1. CS 263

2.  Classification of algorithm against a model pattern ◦ Each model demonstrate the performance scalability of an algorithm  Sorting algorithms might have different model patterns  Depending on the number of records sorted, one model might work better than another  However.. An increase in records would invoke high overhead in processing at some point ◦ A search of records would assume that the record is the last to be found (or non-existent), a worst-case scenario  Noted as O(N)  A 25 record search would take five times as long as the 5 record search

3.  The presumption is that we’re talking about speed/performance  We could also be concerned with other resources ◦ Memory utilization ◦ Disk trashing  Depending on the resource we’re targeting, the algorithm might change ◦ Slower algorithm (time) vs. memory consumption

4.  Constants and variables ◦ Algorithm 1  Compare each array element against every other element for my $i (0.. $#array) { for my $j (0.. $#array) { next if $j == $i; # Compare $i, $j }  O(N^2) ◦ Algorithm 2  Optimized algorithm, cuts run time in half for my $i (0.. $#array - 1) { for my $j ($i + 1.. $#array) { # Compare $i, $j } }  The notation is NOT O(N 2 /2), but still O(N 2 ) ◦ The “divide by 2” remains constant regardless of input size

5.  Big O will not care if you purchase more RAM ◦ It’s THEORY  So why bother? ◦ It serves as an indicator of which algorithm to use when you consider your circumstances  Big O servers as a “limiting behavior” of a function ◦ An upper bound of performance ◦ Big O also referred to as  Landau notation  Bachmann-Landau notation  Asymptotic notation

6.  O(1)No growth curve ◦ Performance is independent of the size of the data set  O(N) ◦ Performance is directly proportional to the size of the data set  O(N+M) ◦ Two data sets combined, and that determines performance  O(N 2 ) ◦ Each element of a set requires to be processed against all others. Bubble sorts are in this category  O(N*M) ◦ Each element of one data set is processed against each element of another data set  Set of regular expressions needs to be processed against a text file  O(N 3 ) ◦ Nested looping going on here…

7.  O(log N) and O(N log N) ◦ Data set is “iteratively partitioned” (example… balanced binary tree)  Unbalanced trees are O(N 2 ) to build and O(N) to search ◦ The O(log N) refers to the number of times you can partition a set in half iteratively  Log 2 N grows slowly (doubling N has a small effect) and the curve flattens out ◦ Building the tree is more expensive

8.  Scaling order ◦ O(1) ◦ O(log N) ◦ O(N) ◦ O(N log N) ◦ O(N 2 ) ◦ O(2 N )  Efficiency is NOT the same as scalability ◦ Well coded O(N 2 ) algorithm might outperform a poorly coded O(N log N) one, but at some point their performance curves will cross

9.  Measure performance of algorithm as the number of steps performed approaches infinity ◦ when the number of steps is exceeded  Algorithm with the greater running time will always take longer to execute that algorithm with the shorter running time  Example: ◦ Bubble sort uses nested loops  Running time is O(N 2 ) ◦ Merge sort  Divides array into halves, sorts each half, and then merges the halves  Running time is O(N log 2 N) ◦ While the Merge sort has a shorter running time, for smaller arrays the Bubble sort will be more efficient

10.  Misconception: An algorithm that works on a small project will scale up when data increases  If an algorithm of O(N 2 ) type works fine, coding complication of trying to switch the routine to an O(N log N) algorithm match

11. # O(n^2) for my $i (0..$#a-1) { for (0..$#a-1-$i) { ($a[$_],$a[$_+1]) = ($a[$_+1],$a[$_]) if ($a[$_+1] < $a[$_]); } # O(n log(n)) for my $i (0..$#a-1) { # looping over already-sorted items is not needed for (0..$#a-1-$i) { ($a[$_],$a[$_+1]) = ($a[$_+1],$a[$_]) if ($a[$_+1] < $a[$_]); }

12.  Searching via a loop ◦ O(N)  Reduce the time to O(log(N)) ◦ Break out of the loop once the “hit” is found