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10/11/10
19:31:55
1
20
CS 61B:
Lecture 20
Wednesday, October 13, 2010
Today’s reading:
ASYMPTOTIC ANALYSIS (bounds on running time or memory)
===================
Suppose an algorithm for processing a retail store’s inventory takes:
 10,000 milliseconds to read the initial inventory from disk, and then
 10 milliseconds to process each transaction (items acquired or sold).
Processing n transactions takes (10,000 + 10 n) ms.
Even though 10,000 >> 10,
we sense that the "10 n" term will be more important if the number of
transactions is very large.
We also know that these coefficients will change if we buy a faster computer or
disk drive, or use a different language or compiler.
We want a way to express
the speed of an algorithm independently of a specific implementation on a
specific machinespecifically, we want to ignore constant factors (which get
smaller and smaller as technology improves).
BigOh Notation (upper bounds on a function’s growth)

We use BigOh notation to say how slowly code might run as its input grows.
Let n be the size of a program’s _input_ (in bits or data words or whatever).
Let T(n) be a function.
For now, T(n) is precisely equal to the algorithm’s
running time, given an input of size n (usually a complicated expression).
Let f(n) be another functionpreferably a simple function like f(n) = n.
We say that T(n) is in O( f(n) )
IF AND ONLY IF
T(n) <= c f(n)
WHENEVER n IS BIG, FOR SOME LARGE CONSTANT c.
*
HOW BIG IS "BIG"?
Big enough to make T(n) fit under c f(n).
*
HOW LARGE IS c?
Large enough to make T(n) fit under c f(n).
EXAMPLE:
Inventory

Let’s consider the function T(n) = 10,000 + 10 n, from our example above.
Let’s try out f(n) = n, because it’s simple.
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This note was uploaded on 01/10/2012 for the course CS 61B taught by Professor Canny during the Fall '01 term at University of California, Berkeley.
 Fall '01
 Canny
 Data Structures

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