Stacks.

Slides:



Advertisements
Similar presentations
STACKS & QUEUES. Stacks Abstract data types An abstract data type (ADT) is an abstraction of a data structure An ADT specifies : –Data stored –Operations.
Advertisements

Stacks.
Stacks. Queues. Double-Ended Queues. 2 CPSC 3200 University of Tennessee at Chattanooga – Summer 2013 © 2010 Goodrich, Tamassia.
Stacks. 2 Outline and Reading The Stack ADT (§4.2.1) Applications of Stacks (§4.2.3) Array-based implementation (§4.2.2) Growable array-based stack.
© 2004 Goodrich, Tamassia Stacks. © 2004 Goodrich, Tamassia Stacks2 The Stack ADT (§4.2) The Stack ADT stores arbitrary objects Insertions and deletions.
© 2004 Goodrich, Tamassia Stacks. © 2004 Goodrich, Tamassia Stacks2 Applications of Stacks Direct applications Delimiter matching Undo sequence in a text.
© 2004 Goodrich, Tamassia Stacks. © 2004 Goodrich, Tamassia Stacks2 Abstract Data Types (ADTs) An abstract data type (ADT) is an abstraction of a data.
Stacks, Queues, and Deques
Elementary Data Structures Stacks, Queues, & Lists Amortized analysis Trees.
Stacks and QueuesCSC311: Data Structures1 Chapter 5 Stacks and Queues Objectives –Stacks and implementations –Queues and implementations –Double-ended.
Stacks. 2 Outline and Reading The Stack ADT (§2.1.1) Array-based implementation (§2.1.1) Growable array-based stack (§1.5) Java.util.Stack class Java.util.Vector.
Part-B1 Stacks. Stacks2 Abstract Data Types (ADTs) An abstract data type (ADT) is an abstraction of a data structure An ADT specifies: Data stored Operations.
Part-B1 Stacks. Stacks2 Abstract Data Types (ADTs) An abstract data type (ADT) is an abstraction of a data structure An ADT specifies: Data stored Operations.
Stacks. 2 Outline and Reading The Stack ADT (§2.1.1) Applications of Stacks (§2.1.1) Array-based implementation (§2.1.1) Growable array-based stack (§1.5)
Stacks1 CS2468 Data Structures and Data Management Lecturer: Lusheng Wang Office: B6422 Phone:
Stacks. week 2a2 Outline and Reading The Stack ADT (§4.1) Applications of Stacks Array-based implementation (§4.1.2) Growable array-based stack Think.
Stacks. 2 What Are Stacks ? PUSHPOP 0 MAX Underflow Overflow.
Stacks © 2010 Goodrich, Tamassia1Stacks. 2 Abstract Data Types (ADTs)  An abstract data type (ADT) is an abstraction of a data structure  An ADT specifies:
Abstract Data Type (ADT) & Stacks
Stacks and Linked Lists. Abstract Data Types (ADTs) An ADT is an abstraction of a data structure that specifies – Data stored – Operations on the data.
Stacks 1. Stack  What is a stack? An ordered list where insertions and deletions occur at one end called the top. Also known as last-in-first-out (LIFO)
Stack. Abstract Data Types (ADTs) An abstract data type (ADT) is an abstraction of a data structure An ADT specifies: Data stored Operations on the data.
Stacks & Queues EECS: Stacks & Queues Stacks April 23, 2017
© 2004 Goodrich, Tamassia Stacks. © 2004 Goodrich, Tamassia Stacks2 Abstract Data Types (ADTs) An abstract data type (ADT) is an abstraction of a data.
Stacks. A stack is a data structure that holds a sequence of elements and stores and retrieves items in a last-in first- out manner (LIFO). This means.
30 May Stacks (5.1) CSE 2011 Winter Stacks2 Abstract Data Types (ADTs) An abstract data type (ADT) is an abstraction of a data structure An.
Dynamic Arrays and Stacks CS 244 Brent M. Dingle, Ph.D. Game Design and Development Program Department of Mathematics, Statistics, and Computer Science.
Min Chen School of Computer Science and Engineering Seoul National University Data Structure: Chapter 3.
Lecture6: Stacks Bohyung Han CSE, POSTECH CSED233: Data Structures (2014F)
© 2004 Goodrich, Tamassia Vectors1 Vectors and Array Lists.
Array Lists1 © 2010 Goodrich, Tamassia. Array Lists2 The Array List ADT  The Array List ADT extends the notion of array by storing a sequence of arbitrary.
Parasol Lab, Dept. CSE, Texas A&M University
Welcome to CSCE 221 – Data Structures and Algorithms
© 2004 Goodrich, Tamassia Stacks. © 2004 Goodrich, Tamassia Stacks2 Abstract Data Types (ADTs) An abstract data type (ADT) is an abstraction of a data.
CH 5 : STACKS, QUEUES, AND DEQUES ACKNOWLEDGEMENT: THE SLIDES ARE PREPARED FROM SLIDES PROVIDED WITH DATA STRUCTURES AND ALGORITHMS IN C++, GOODRICH, TAMASSIA.
Lists1 © 2010 Goodrich, Tamassia. Position ADT  The Position ADT models the notion of place within a data structure where a single object is stored 
Stack. ADS2 Lecture 1010 The Stack ADT (GoTa §5.1) The Stack ADT stores arbitrary objects Insertions and deletions follow the last-in.
© 2004 Goodrich, Tamassia Stacks. © 2004 Goodrich, Tamassia Stack: Last In First Out (LIFO).–Used in procedure calls, to compute arithmetic expressions.
Stacks Presentation for use with the textbook Data Structures and Algorithms in Java, 6 th edition, by M. T. Goodrich, R. Tamassia, and M. H. Goldwasser,
Stacks 1/25/2018 Presentation for use with the textbook Data Structures and Algorithms in Java, 6th edition, by M. T. Goodrich, R. Tamassia, and M. H.
Stacks (and Queues).
Stacks 5/2/2018 Presentation for use with the textbook Data Structures and Algorithms in Java, 6th edition, by M. T. Goodrich, R. Tamassia, and M. H. Goldwasser,
Lists and Iterators 5/3/2018 Presentation for use with the textbook Data Structures and Algorithms in Java, 6th edition, by M. T. Goodrich, R. Tamassia,
Stacks Stacks.
CSCI 3333 Data Structures Stacks.
Stacks.
Stacks 9/12/2018 Presentation for use with the textbook Data Structures and Algorithms in Java, 6th edition, by M. T. Goodrich, R. Tamassia, and M. H.
Queues 11/9/2018 6:28 PM Queues 11/9/2018 6:28 PM Queues.
Stacks © 2013 Goodrich, Tamassia, Goldwasser Stacks.
Stacks.
Queues 11/16/2018 4:18 AM Queues 11/16/2018 4:18 AM Queues.
Queues 11/16/2018 4:19 AM Queues 11/16/2018 4:19 AM Queues.
Lists and Iterators 3/9/15 Presentation for use with the textbook Data Structures and Algorithms in Java, 6th edition, by M. T. Goodrich, R. Tamassia,
Queues 11/22/2018 6:47 AM 5.2 Queues Queues Dr Zeinab Eid.
Vectors 11/23/2018 1:03 PM Growing Arrays Vectors.
Chapter 5 Stacks and Queues 11/28/2018 Stacks.
" A list is only as strong as its weakest link. " - Donald Knuth
Stacks 12/7/2018 Presentation for use with the textbook Data Structures and Algorithms in Java, 6th edition, by M. T. Goodrich, R. Tamassia, and M. H.
Copyright © Aiman Hanna All rights reserved
Queues 12/30/2018 9:24 PM Queues 12/30/2018 9:24 PM Queues.
Recall What is a Data Structure Very Fundamental Data Structures
Stacks Abstract Data Types (ADTs) Stacks
Stacks and Queues DSA 2013 Stacks n Queues.
Stacks.
Stacks.
Computing Spans Given an an array X, the span S[i] of X[i] is
Lecture 8: Stacks, Queues
Vectors and Array Lists
Stacks and Linked Lists
Presentation transcript:

Stacks

Outline and Reading The Stack ADT (§2.1.1) Applications of Stacks (§2.1.1) Array-based implementation (§2.1.1) Growable array-based stack (§1.5) Stacks

Abstract Data Types (ADTs) An abstract data type (ADT) is an abstraction of a data structure An ADT specifies: Data stored Operations on the data Error conditions associated with operations Example: ADT modeling a simple stock trading system The data stored are buy/sell orders The operations supported are order buy(stock, shares, price) order sell(stock, shares, price) void cancel(order) Error conditions: Buy/sell a nonexistent stock Cancel a nonexistent order Stacks

The Stack ADT The Stack ADT stores arbitrary objects Insertions and deletions follow the last-in first-out scheme Think of a spring-loaded plate dispenser Main stack operations: push(object): inserts an element object pop(): removes and returns the last inserted element Auxiliary stack operations: object top(): returns the last inserted element without removing it integer size(): returns the number of elements stored boolean isEmpty(): indicates whether no elements are stored Stacks

Exceptions Attempting the execution of an operation of ADT may sometimes cause an error condition, called an exception Exceptions are said to be “thrown” by an operation that cannot be executed In the Stack ADT, operations pop and top cannot be performed if the stack is empty Attempting the execution of pop or top on an empty stack throws an EmptyStackException Stacks

Applications of Stacks Direct applications Page-visited history in a Web browser Undo sequence in a text editor Chain of method calls in the Java Virtual Machine Indirect applications Auxiliary data structure for algorithms Component of other data structures Stacks

Method Stack in the JVM main() { int i = 5; foo(i); } foo(int j) { int k; k = j+1; bar(k); } bar(int m) { … } The Java Virtual Machine (JVM) keeps track of the chain of active methods with a stack When a method is called, the JVM pushes on the stack a frame containing Local variables and return value Program counter, keeping track of the statement being executed When a method ends, its frame is popped from the stack and control is passed to the method on top of the stack bar PC = 1 m = 6 foo PC = 3 j = 5 k = 6 main PC = 2 i = 5 Stacks

Array-based Stack Algorithm size() return t + 1 Algorithm pop() if isEmpty() then throw EmptyStackException else t  t  1 return S[t + 1] A simple way of implementing the Stack ADT uses an array We add elements from left to right A variable keeps track of the index of the top element … S 1 2 t Stacks

Array-based Stack (cont.) The array storing the stack elements may become full A push operation will then throw a FullStackException Limitation of the array-based implementation Not intrinsic to the Stack ADT Algorithm push(o) if t = S.length  1 then throw FullStackException else t  t + 1 S[t]  o S 1 2 t … Stacks

Performance and Limitations Let n be the number of elements in the stack The space used is O(n) Each operation runs in time O(1) Limitations The maximum size of the stack must be defined a priori and cannot be changed Trying to push a new element into a full stack causes an implementation-specific exception Stacks

Computing Spans We show how to use a stack as an auxiliary data structure in an algorithm Given an an array X, the span S[i] of X[i] is the maximum number of consecutive elements X[j] immediately preceding X[i] and such that X[j]  X[i] Spans have applications to financial analysis E.g., stock at 52-week high X 6 3 4 5 2 1 S Stacks

Quadratic Algorithm Algorithm spans1(X, n) Input array X of n integers Output array S of spans of X # S  new array of n integers n for i  0 to n  1 do n s  1 n while s  i  X[i - s]  X[i] 1 + 2 + …+ (n  1) s  s + 1 1 + 2 + …+ (n  1) S[i]  s n return S 1 Algorithm spans1 runs in O(n2) time Stacks

Computing Spans with a Stack We keep in a stack the indices of the elements visible when “looking back” We scan the array from left to right Let i be the current index We pop indices from the stack until we find index j such that X[i]  X[j] We set S[i]  i - j We push x onto the stack Stacks

Linear Algorithm Each index of the array Is pushed into the stack exactly one Is popped from the stack at most once The statements in the while-loop are executed at most n times Algorithm spans2 runs in O(n) time Algorithm spans2(X, n) # S  new array of n integers n A  new empty stack 1 for i  0 to n  1 do n while (A.isEmpty()  X[top()]  X[i] ) do n j  A.pop() n if A.isEmpty() then n S[i]  i + 1 n else S[i]  i - j n A.push(i) n return S 1 Stacks

Growable Array-based Stack In a push operation, when the array is full, instead of throwing an exception, we can replace the array with a larger one How large should the new array be? incremental strategy: increase the size by a constant c doubling strategy: double the size Algorithm push(o) if t = S.length  1 then A  new array of size … for i  0 to t do A[i]  S[i] S  A t  t + 1 S[t]  o Stacks

Comparison of the Strategies We compare the incremental strategy and the doubling strategy by analyzing the total time T(n) needed to perform a series of n push operations We assume that we start with an empty stack represented by an array of size 1 We call amortized time of a push operation the average time taken by a push over the series of operations, i.e., T(n)/n Stacks

Incremental Strategy Analysis We replace the array k = n/c times The total time T(n) of a series of n push operations is proportional to n + c + 2c + 3c + 4c + … + kc = n + c(1 + 2 + 3 + … + k) = n + ck(k + 1)/2 Since c is a constant, T(n) is O(n + k2), i.e., O(n2) The amortized time of a push operation is O(n) Stacks

Doubling Strategy Analysis We replace the array k = log2 n times The total time T(n) of a series of n push operations is proportional to n + 1 + 2 + 4 + 8 + …+ 2k = n + 2k + 1 -1 = 2n -1 T(n) is O(n) The amortized time of a push operation is O(1) geometric series 1 2 4 8 Stacks

Stack Interface in Java public interface Stack { public int size(); public boolean isEmpty(); public Object top() throws EmptyStackException; public void push(Object o); public Object pop() throws EmptyStackException; } Java interface corresponding to our Stack ADT Requires the definition of class EmptyStackException Different from the built-in Java class java.util.Stack Stacks

Array-based Stack in Java public class ArrayStack implements Stack { // holds the stack elements private Object S[ ]; // index to top element private int top = -1; // constructor public ArrayStack(int capacity) { S = new Object[capacity]); } public Object pop() throws EmptyStackException { if isEmpty() throw new EmptyStackException (“Empty stack: cannot pop”); Object temp = S[top]; // facilitates garbage collection S[top] = null; top = top – 1; return temp; } Stacks