Random sampling: disk model, known length, the streaming model m=1. About the algorithmic solution of the density-based problem only hints of the basic ideas. Maximum sub-array sum in 1d and its variations.Ĭhap. An example of algorithm analysis: the sum of n numbers.

Models of computation: RAM, 2-level memory. Some of these solutions will be discussed also at an experimental level, in order to introduce proper engineering and tuning tools for algorithmic development. We will add to such theoretical analysis several other engineering considerations spurring from the implementation of the proposed algorithms and from experiments published in the literature.Įvery lecture will follow a problem-driven approach that starts from a real software-design problem, abstracts it in a combinatorial way (suitable for an algorithmic investigation), and then introduces algorithms aimed at minimizing the use of some computational resources like time, space, communication, I/O, energy, etc.

The design and analysis will involve several models of computation- such as RAM, 2-level memory, cache-oblivious, streaming- in order to take into account the architectural features and the memory hierarchy of modern PCs and the availability of Big Data upon which those algorithms could work on.

In this course we will study, design and analyze advanced algorithms and data structures for the efficient solution of combinatorial problems involving all basic data types, such as integers, strings, (geometric) points, trees and graphs.

Free Disk Sorter Ultimate 15.6.18