Algorithm Animations for Practical Optimization: A Gentle Introduction

Animations are a good way of visualizing and understanding how algorithms work. See below for a list of animations to accompany the online textbook Practical Optimization: a Gentle Introduction.

This is an open-source site (you can download the source files).These animations were created by Kelly Kinahan and Jennifer Pryor.

Linear Programming:

Example LP1: A Graphical Representation of a 2D Problem
Example LP2: Finding Corner Points From a Graph
Example LP3: Using a Simplex Tableau
Example LP4: Finding the Optimum Point from a Two Phase Graph
Example LP5: Two Phase Simplex Tableau
Example LP6: Sensitivity Analysis

Networks:

Example Networks1: Dijkstra's Algorithm for Shortest Route Problems
Example Networks2: Minimum Spanning Tree
Example Networks3: Maximum Flow/Minimum Cut Problem
Example Networks4: PERT for Project Planning and Scheduling

Integer Programming:

Example IP1: Branch and Bound Method
Example IP2: Balas Additive Algorithm for Binary Integer Programming
Example IP3: Dakin's Algorithm for Mixed Integer LPs

Heuristics for Discrete Optimization:

Example Heuristics1: The Assignment Problem Using the Genetic Algorithm
Example Heuristics2: The Traveling Salesman Problem Using Simulated Annealing

Dynamic Programming:

Example DP1: Dynamic Programming Problem

Nonlinear Programming:

Example NLP1: Why Nonlinear Programming is Difficult
Example NLP2: Nonlinear Programming Sample Problem
Example NLP3: LOQO Solver Trajectory
Example NLP4: MINOS Solver Trajectory
Example NLP5: DONLP2 Solver Trajectory
Example NLP6: Demo Program, Steepest Descent, Offsite Animation Links
Example NLP7: Nelder-Mead A lgorithm