Loading in 5 sec....

CSCE350 Algorithms and Data StructurePowerPoint Presentation

CSCE350 Algorithms and Data Structure

- 98 Views
- Uploaded on
- Presentation posted in: General

CSCE350 Algorithms and Data Structure

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

CSCE350 Algorithms and Data Structure

Lecture 14

Jianjun Hu

Department of Computer Science and Engineering

University of South Carolina

2009.10.

- Midterm 2 – Thursday Oct 29 in class
- As in Midterm 1, you will be allowed to bring in one single-side letter-size cheat sheet. For the good cheat sheets, we will give you bonus points – 5 points

- Solve problem by transforming into:
- a more convenient instance of the same problem (instance simplification)
- presorting
- Gaussian elimination

- a different representation of the same instance (representation change)
- balanced search trees
- heaps and heapsort
- polynomial evaluation by Horner’s rule
- Fast Fourier Transform

- a different problem altogether (problem reduction)
- reductions to graph problems
- linear programming

- Solve instance of problem by transforming into another simpler/easier instance of the same problem
- Presorting:
- Many problems involving lists are easier when list is sorted.
- searching
- computing the median (selection problem)
- computing the mode
- finding repeated elements

- Find the kth smallest element in A[1],…A[n]. Special cases:
- minimum: k = 1
- maximum: k = n
- median: k = n/2

- Presorting-based algorithm
- sort list
- return A[k]

- Partition-based algorithm (Variable decrease & conquer):
- pivot/split at A[s] using partitioning algorithm from quicksort
- if s=k return A[s]
- else if s<k repeat with sublist A[s+1],…A[n].
- else if s>k repeat with sublist A[1],…A[s-1].

- Presorting-based algorithm: Ω(nlgn) + Θ(1) = Ω(nlgn)
- Partition-based algorithm (Variable decrease & conquer):
- worst case: T(n) =T(n-1) + (n+1) Θ(n2)
- best case: Θ(n)
- average case: T(n) =T(n/2) + (n+1) Θ(n)
- Bonus: also identifies the k smallest elements (not just the kth)

- Special cases of max, min: better, simpler linear algorithm (brute force)
- Conclusion: Presorting does not help in this case.

- Presorting-based algorithm:
- use mergesort (optimal): Θ(nlgn)
- scan array to find repeated adjacent elements: Θ(n)

- Brute force algorithm: Θ(n2)
- Conclusion: Presorting yields significant improvement

Θ(nlgn)

- A mode is a value that occurs most often in a given list of numbers
- For example: the mode of [5, 1, 5, 7, 6, 5, 7] is 5
- Brute-force technique: construct a list to record the frequency of each distinct element
- In each iteration, the i-th element is compared to the stored distinct elements. If a matching is found, its frequency is incremented by 1. Otherwise, current element is added to the list as a distinct element
- Worst case complexity Θ(n2), when all the given n elements are distinct

- The dominating part is on the sorting Θ(nlgn)
- The complexity of the outer while loop is linear since each element will be visited once for comparison
- Therefore, the complexity of presortMode is Θ(nlgn)
- This is much more efficient than the brute-force algorithm that needs Θ(n2)

- Brute-force search: worst case Θ(n).
- Presorted search T(n)=Tsort(n)+ Tsearch(n)
- = Θ(n log n)+ Θ(log n) = Θ(n log n)
- Therefore, the presorted search is inferior to the brute-force search
- However, if we are going to repeat the searching in the same list many times, presorted search may be more efficient because the sorting need not be repeated

Binary search

- Elementary searching algorithms
- sequential search
- binary search
- binary tree search

- Balanced tree searching
- AVL trees
- red-black trees
- multiway balanced trees (2-3 trees, 2-3-4 trees, B trees)

- Hashing
- separate chaining
- open addressing

- For every node, difference in height between left and right subtree is at most 1

An AVL tree

Not an AVL tree

The number shown above the node is its balance factor, the difference between the heights of the node’s left and right subtrees.

- Insert a new node to a AVL binary search tree may make it unbalanced. The new node is always inserted as a leaf
- We transform it into a balanced one by rotation operations
- A rotation in an AVL tree is a local transformation of its subtree rooted at a node whose balance factor has become either +2 or -2 because of the insertion of a new node
- If there are several such nodes, we rotate the tree rooted at the unbalanced node that is closest to the newly inserted leaf
- There are four types of rotations and two of them are mirror images of the other two

- Rotations can be done in constant time (steps)
- Rotations not only guarantee that the resulting tree is balanced, but also should preserve the basic property of a binary search tree, i.e., all the keys in the left subtree should be smaller than the root, the root should be smaller than all the keys in the right subtree.
- The height (h) of an AVL tree with n nodes is bounded by
- lg n≤h≤ 1.4404 lg (n + 2) - 1.3277 average: 1.01 lg n + 0.1 for large n
- So the operations of searching and insertion in an AVL tree take Θ(logn) in the worst case

- Balance tree:Red-black trees (height of subtrees is allowed to differ by up to a factor of 2) and splay trees
- Allow more than one element in a node of a search tree: 2-3 trees, 2-3-4 trees and B-trees.
- Read Section 6.3 for more details.