FUNDAMENTAL ALGORITHMS

Type of course:	Compulsory
Language of instruction:	English
Erasmus Language of instruction:	English
Name of lecturer:	Ovidiu Domsa
Seminar tutor:	Adriana Bîrluțiu
Form of education	Full-time

Form of instruction:	Class
Number of teaching hours per semester:	56
Number of teaching hours per week:	4
Semester:	Autumn
Form of receiving a credit for a course:	Grade
Number of ECTS credits allocated	5

Course aims:

• Develop algorithmic thinking and skills for developing complex algorithms
• Learning basic tools for developing fundamental algorithms.
• Knowledge of different types of fundamental algorithms and their development methods.
• Use of an advanced programming language for implementing the studied algorithms.

Course Entry Requirements:

Imperative and procedural programming Algorithms and data structures Graph algorithms

Course contents:

• General principles for algorithm development. • Complexity of algorithms. Asymptotic analysis of worst case scenario. • Numerical algorithms. Optimization of numerical algorithms. Primality. Bell numbers. Stirling numbers. Catalan numbers. Numbers with special properties. • Sorting: HeapSort, QuickSort, RadixSort, Median-Algorithms, Lower Bounds. • Analysis of sorting and searching algorithms complexity. • Parallel sorting: enumeration sort, odd-even transposition sort. • Parallel sorting: bitonic sort, quicksort on a hypercube. • Binary search trees. • AVL trees. Red-black trees. B-trees. • Hash tables. Collision resolution. Hash functions. • Graph algorithms: Transitive Closure, Shortest Path Problems, Minimum Spanning Trees. • Branch&Bound algorithms. Exemples of problems solved with the Branch&Bound method. • NP-complete algorithms. • Analysis, evaluation, and feed-back.

Teaching methods:

Lecture, conversation, exemplification, problem solving, documentation.

Learning outcomes:

• acquisition of basic and specific knowledge about the concept of fundamental algorithms; • the ability to identify the applicability of the studied algorithms in real problems; • understanding the need of using advanced methods to create efficient algorithms when addressing problems from an specific domain; • Acquiring advanced knowledge of algorithms complexity and apply efficient methods to solve different practical problems.

Learning outcomes verification and assessment criteria:

Written exams – 50%; Continuous assessment and laboratory practical works – 50%.