Algorithms For Dummies

» Introducing key ideas about 

probability and its distributions

 

» Discovering how a Monte Carlo 

simulation works

 

» Learning about Quickselect and 

revisiting Quicksort algorithms

Using Randomized 

Algorithms

R

andom number generators are a key function in computing and play an 

important role in the algorithmic techniques discussed in this part of the 

book. Randomization isn’t just for gaming or for gambling, but people 

employ it to solve a large variety of problems. Randomization sometimes proves 

more  effective  during  optimization  than  other  techniques  and  in  obtaining  the 

right solution than more reasoned ways. It helps different techniques work better, 

from local search and simulated annealing to heuristics, cryptography, and dis-

tributed computing (with cryptography for concealing information being the most 

critical).

You can find randomization embedded into unexpected everyday tools. The robot 

vacuum cleaner Roomba (designed by a company founded by the Massachusetts 

Institute of Technology [MIT]) cleans rooms without having a precise plan and a 

blueprint of the place. The tool works most of the time by wandering randomly 

around the room and, according to the original patent, after hitting an obstacle, it 

turns a random number of degrees and starts in a new direction. Yet Roomba 

always completes its cleaning chores. (If you are curious about how it operates, 

you can consult 

http://www.explainthatstuff.com/how-roomba-works.html

.)

Chapter