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Εικόνα επιλογής

Open Algorithms

(TP324) -  A. MALAMOS

Περιγραφή Μαθήματος

A. COURSE OUTLINE

 

1. Course Title / Type

 Open Algorithms

ü   Mandatory                  ❏ Selective

 

4. Subject Area

❏ Information Systems

❏ Networks

❏ Computer Science

❏ Multimedia

❏ Intelligent Systems 

 

2. School / Program

STEF / Postgraduate ‘Informatics & Multimedia’

 

3. Author contact details

1) Name: A.G. Malamos

2) Title/Position: Assoc. Professor

3) Phone: 2810 379884

4) E-mail: amalamos@epp.teicrete.gr

 

Brief Course Description

 

The course commands a central role in computer science it both theoretical and practical levels, in many cases covering themes beyond the subject of informatics. The course strives for students to understand fundamental ways or data organization in computer memory and to learn and implement techniques for the handling of that data. Special attention is placed on creating new data algorithms with an obvious result on the dexterities of students to handle such problems.

 

B.  COURSE CONTENT

Week 1 & 2:

Detailed description

Review on Data Structures and Applications

Expected outcomes

Student would be able to:

1.Understand terminology and infrastructure on data Structures

2. Work with Data Structures and the corresponding computational techniques

Tutorial exercise

Assignments

Learning materials

Th.H. Cormen, C.E. Leiserson, R.L. Rivest, C. Stein, “Introduction to Algorithms” 3rd edition, MIT Press

 

Week 3 & 4:

Detailed description

Graphs

Search in graphs, Paths in graphs, Distances, Lengths on edges, Dijkstra's algorithm, Priority queue implementations, Shortest path.

 

Expected outcomes

Student would be able to:

1.Understand issues on arithmetic calculations and  fundamental terms in Graphs

2. Work with Graphs

Tutorial exercise

Assignments

Learning materials

Dasgupta, C. H. Papadimitriou, and U. V. Vazirani, “Algorithms”, MC Graw Hill Higher Education Edition

 

Week 5 & 6:

Detailed description

Greedy algorithms

Minimum spanning trees, Huffman encoding

Expected outcomes

Student would be able to identify and design greedy algorithms

Tutorial exercise

Assignments

Learning materials

Dasgupta, C. H. Papadimitriou, and U. V. Vazirani, “Algorithms”, MC Graw Hill Higher Education Edition

 

Week 7 & 8:

Detailed description

Dynamic programming

Shortest paths, Longest increasing subsequences, Knapsack

Expected outcomes

Student would be able to identify and design dynamic programming algorithms

Tutorial exercise

Assignments

Learning materials

Dasgupta, C. H. Papadimitriou, and U. V. Vazirani, “Algorithms”, MC Graw Hill Higher Education Edition

 

Week 9 & 10:

Detailed description

Simplex algorithms and Linear Programming

Linear Programming, feasible solutions, geometry of linear programming, Duality, max-flow, min-cut, labeling algorithm, Dijkstra algorithm

Expected outcomes

Student would be able to work with fundamental theoretical issues in linear programming and geometry of solutions

Tutorial exercise

Assignments

Learning materials

C. H. Papadimitriou, Keneth Steinglitz“ Combinatorial Optimization”, Dover Publications

 

Week 11:

Detailed description

Algorithms and Complexity

Computability, Time bounds, polynomial time algorithms,

Expected outcomes

Student would be able to estimate computational criteria in algorithms

Tutorial exercise

 

Learning materials

C. H. Papadimitriou, Keneth Steinglitz“ Combinatorial Optimization”, Dover Publications

 

 

Week 12 & 13:

Detailed description

NP-Problems                                                     

Discussion about NP problems, Backtracking, Branch and Bound

Expected outcomes

Students would be able to work and understand NP complexity problems

Tutorial exercise

Assignments

Learning materials

Dasgupta, C. H. Papadimitriou, and U. V. Vazirani, “Algorithms”, MC Graw Hill Higher Education Edition

C. H. Papadimitriou, Keneth Steinglitz“ Combinatorial Optimization”, Dover Publications

 

 

Week 14:

Detailed description

Review of the subject                                       

Discussion about NP problems, Backtracking, Branch and Bound

Expected outcomes

Student would be able to work and understand NP complexity problems

Tutorial exercise

Assignments

Learning materials

 

 

 

 

T Cormen, C Leiserson, R Rivest and C Stein, "Introduction to Algorithms", MIT Press; 3rd edition

 

Dasgupta, C. H. Papadimitriou, and U. V. Vazirani, “Algorithms”, MC Graw Hill Higher Education Edition

 

C. H. Papadimitriou, Keneth Steinglitz, “Combinatorial Optimization”, Dover Publications

 

 

Ημερομηνία δημιουργίας

Τρίτη 24 Φεβρουαρίου 2015