DOCSLIB.ORG

Savitch Java Ch. 11

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Savitch Java Ch. 11 Chapter 11 Recursion z Basics of Recursion z Programming with Recursion Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 1 Overview Recursion: a definition in terms of itself. Recursion in algorithms: z Recursion is a natural approach to some problems » it sounds circular, but in practice it is not z An algorithm is a step-by-step set of rules to solve a problem » it must eventually terminate with a solution z A recursive algorithm uses itself to solve one or more subcases Recursion in Java: z Recursive methods implement recursive algorithms z A recursive method is one whose definition includes a call to itself » a method definition with an invocation of the very method used to define it Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 2 Recursive Methods Must Eventually Terminate A recursive method must have at least one base, or stopping, case. z A base case does not execute a recursive call » it stops the recursion z Each successive call to itself must be a "smaller version of itself" so that a base case is eventually reached » an argument must be made smaller each call so that eventually the base case executes and stops the recursion Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 3 Example: a Recursive Algorithm One way to search a phone book (which is an alphabetically ordered list) for a name is with the following recursive algorithm: Search: middle page = (first page + last page)/2 Open the phone book to middle page; If (name is on middle page) then done; //this is the base case else if (name is alphabetically before middle page) last page = middle page //redefine search area to front half Search //recursive call with reduced number of pages else //name must be after middle page first page = middle page //redefine search area to back half Search //recursive call with reduced number of pages Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 4 Example: A Recursive Method z RecursionDemo is a class to process an integer and print out its digits in words » e.g. entering 123 would produce the output "one two three" z inWords is the method that does the work of translating an integer to words public static void inWords(int numeral) { if (numeral < 10) System.out.print(digitWord(numeral) + " "); Here is the else //numeral has two or more digits recursive call: { inWords inWords(numeral/10); definition calls itself System.out.print(digitWord(numeral%10) + " "); } } Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 5 Example: A Base Case z Each recursive call to inWords reduces the integer by one digit » it drops out the least significant digit z Eventually the argument to inWords has only one digit » the if/else statement finally executes the base case and the algorithm terminates public static void inWords(int numeral) Base case { executes if (numeral < 10) when only 1 System.out.print(digitWord(numeral) + " "); digit is left else //numeral has two or more digits { Size of problem inWords(numeral/10); is reduced for each recursive System.out.print(digitWord(numeral%10) + " "); call } } Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 6 inWords(987) 1 What Happens with if (987 < 10) // print digit here a Recursive Call else //two or more digits left { inWords(987/10); // print digit here } z Suppose that inWords is called from the main method of RecursionDemo with the argument 987 z This box shows the code of inWords (slightly simplified) with the parameter numeral replaced by the argument 987 Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 7 inWords(987) What Happens with if (987 < 10) // print digit here a Recursive Call else //two or more digits left { inWords(987/10); The argument is getting // print digit here shorter and will eventually } inWords(98) get to the base case. 2 if (98 < 10) // print digit here Computation else //two or more digits left waits here { until recursive inWords(98/10); call returns // print digit here } z The if condition is false, so the else part of the code is executed z In the else part there is a recursive call to inWords, with 987/10 or 98 as the argument Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 8 inWords(987) What Happens with if (987 < 10) // print digit here a Recursive Call else //two or more digits left { inWords(987/10); // print digit here } inWords(98) if (98 < 10) // print digit here else //two or more digits left { inWords(98/10); // print digit here } inWords(9) 3 if (9 < 10) z In the recursive call, the if // print digit here condition is false, so again else //two or more digits left the else part of the code is { inWords(numeral/10); executed and another // print digit here recursive call is made. } Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 9 inWords(987) What Happens with if (987 < 10) // print digit here a Recursive Call else //two or more digits left { inWords(987/10); Output: nine // print digit here } inWords(98) if (98 < 10) // print digit here else //two or more digits left { 4 inWords(98/10); // print 98 % 10 } inWords(9) if (9 < 10) z This time the if condition is // print nine true and the base case is else //two or more digits left executed. { inWords(numeral/10); z The method prints nine and // print digit here returns with no recursive call. } Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 10 inWords(987) What Happens with if (987 < 10) // print out digit here a Recursive Call else //two or more digits left { 5 inWords(987/10); // print digit here } inWords(98) if (98 < 10) // print out digit here else //two or more digits left { inWords(98/10); // print out 98 % 10 here } Output: nine eight z The method executes the next statement after the recursive call, prints eight and then returns. Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 11 inWords(987) What Happens with if (987 < 10) // print out digit here 6 a Recursive Call else //two or more digits left { inWords(987/10); 6 // print 987 % 10 Output: nine eight seven } z Again the computation resumes where it left off and executes the next statement after the recursive method call. z It prints seven and returns and computation resumes in the main method. Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 12 Remember: Key to Successful Recursion Recursion will not work correctly unless you follow some specific guidelines: z The heart of the method definition can be an if-else statement or some other branching statement. z One or more of the branches should include a recursive invocation of the method. » Recursive invocations should use "smaller" arguments or solve "smaller" versions of the task. z One or more branches should include no recursive invocations. These are the stopping cases or base cases. Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 13 Warning: Infinite Recursion May Cause a Stack Overflow Error z If a recursive method is called and a base case never executes, the method keeps calling itself z The stack is a data structure that keeps track of recursive calls z Every call puts data related to the call on the stack » the data is taken off the stack only when the recursion stops z So, if the recursion never stops, the stack eventually runs out of space » and a stack overflow error occurs on most systems Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 14 Recursive Versus Iterative Methods All recursive algorithms/methods can be rewritten without recursion. z Methods rewritten without recursion typically have loops, so they are called iterative methods z Iterative methods generally run faster and use less memory space z So when should you use recursion? » when efficiency is not important and it makes the code easier to understand Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 15 numberOfZeros–A Recursive Method that Returns a Value z takes a single int argument and returns the number of zeros in the number »example: numberOfZeros(2030) returns 2 z uses the following fact: If n is two or more digits long, then the number of zero digits is (the number of zeros in n with the last digit removed) plus an additional 1 if that digit is zero. recursive Examples: » number of zeros in 20030 is number of zeros in 2003 plus 1 for the last zero » number of zeros in 20031 is number of zeros in 2003 plus 0 because last digit is not zero Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 16 numberOfZeros public static int numberOfZeros(int n) { if (n==0) Which is (are) return 1; the base else if (n < 10) // and not 0 case(s)? Why? return 0; // 0 for no zeros else if (n%10 == 0) Which is (are) return (numberOfZeros(n/10) + 1); the recursive else // n%10 != 0 case(s)? Why? return (numberOfZeros(n/10)); } If n is two or more digits long, then the number of zero digits is (the number of zeros in n with the last digit removed) plus an additional 1 if that digit is zero. Chapter 11 Java: an Introduction to Computer Science & Programming - Walter Savitch 17 Recursive public static int numberOfZeros(int n) { Calls if (n==0) return 1; else if (n < 10) // and not 0 Each method return 0; // 0 for no zeros invocation will else if (n%10 == 0) execute one of the return (numberOfZeros(n/10) + 1); if-else cases else // n%10 != 0 shown at right.
Load more

Recommended publications
  • Binary Search Tree
    ADT Binary Search Tree! Ellen Walker! CPSC 201 Data Structures! Hiram College! Binary Search Tree! •" Value-based storage of information! –" Data is stored in order! –" Data can be retrieved by value efficiently! •" Is a binary tree! –" Everything in left subtree is < root! –" Everything in right subtree is >root! –" Both left and right subtrees are also BST#s! Operations on BST! •" Some can be inherited from binary tree! –" Constructor (for empty tree)! –" Inorder, Preorder, and Postorder traversal! •" Some must be defined ! –" Insert item! –" Delete item! –" Retrieve item! The Node<E> Class! •" Just as for a linked list, a node consists of a data part and links to successor nodes! •" The data part is a reference to type E! •" A binary tree node must have links to both its left and right subtrees! The BinaryTree<E> Class! The BinaryTree<E> Class (continued)! Overview of a Binary Search Tree! •" Binary search tree definition! –" A set of nodes T is a binary search tree if either of the following is true! •" T is empty! •" Its root has two subtrees such that each is a binary search tree and the value in the root is greater than all values of the left subtree but less than all values in the right subtree! Overview of a Binary Search Tree (continued)! Searching a Binary Tree! Class TreeSet and Interface Search Tree! BinarySearchTree Class! BST Algorithms! •" Search! •" Insert! •" Delete! •" Print values in order! –" We already know this, it#s inorder traversal! –" That#s why it#s called “in order”! Searching the Binary Tree! •" If the tree is
    [Show full text]
  • Binary Search Trees (BST)
    CSCI-UA 102 Joanna Klukowska Lecture 7: Binary Search Trees [email protected] Lecture 7: Binary Search Trees (BST) Reading materials Goodrich, Tamassia, Goldwasser: Chapter 8 OpenDSA: Chapter 18 and 12 Binary search trees visualizations: http://visualgo.net/bst.html http://www.cs.usfca.edu/~galles/visualization/BST.html Topics Covered 1 Binary Search Trees (BST) 2 2 Binary Search Tree Node 3 3 Searching for an Item in a Binary Search Tree3 3.1 Binary Search Algorithm..............................................4 3.1.1 Recursive Approach............................................4 3.1.2 Iterative Approach.............................................4 3.2 contains() Method for a Binary Search Tree..................................5 4 Inserting a Node into a Binary Search Tree5 4.1 Order of Insertions.................................................6 5 Removing a Node from a Binary Search Tree6 5.1 Removing a Leaf Node...............................................7 5.2 Removing a Node with One Child.........................................7 5.3 Removing a Node with Two Children.......................................8 5.4 Recursive Implementation.............................................9 6 Balanced Binary Search Trees 10 6.1 Adding a balance() Method........................................... 10 7 Self-Balancing Binary Search Trees 11 7.1 AVL Tree...................................................... 11 7.1.1 When balancing is necessary........................................ 12 7.2 Red-Black Trees.................................................. 15 1 CSCI-UA 102 Joanna Klukowska Lecture 7: Binary Search Trees [email protected] 1 Binary Search Trees (BST) A binary search tree is a binary tree with additional properties: • the value stored in a node is greater than or equal to the value stored in its left child and all its descendants (or left subtree), and • the value stored in a node is smaller than the value stored in its right child and all its descendants (or its right subtree).
    [Show full text]
  • A Set Intersection Algorithm Via X-Fast Trie
    Journal of Computers A Set Intersection Algorithm Via x-Fast Trie Bangyu Ye* National Engineering Laboratory for Information Security Technologies, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100091, China. * Corresponding author. Tel.:+86-10-82546714; email: [email protected] Manuscript submitted February 9, 2015; accepted May 10, 2015. doi: 10.17706/jcp.11.2.91-98 Abstract: This paper proposes a simple intersection algorithm for two sorted integer sequences . Our algorithm is designed based on x-fast trie since it provides efficient find and successor operators. We present that our algorithm outperforms skip list based algorithm when one of the sets to be intersected is relatively ‘dense’ while the other one is (relatively) ‘sparse’. Finally, we propose some possible approaches which may optimize our algorithm further. Key words: Set intersection, algorithm, x-fast trie. 1. Introduction Fast set intersection is a key operation in the context of several fields when it comes to big data era [1], [2]. For example, modern search engines use the set intersection for inverted posting list which is a standard data structure in information retrieval to return relevant documents. So it has been studied in many domains and fields [3]-[8]. One of the most typical situations is Boolean query which is required to retrieval the documents that contains all the terms in the query. Besides, set intersection is naturally a key component of calculating the Jaccard coefficient of two sets, which is defined by the fraction of size of intersection and union. In this paper, we propose a new algorithm via x-fast trie which is a kind of advanced data structure.
    [Show full text]
  • Objectives Objectives Searching a Simple Searching Problem A
    Python Programming: Objectives An Introduction to To understand the basic techniques for Computer Science analyzing the efficiency of algorithms. To know what searching is and understand the algorithms for linear and binary Chapter 13 search. Algorithm Design and Recursion To understand the basic principles of recursive definitions and functions and be able to write simple recursive functions. Python Programming, 1/e 1 Python Programming, 1/e 2 Objectives Searching To understand sorting in depth and Searching is the process of looking for a know the algorithms for selection sort particular value in a collection. and merge sort. For example, a program that maintains To appreciate how the analysis of a membership list for a club might need algorithms can demonstrate that some to look up information for a particular problems are intractable and others are member œ this involves some sort of unsolvable. search process. Python Programming, 1/e 3 Python Programming, 1/e 4 A simple Searching Problem A Simple Searching Problem Here is the specification of a simple In the first example, the function searching function: returns the index where 4 appears in def search(x, nums): the list. # nums is a list of numbers and x is a number # Returns the position in the list where x occurs # or -1 if x is not in the list. In the second example, the return value -1 indicates that 7 is not in the list. Here are some sample interactions: >>> search(4, [3, 1, 4, 2, 5]) 2 Python includes a number of built-in >>> search(7, [3, 1, 4, 2, 5]) -1 search-related methods! Python Programming, 1/e 5 Python Programming, 1/e 6 Python Programming, 1/e 1 A Simple Searching Problem A Simple Searching Problem We can test to see if a value appears in The only difference between our a sequence using in.
    [Show full text]
  • Prefix Hash Tree an Indexing Data Structure Over Distributed Hash
    Prefix Hash Tree An Indexing Data Structure over Distributed Hash Tables Sriram Ramabhadran ∗ Sylvia Ratnasamy University of California, San Diego Intel Research, Berkeley Joseph M. Hellerstein Scott Shenker University of California, Berkeley International Comp. Science Institute, Berkeley and and Intel Research, Berkeley University of California, Berkeley ABSTRACT this lookup interface has allowed a wide variety of Distributed Hash Tables are scalable, robust, and system to be built on top DHTs, including file sys- self-organizing peer-to-peer systems that support tems [9, 27], indirection services [30], event notifi- exact match lookups. This paper describes the de- cation [6], content distribution networks [10] and sign and implementation of a Prefix Hash Tree - many others. a distributed data structure that enables more so- phisticated queries over a DHT. The Prefix Hash DHTs were designed in the Internet style: scala- Tree uses the lookup interface of a DHT to con- bility and ease of deployment triumph over strict struct a trie-based structure that is both efficient semantics. In particular, DHTs are self-organizing, (updates are doubly logarithmic in the size of the requiring no centralized authority or manual con- domain being indexed), and resilient (the failure figuration. They are robust against node failures of any given node in the Prefix Hash Tree does and easily accommodate new nodes. Most impor- not affect the availability of data stored at other tantly, they are scalable in the sense that both la- nodes). tency (in terms of the number of hops per lookup) and the local state required typically grow loga- Categories and Subject Descriptors rithmically in the number of nodes; this is crucial since many of the envisioned scenarios for DHTs C.2.4 [Comp.
    [Show full text]
  • Chapter 13 Sorting & Searching
    13-1 Java Au Naturel by William C. Jones 13-1 13 Sorting and Searching Overview This chapter discusses several standard algorithms for sorting, i.e., putting a number of values in order. It also discusses the binary search algorithm for finding a particular value quickly in an array of sorted values. The algorithms described here can be useful in various situations. They should also help you become more comfortable with logic involving arrays. These methods would go in a utilities class of methods for Comparable objects, such as the CompOp class of Listing 7.2. For this chapter you need a solid understanding of arrays (Chapter Seven). · Sections 13.1-13.2 discuss two basic elementary algorithms for sorting, the SelectionSort and the InsertionSort. · Section 13.3 presents the binary search algorithm and big-oh analysis, which provides a way of comparing the speed of two algorithms. · Sections 13.4-13.5 introduce two recursive algorithms for sorting, the QuickSort and the MergeSort, which execute much faster than the elementary algorithms when you have more than a few hundred values to sort. · Sections 13.6 goes further with big-oh analysis. · Section 13.7 presents several additional sorting algorithms -- the bucket sort, the radix sort, and the shell sort. 13.1 The SelectionSort Algorithm For Comparable Objects When you have hundreds of Comparable values stored in an array, you will often find it useful to keep them in sorted order from lowest to highest, which is ascending order. To be precise, ascending order means that there is no case in which one element is larger than the one after it -- if y is listed after x, then x.CompareTo(y) <= 0.
    [Show full text]
  • P4 Sec 4.1.1, 4.1.2, 4.1.3) Abstraction, Algorithms and ADT's
    Computer Science 9608 P4 Sec 4.1.1, 4.1.2, 4.1.3) Abstraction, Algorithms and ADT’s with Majid Tahir Syllabus Content: 4.1.1 Abstraction show understanding of how to model a complex system by only including essential details, using: functions and procedures with suitable parameters (as in procedural programming, see Section 2.3) ADTs (see Section 4.1.3) classes (as used in object-oriented programming, see Section 4.3.1) facts, rules (as in declarative programming, see Section 4.3.1) 4.1.2 Algorithms write a binary search algorithm to solve a particular problem show understanding of the conditions necessary for the use of a binary search show understanding of how the performance of a binary search varies according to the number of data items write an algorithm to implement an insertion sort write an algorithm to implement a bubble sort show understanding that performance of a sort routine may depend on the initial order of the data and the number of data items write algorithms to find an item in each of the following: linked list, binary tree, hash table write algorithms to insert an item into each of the following: stack, queue, linked list, binary tree, hash table write algorithms to delete an item from each of the following: stack, queue, linked list show understanding that different algorithms which perform the same task can be compared by using criteria such as time taken to complete the task and memory used 4.1.3 Abstract Data Types (ADT) show understanding that an ADT is a collection of data and a set of operations on those data
    [Show full text]
  • Binary Search Algorithm This Article Is About Searching a Finite Sorted Array
    Binary search algorithm This article is about searching a finite sorted array. For searching continuous function values, see bisection method. This article may require cleanup to meet Wikipedia's quality standards. No cleanup reason has been specified. Please help improve this article if you can. (April 2011) Binary search algorithm Class Search algorithm Data structure Array Worst case performance O (log n ) Best case performance O (1) Average case performance O (log n ) Worst case space complexity O (1) In computer science, a binary search or half-interval search algorithm finds the position of a specified input value (the search "key") within an array sorted by key value.[1][2] For binary search, the array should be arranged in ascending or descending order. In each step, the algorithm compares the search key value with the key value of the middle element of the array. If the keys match, then a matching element has been found and its index, or position, is returned. Otherwise, if the search key is less than the middle element's key, then the algorithm repeats its action on the sub-array to the left of the middle element or, if the search key is greater, on the sub-array to the right. If the remaining array to be searched is empty, then the key cannot be found in the array and a special "not found" indication is returned. A binary search halves the number of items to check with each iteration, so locating an item (or determining its absence) takes logarithmic time. A binary search is a dichotomic divide and conquer search algorithm.
    [Show full text]
  • Binary Search Algorithm
    Binary search algorithm Definition Search a sorted array by repeatedly dividing the search interval in half. Begin with an interval covering the whole array. If the value of the search key is less than the item in the middle of the interval, narrow the interval to the lower half. Otherwise narrow it to the upper half. Repeatedly check until the value is found or the interval is empty. Generally, to find a value in unsorted array, we should look through elements of an array one by one, until searched value is found. In case of searched value is absent from array, we go through all elements. In average, complexity of such an algorithm is proportional to the length of the array. Divide in half A fast way to search a sorted array is to use a binary search. The idea is to look at the element in the middle. If the key is equal to that, the search is finished. If the key is less than the middle element, do a binary search on the first half. If it's greater, do a binary search of the second half. Performance The advantage of a binary search over a linear search is astounding for large numbers. For an array of a million elements, binary search, O(log N), will find the target element with a worst case of only 20 comparisons. Linear search, O(N), on average will take 500,000 comparisons to find the element. Algorithm Algorithm is quite simple. It can be done either recursively or iteratively: 1. get the middle element; 2.
    [Show full text]
  • Algorithm for Character Recognition Based on the Trie Structure
    University of Montana ScholarWorks at University of Montana Graduate Student Theses, Dissertations, & Professional Papers Graduate School 1987 Algorithm for character recognition based on the trie structure Mohammad N. Paryavi The University of Montana Follow this and additional works at: https://scholarworks.umt.edu/etd Let us know how access to this document benefits ou.y Recommended Citation Paryavi, Mohammad N., "Algorithm for character recognition based on the trie structure" (1987). Graduate Student Theses, Dissertations, & Professional Papers. 5091. https://scholarworks.umt.edu/etd/5091 This Thesis is brought to you for free and open access by the Graduate School at ScholarWorks at University of Montana. It has been accepted for inclusion in Graduate Student Theses, Dissertations, & Professional Papers by an authorized administrator of ScholarWorks at University of Montana. For more information, please contact [email protected]. COPYRIGHT ACT OF 1 9 7 6 Th is is an unpublished manuscript in which copyright sub ­ s i s t s , Any further r e p r in t in g of its contents must be approved BY THE AUTHOR, Ma n sfield L ibrary U n iv e r s it y of Montana Date : 1 987__ AN ALGORITHM FOR CHARACTER RECOGNITION BASED ON THE TRIE STRUCTURE By Mohammad N. Paryavi B. A., University of Washington, 1983 Presented in partial fulfillment of the requirements for the degree of Master of Science University of Montana 1987 Approved by lairman, Board of Examiners iean, Graduate School UMI Number: EP40555 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted.
    [Show full text]
  • Binary Search Algorithm Anthony Lin¹* Et Al
    WikiJournal of Science, 2019, 2(1):5 doi: 10.15347/wjs/2019.005 Encyclopedic Review Article Binary search algorithm Anthony Lin¹* et al. Abstract In In computer science, binary search, also known as half-interval search,[1] logarithmic search,[2] or binary chop,[3] is a search algorithm that finds a position of a target value within a sorted array.[4] Binary search compares the target value to an element in the middle of the array. If they are not equal, the half in which the target cannot lie is eliminated and the search continues on the remaining half, again taking the middle element to compare to the target value, and repeating this until the target value is found. If the search ends with the remaining half being empty, the target is not in the array. Binary search runs in logarithmic time in the worst case, making 푂(log 푛) comparisons, where 푛 is the number of elements in the array, the 푂 is ‘Big O’ notation, and 푙표푔 is the logarithm.[5] Binary search is faster than linear search except for small arrays. However, the array must be sorted first to be able to apply binary search. There are spe- cialized data structures designed for fast searching, such as hash tables, that can be searched more efficiently than binary search. However, binary search can be used to solve a wider range of problems, such as finding the next- smallest or next-largest element in the array relative to the target even if it is absent from the array. There are numerous variations of binary search.
    [Show full text]
  • ECE 2574 Introduction to Data Structures and Algorithms 31
    ECE 2574 Introduction to Data Structures and Algorithms 31: Binary Search Trees Chris Wyatt Electrical and Computer Engineering Recall the binary search algorithm using a sorted list. We can represent the sorted list using a binary tree with a specific relationship among the nodes. This leads to the Binary Search Tree ADT. We can map the sorted list operations onto the binary search tree. Because the insert and delete can use the binary structure, they are more efficient. Better than binary search on a pointer-based (linked) list. Binary Search Tree (BST) Operations Consider the items of type TreeItemType to have an associated key of keyType. // create an empty BST +createBST() // destroy a BST +destroyBST() // check if a BST is empty +isEmpty(): bool Binary Search Tree (BST) Operations // insert newItem into the BST based on its // key value, fails if key exists or node // cannot be created +insert(in newItem:TreeItemType): bool // delete item with searchKey from the BST // fails if no such key exists +delete(in searchKey:KeyItemType): bool // get item corresponding to searchKey from // the BST, fails if no such key exists +retrieve(in searchKey:KeyItemType, out treeItem:TreeItemType): bool Binary Search Tree (BST) Operations // call function visit passing each node data //as the argument, using a preorder //traversal +preorderTraverse(in visit:FunctionType) // call function visit passing each node data // as the argument, using an inorder //traversal +inorderTraverse(in visit:FunctionType) // call function visit passing each node dat a // as the argument, using a postorder // traversal +postorderTraverse(in visit:FunctionType) An interface for Binary Search Trees Template over the key type and the value type.
    [Show full text]