![Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil
22
String : A string is a finite sequence of symbols that are
chosen from a set or alphabet:
Alphabet is a set of characters or symbols
Substring: A substring or subsequence of a string is a
subset of the symbols in a string where the order of
elements is preserved
Suffix: A suffix of S is a substring S[i, …, m − 1], where i
ranges between 0 and m − 1](https://image.slidesharecdn.com/16algoanalysisdesign-150824113102-lva1-app6892/85/16-Algo-analysis-Design-Data-Structures-using-C-by-Varsha-Patil-22-320.jpg)
16. Algo analysis & Design - Data Structures using C++ by Varsha Patil
- 1. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 1
- 2. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 2 After completing this chapter, the reader will be able to understand the following: Basic tools needed to develop and analyze algorithms Methods to compute the efficiency of algorithms Ways to make a wise choice among many solutions for a given problem
- 3. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 3 Algorithm Analysis Asymptoti c Notations (W, p, O) Big Omega (W)
- 4. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 4 Algorithm Analysis Asymptoti c Notations (W, p, O) Big Omega (W)
- 5. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 5 The following steps elaborate the general structure of the divide-and-conquer strategy If the data size n of problem P is fundamental, calculate the result of P(n) and go to step 4 If the data size n of problem P is not fundamental, divide the problem P(n) into equivalent subproblems P(n1), P(n2), … P(ni) such that i ≥ 1 Apply divide-and-conquer recursively to each individual subproblem P(n1), P(n2), …,P(ni) Combine the results of all subproblems P(n1), P(n2),…, P(ni) to get the final solution of P(n)
- 6. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 6 At level one, only one call to a partition is made with n elements; at level two, Atmost two calls are made with elements (n - 1), and so on C(n) = O(n2)
- 7. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 7 General Knapsack Problem Elements of Greedy Strategy
- 8. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 8 To decide whether a problem can be solved using a greedy strategy, the following elements should be considered: Greedy-choice property Optimal substructure Greedy Method
- 9. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 9 Greedy-choice property A problem exhibits greedy-choice property if a globally optimal solution can be arrived at by making a locally optimal greedy choice That is, we make the choice that seems best at that time without considering the results from the sub problems
- 10. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 10 The General Method Elements of Dynamic Programming Principle of Optimality Limitations of Dynamic Programming Knapsack Problem
- 11. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 11 The General Method Elements of Dynamic Programming Principle of Optimality Limitations of Dynamic Programming Knapsack Problem
- 12. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 12 A dynamic programming solution has the following three components: Formulate the answer as a recurrence relation or a recursive algorithm Show that the number of different instances of your recurrence is bounded by a polynomial Specify an order of evaluation for the recurrence
- 13. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 13 To decide whether a problem can be solved using the dynamic programming method, the following three elements of dynamic programming should be considered: Optimal substructure Overlapping subproblems Memorization
- 14. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 14 A problem exhibits optimal substructure if an optimal solution to the problem contains within it optimal solutions to subproblems It also means that dynamic programming (and greedy method) might apply
- 15. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 15 When a recursive algorithm revisits the same problem repeatedly, it is said that the optimization problem has overlapping sub problems This is beneficial for dynamic programming It solves each subproblem once and stores the answer in a table This answer can be searched in constant time when required. This is contradictory to the divide-and-conquer strategy where a new problem is generated at each step of recursion
- 16. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 16 A problem exhibits optimal substructure if an optimal solution to the problem contains within it optimal solutions to subproblems It also means that dynamic programming (and greedy method) might apply
- 17. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 17 However, it uses the control structure similar to the recursive algorithm In a memorized recursive algorithm, an entry is maintained in a table for the solution to each subproblem Initially, all entries contain a special value, which indicates that the entry is not yet used For each subproblem, which is encountered for the first time, its solution is computed and stored in the table Next time, for that subproblem, its entry is searched and the value is used This can be implemented using hashing
- 18. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 18 The principle of optimality states that an optimal sequence of decisions has the property that whatever the initial state and decision are, the remaining decisions must constitute an optimal decision sequence with regard to the state resulting from the first decision Principle of Optimality
- 19. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 19 Pattern matching is the process of finding the presence of a particular string (pattern) in the given string (text) Pattern Matching
- 20. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 20 Database search Search engine Text editors Intrusion detection Natural language processing Feature detection in digitized images A few such applications are as follows:
- 21. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 21 The most popular are the following: Brute-force approach Boyer–Moore algorithm Knuth–Morris–Pratt algorithm Robin–Karp algorithm Text partitioning algorithm Semi-numerical algorithm Popular techniques for string pattern search
- 22. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 22 String : A string is a finite sequence of symbols that are chosen from a set or alphabet: Alphabet is a set of characters or symbols Substring: A substring or subsequence of a string is a subset of the symbols in a string where the order of elements is preserved Suffix: A suffix of S is a substring S[i, …, m − 1], where i ranges between 0 and m − 1
- 23. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 23 This is a simple straight forward approach based on the comparison of a pattern character by character with a string
- 24. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 24 The steps involved in this approach are as follows: Adjust the pattern P at beginning of the text Start moving from left to right and compare the character of pattern to the corresponding character in text Continue with step 2 until successful (all characters of the pattern are matched) or unsuccessful (a mismatch is detected)
- 25. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 25 Boyer and Moore have developed an efficient pattern matching algorithm Instead of sliding by one character to the right at a time, in Boyer–Moore approach, the sliding to the right is done in longer steps The algorithm scans the character of pattern from right to left beginning with the rightmost character If the text symbol compared with the rightmost pattern symbol does not occur in the pattern at all, then the pattern can be shifted by m positions (where m is length of pattern)
- 26. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 26 The researchers Knuth, Morris, and Pattern proposed a linear time algorithm for the string matching problem In this approach, a matching time of O(n) is achieved by avoiding comparisons with characters of T that have previously been involved in comparison with some element of the pattern P to be matched so that backtracking is avoided
- 27. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 27 The KMP matcher finds the occurrence of the pattern P in text T and returns the number of shifts of P, after which the occurrence is found taking T, P, and prefix function p as inputs
- 28. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 28 A compact data structure that represents a set of strings (such as all the words in a text) known as tries A trie is a tree-based data structure for storing strings to make pattern matching faster A trie helps in pattern matching in time that is proportional to the length of the pattern Tries can be used to perform prefix query for information retrieval Prefix query searches for the longest prefix of a given string that matches a prefix of some string in the tries
- 29. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 29 There are variants of tries, which are listed as follows: Standard tries Compressed trie Suffix tries
- 30. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 30 The standard trie for a set of strings S is an ordered tree such that Each node but the root is labelled with a character; The children of a node are alphabetically ordered; The paths from the external nodes to the oot yield the strings of S
- 31. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 31 Similar to the standard trie, a compressed is a tree- based data structure For storing strings in order to make pattern matching much faster This is an optimized approach for pattern matching specially suitable for applications where time is a more crucial factor
- 32. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 32 Following are the unique characteristics of compressed tire: A compressed trie (or Patricia trie) has internal nodes of degree at least 2 It is obtained from standard trie by compressing chains of redundant nodes
- 33. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 33 b id u o ok il sh y s ell to ck p
- 34. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 34 m e g h a v i n d 0 1 2 3 4 5 6 7 8 d e me ghavind nd ndghavind nd ghavind vind
- 35. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 35 A suffix trie is a compressed Trie for all the suffixes of a text This is a compressed Trie, and hence, possesses all features a compressed trie and makes it more powerful for making a search faster as it includes all suffixes of a text
- 36. Oxford University Press © 2012Data Structures Using C++ by Dr Varsha Patil 36