Module: Algorithms

Algorithms, computational complexity, asymptotic notations, pseudocode, greedy algorithms, easy vs. hard problems.

Learning Outcomes

Use algorithm principles to characterize and solve problems.

Use Big O notation to characterize complexity of algorithms.
Represent an algorithm using pseudocode.
Use the greedy algorithm to solve problems such as the coin change problem.

Readings

Rosen Sections 3.1-3.3

Algorithms, growth of functions, complexity

Textbook 42 pages

Algorithms, Part 1

Algorithms, computational complexity, asymptotic notations.

Lecture notes

Algorithms, Part 2

Pseudocode, algorithmic paradigms, greedy algorithms, easy vs. hard problems.

Lecture notes

Experiential Learning

Algorithms

Linear search, binary search, bubble sort, insertion sort (Rosen Section 3.1)

Problems

Algorithms

Linear search, binary search, bubble sort, insertion sort (Rosen Section 3.1)

Solutions

Growth of functions

Big O estimates (Rosen Section 3.2)

Problems

Growth of functions

Big O estimates (Rosen Section 3.2)

Solutions

Complexity of algorithms

Big O estimates (continued) (Rosen Section 3.3)

Problems

Complexity of algorithms

Big O estimates (continued) (Rosen Section 3.3)

Solutions