Module: Algorithms

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

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

A07: Homework 7

Homework for section 3.1

Homework 23 Oct 05:45 PM

A08: Homework 8

Homework for sections 3.2-3.3

Homework 30 Oct 05:45 PM

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