Knowledge and understanding: students will acquire knowldege relative to the main methodologies for the design of efficient algorithms (incremental, recursion, dynamic programming, greedy algorithms), as well as the techniques for their computational analysis, both in the worst and in the average cases.
Applying knowledge and understanding: students will acquire the ability to solve problems of low difficulty, requiring the design and analysis of elementary algorithmic solutions.
Making judgements: students will be able to evaluate the quality of an algorithmic solution in terms of efficiency and reuse.
Communication skills: students will acquire the necessary communication skills and expressive ability in communicate problems regarding the algorithmic studies, also to non-expert interlocutors.
Learning skills: students will have the ability to adapt the knowledge acquired also in new contexts and to advance his/her knowledge through the consultation of specialist sources in the algorithmic field.
Conoscenza e capacità di comprensione (knowledge and understanding): we will focus on the implementation of the main data stuttures analyzed during the theoretical module of Algorithms
Capacità di applicare conoscenza e comprensione (applying knowledge and understanding): we will focus on the implementation skills and on the algorithmic solutions design.
Autonomia di giudizio (making judgements): The student will be able to judge the effectiveness of its implementation and of their project work.
Capacità di apprendimento (learning skills): the student will be able to adapt to other contexts all solutions and algorithms.
General description
The course presents the main design methodoligies (incremental, recursion, dynamic programming, greedy algorithms) and the techniques for their complexity analysis, both in the worst and average cases.
DETAILED SYLLABUS
Introduction
Computational problems and algorithms: the sorting problem
Algorithms as technology
Incremental methodology: Insertion-Sort (correctness and complexity)
Divide-and-Conquer methodology: Merge-Sort (complexity)
Asymptotic notations and relationships among them
Standard notations and common functions.
Recurrences
The substitution method
The iterative method and the recursion tree
The master theorem
Sorting and order statistics
Heaps and their construction
Priority queues
Quicksort and its randomized version
Analysis of Quicksort in the worst- and average case
Lower bounds for sorting
Sorting in linear time: Counting-Sort, Radix-Sort, Bucket-Sort
Medians and order statistics
Hashing
Hash table
Hash functions (division method, multiplication method, universal hashing)
Open addressing
Red-black trees
Rotations, insertions, deletions
Complexity analysis
Elements of dynamic programming
Optimal substructure, overlapping subproblems, reconstructing an optimal solution
Some case studies: assembly line scheduling, matrix-chain multiplication, longest common subsequence, editing distance
Elements of the greedy strategy
Greedy-choice property, optimal substructure
Some case studies: the activity-selection problem, Huffman codes
Elementary graph algorithms
Breadth-first search
Depth-first search (edges classification)
Topological sorting
Strongly connected components
The aim of Algorithms Laboratory is to provide the tools for the implementation of the algorithms and the data structures discussed in the Algorithms class. It will be done through the use of an object-oriented programming language. The C ++ language will be used as the main tool to present the implementations of data structures and algorithms.
T.H. Cormen, C.E. Leiserson, R.L. Rivest, C. Stein. Introduction to algorithms (Third Edition), The MIT Press, Cambridge - Massachusetts, 2009