithm correctness
Analysis of algorithm efficiency
Sorting and searching
Basic techniques in algorithm design:
a) divide and conquer, decrease and conquer
b) greedy
c) dynamic programming
d) backtracking, branch and bound
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Course materials
Web page: http://www.info.uvt.ro/~dzaharie
Algorithmics - english
There you will find some downloadable materials:
- lectures files (PDF)
- lecture slides (PPT or PDF)
- exercises for seminar/lab (PDF)
If you find typos or other errors please let me know !
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Course materials
The course materials are based mainly on:
1. T.H. Cormen, C.E. Leiserson, R.R. Rivest - Introduction to
algorithms, MIT Press 1990
2. R. Johnsonbaugh, M. Schaefer - Algorithms, Pearson Education,
2004
3. A. Levitin - The design & analysis of the algorithms, 2003
and course slides of
Jeff Edmonds (York University, Canada),
David Luebke (Virginia University, USA),
Steven Rudich (Carnegie Mellon University, USA) ...
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Grading policy
The final grade is between 1 and 10 and is based on:
Midterm written test - 20%
(during the 4th seminar – weeks 7-8)
Lab practical test – 20%
(during the 5th lab – weeks 9-10)
Homework & seminar/lab/course activity - 15%
Final written and practical exam - 45%
(during the winter exam session)
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Some rules
• The homework should be finalized by the next seminar/lab; late
homework is penalized with 0.2 points/week
•
More than 2 absences at seminar/lab lead to exam failing
• Collaboration is permitted on a conceptual level only; you can
discuss with your colleagues, but written solutions must always be
the result of an individual effort. Plagiarism of homework or written
test is punished by not considering that homework/ test
contribution to the final grade or even worse ...
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