Chapter 17

Using Randomized Algorithms

IN THIS CHAPTER

check Understanding how randomness can prove smarter than more reasoned ways

check Introducing key ideas about probability and its distributions

check Discovering how a Monte Carlo simulation works

check Learning about Quickselect and revisiting Quicksort algorithms

Random 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 distributed 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 ...

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