COMP90038 Algorithms and Complexity
COMP90038
Algorithms and Complexity
Lecture 14: Transform and Conquer
(with thanks to Harald Søndergaard & Michael Kirley)
Andres Munoz-Acosta
munoz.m@unimelb.edu.au
Peter Hall Building G.83
mailto:munoz.m@unimelb.edu.au
Exercise: Finding Anagrams
• An anagram of a word w is a word which uses the same letters as w
but in a different order.
• Example: ‘ate’, ‘tea’ and ‘eat’ are anagrams.
• Example: ‘post’, ‘spot’, ‘pots’ and ‘tops’ are anagrams.
• Example: ‘garner’ and ‘ranger’ are anagrams.
Exercise: Finding Anagrams
• You are given a very long list of words:
{health, revolution, foolish, garner, drive, praise, traverse, anger, ranger,
… scoop, fall, praise}
• Devise an algorithm to find all anagrams in the list.
Transform and Conquer
• Instance simplification
• Representational change
• Problem reduction
Instance Simplification
• General principle: Try to make the problem easier through some type
of pre-processing, typically sorting.
• We can pre-sort input to speed up, for example
• finding the median
• uniqueness checking
• finding the mode
Uniqueness Checking, Brute-Force
• The problem:
• Given an unsorted array A[0]…A[n-1], is A[i]≠A[j] whenever i≠j?
• The obvious approach is brute-force:
• What is the complexity of this?
Uniqueness Checking, with Pre-sorting
• Sorting makes the problem easier:
• What is the complexity of this?
Exercise: Computing a Mode
• A mode is a list or array element which occurs most frequently in the
list/array. For example, in
[ 42, 78, 13, 13, 57, 42, 57, 78, 13, 98, 42, 33 ]
the elements 13 and 42 are modes.
• The problem:
• Given array A, find a mode.
• Discuss a brute-force approach vs a pre-sorting approach.
Mode Finding, with Pre-sorting
• Again, after sorting, the rest takes linear time.
Searching, with Pre-sorting
• The problem:
• Given unsorted array A, find item x (or determine that it is absent).
• Compare these two approaches:
• Perform a sequential search
• Sort, then perform binary search
• What are the complexities of these approaches?
Searching, with Pre-sorting
• What if we need to search for m items?
• Let us do a back-of-the envelope calculation (consider worst-cases for simplicity):
• Take n = 1024 and m = 32.
• Sequential search: m n = 32,768.
• Sorting + binsearch: n log2 n + m log2 n = 10,240 + 320 = 10,560.
• Average-case analysis will look somewhat better for sequential search, but pre-sorting
will still win.
Exercise: Finding Anagrams
• You are given a very long list of words.
• Devise an algorithm to find all anagrams in the list.
• An approach could be to sort each word, sort the list of words, and
then find the repeats…
• What would be the time complexity?
Exercise: Finding Anagrams
aehhlt
eilnoortvu
fhiloos
aegnrr
deirv
aeiprs
aeerstv
aegnr
aegnrr
…
coops
afll
lrtuy
health
revolution
foolish
garner
drive
praise
traverse
anger
ranger
…
scoop
fall
truly
aeerstv
aegnr
aegnrr
aegnrr
aehhlt
aeiprs
afll
coops
deirv
…
eilnoortvu
fhiloos
lrtuy
1
1
1
2 (This element is an anagram)
1
1
1
1
1
…
1
1
1
Sort each word Sort the list Find repeats
Binary Search Trees
• A binary search tree, or BST, is a binary tree that stores elements in
all internal nodes, with each sub-tree satisfying the BST property:
• Let the root be r; then each element in the left subtree is smaller
than r and each element in the right sub-tree is larger than r.
• (For simplicity we will assume that all keys are different.)
Binary Search Trees
• BSTs are useful for search applications. To search for k in a BST, compare against its root r.
If r=k, we are done; otherwise search in the left or right sub-tree, according as k
• If a BST with n elements is “reasonably” balanced, search involves, in the worst case,
Θ(log n) comparisons.
Binary Search Trees
• If the BST is not well balanced, search performance degrades, and
may be as bad as linear search:
Insertion in Binary Search Trees
• To insert a new element k into a BST, we pretend to search for k.
• When the search has taken us to the fringe of the BST (we find an empty sub-tree), we insert k where we
would expect to find it.
• Where would you insert 24?
Insertion in Binary Search Trees
• To insert a new element k into a BST, we pretend to search for k.
• When the search has taken us to the fringe of the BST (we find an empty sub-tree), we insert k where we
would expect to find it.
• Where would you insert 24?
BST Traversal Quiz
• Performing ………………. traversal of a BST will produce its elements in
sorted order.
Next Up: Balancing Binary Search Trees
• To optimise the performance of BST search, it is important to keep
trees (reasonably) balanced.
• Next we shall look at AVL trees and 2–3 trees.