**__2.4: A Survey of Common Running Times__**

This chapter looks at different running times of algorithms and cases in which they would be used.
It is suggested to look at a new problem by thinking about two kinds of bounds: one on the running time you hope to achieve, and the other on the size of the problem's natural search space - the set of all possible solutions.  

__Linear Time__

In most cases linear time is used for iterating through a collection.

__Quadratic Time__

Often times quadratic time is used in double for loops or two conditions on a while loop (as seen in the GS algorithm).

__Cubic Time__

More elaborate sets of nested loops often lead to algorithms that run in O(n^3).

__O(n^k) Time__

A running time of O(n^k) is obtained for any constant k when searching over all subsets of size k.

__Beyond Polynomial Time__

Exponential and n! - avoid these.

__Sublinear Time__

Essentially in order to get algorithms that run at less than O(n), you need to shrink the size of the "active region" while executing the algorithm.  For example, binary search cuts the active region in half each time the loop iterates.

This chapter lays down a good basic description of common running times, but I already knew most of it.  
8/10