ADVANCED ALGORITHMS

Academic Year 2022-2023

Teachers

Alberto Policriti

Unit Credits

Teaching Period

Second Period

Course Type

Characterizing

Prerequisites. Elementary algorithms and data structures

Teaching Methods. Frontal lessons and seminars

Verification of Learning. Oral examination and in-depth study of a specific topic chosen by the student.

Objectives

After passing the exam, it is considered that the student is able to read and implement advanced algorithms for searching and compressing texts, even large ones. Be able to interpret and adapt to specific case studies of advanced data structures design. Be able to use the “randomness” and the ability to deploy computing on multiple-node architectures. It is able to set sufficiently precise and independent architectural limits to the computational complexity of programs studied.

Algorithms and data structures for pattern matching and compression.

Randomized algorithms.

Texts

D. Gusfield, Algorithms on Strings, Trees, and Sequences.

Cormen T.H., Leiserson C.E., Rivest R.L, Stein C., Introduction to Algorithms, MIT Press, Third edition, 2009.

P. Raghavan R. Motwani, Randomized Algorithms.

Compact Data Structures – A Practical Approach. Cambridge University Press 2016

Lecture notes provided by the teacher

Academic Year 2022-2023