Abstract Datatypes and Data Structures: Stacks, Queues, ...

(抽象データ型とデータ構造、スタック、キューなど)

Data Structures and Algorithms

4th lecture, October 24, 2019

http://www.sw.it.aoyama.ac.jp/2019/DA/lecture4.html

Martin J. Dürst

© 2009-19 Martin J. Dürst 青山学院大学

Today's Schedule

• Summary and homework of last lecture
• Polynomial vs. exponential time
• Finding the (asymptotic) time complexity of an algorithm
• Recurrence relations
• Abstract Data Types
  • Stack
  • Queue

Summary of Last Lecture

The asymptotic growth (order of growth) of a function and the time (and space) complexity of an algorithm can be expressed with the Big-O/Ω/Θ notation:

• O(g(n)): Set of functions with lower or same order of growth as g(n)
  
• Ω(g(n)): Set of functions with larger or same order of growth as g(n)
• Θ(g(n)): Set of functions with same order of growth as g(n)

f(n)∈O(g(n)) ⇔ ∃c>0: ∃n₀≥0: ∀n≥n₀: f(n)≤c·g(n)

The order of growth of a function can be found by:

• Looking for appropriate c and n₀
• Calculating lim_n→∞(f(n)/g(n))
• Simplification (e.g. O(5n²+30n+200) → O(n²); O(17 log₅ n) → O(log n))

When using Big-O notation, always try to simplify g() as much as possible.

Last Week's Homework

(no need to submit)

Review this lecture's material every day!

On the Web, find algorithms with time complexity O(1), O(log n), O(n), O(n log n), O(n²), O(n³), O(2ⁿ), O(n!), and so on.

Frequent Orders

• O(1): Simple formulæ (e.g. interest calculation), initialization
• O(log n) (logarithmic order/time): binary search, other "divide and conquer" algorithms
• O(n) (linear order/time): proportional to size of data, checking all data items once (or a finite number of times)
• O(n log n): Many sorting algorithms, other "divide and conquer" algorithms
• O(n²) (quadratic order/time), O(n³) (cubic order/time): Considering all/most combinations of 2 or 3 data items
• O(2ⁿ): Considering all/most subsets of data items
• O(n!): Considering all/most permutations of data items

Example of other order: O(n^2.373), fastest known algorithm for matrix multiplication

Polynomial versus Exponential Growth

Example:

1.1ⁿ ≶ n²⁰

log(1.1)·n ≶ log(n)·20

n/log₁₀(n) ≶ 20/log₁₀(1.1) ≊483.2

n₀ ≊ 1541

Conclusion: For any a, b > 1, aⁿ will always eventually grow faster than n^b (O(n^b)⊊O(aⁿ))

(n^b is polynominal, aⁿ is exponential)

The Importance of Polynomial Time

• What can be called a "realistic" time complexity depends on the problem
• In general:
  • Polynomial time is realistic
  • Exponential time is unrealistic

[We will discuss this in more detail in lecture 14]

Finding the (Asymptotic) Time Complexity of an Algorithm

1. Find/define the variables that determine the size of the input (e.g. n)
2. Find the basic operations (steps) in the algorithm that are most frequently executed
3. Express the total number of basic operations (steps) using summation or a recurrence relation
4. Determine the time complexity expressed with big-O notation

Simplifications possible for big-O notation can be applied early.
Example: Because constant factors are irrelevant in big-O notation, they can be eliminated when counting steps.

How to Define Input Size Variables

• In many cases, the input size is the number of data items (examples: search, sort)
• For matrices, ..., often the number of rows or columns is used (matrices of size n × n or n × m)
• In some cases, the size of individual data items has to be considered
  Examples: Size in bits of integers with unlimited precision; length of variable-length strings, ...
• Sometimes, there are two or more kinds of data, with different size
  Example: String matching (text size n and pattern size m, lecture 11)

How to Identify the Most Frequent Basic Operations

• Usually inside a loop (especially inside multiple loops)
• If there are several independent loops, check all of them
• If the number of operations depends on the values in the input, check the worst case
  Example: For search, time if value is not found
• When methonds/functions are called, consider the content of the function
  

Caution: Some methods/functions may hide complexity (e.g. Ruby sort, ...)

Counting Basic Operations using Summation

• Example program:

  for (i=0; i<n; i++)
      for (j=i; j<n; j++)
          sum += i*j;

• Most frequent operation: Addition or multiplication in last line (inner loop)
• Expressing the number of operations as a sum:
  ∑_i=0^n-1 ∑_j=i^n-1 1
• Evaluating the sum:
  ∑_i=0^n-1 ∑_j=i^n-1 1 = ∑_i=0^n-1 n-i =
  = n + n-1 + n-2 + ... + 2 + 1 = n · (n+1) / 2
• Asymptotic time complexity:
  n · (n+1) / 2 = 0.5 n² + 0.5 n∈ O(n²)

Counting Basic Operations using Recurrence Relations

• Example program (recursive version of binary search):
  

  def binsearch(array, low, high, key)
    middle = (high+low)/2
    if low>=high
      array[low]==key ? low : nil
    elsif key>array[middle]
      binsearch(array, middle+1, high, key)
    else
      binsearch(array, low, middle, key)
    end
  end
  

• Expressing the number of operations as a recurrence:
  B(1) = 1
  B(n) = B(⌈n/2⌉) + 1
  (⌈⌉: ceiling function)

Recurrence Relations

• A recurrence (relation) is a recursive definition of a mathematical function
• There are several ways to solve recurrences
• One way to solve a recurrence is to discover a pattern by repeated substitution:
  B(n) = B(⌈n/2⌉) + 1 = B(⌈⌈n/2⌉/2⌉) + 1 + 1 = B(⌈n/2²⌉) + 2 =
  = B(⌈n/2³⌉) + 3 = B(⌈n/2^k⌉) + k
• Using B(1) = 1:
  ⌈n/2^k⌉ = 1 ⇒ 1 ≥ n/2^k (>1/2) ⇒ 2^k ≥ n (> 2^k-1) ⇒ k ≥ log₂ n (> k-1) ⇒ k = ⌈log₂ n⌉
• B(n) = 1 + ⌈log₂ n⌉ ∈ O(log n)
• The asymptotic time complexity of binary search is O(log n)

Comparing the Execution Time of Algorithms

(from previous lectures)

Possible questions:

• How many seconds faster is binary search when compared to linear search?
• How many times faster is binary search when compared to linear search?
• What is the order [of growth of the execution time] of linear search and binary search?
  Linear search is O(n), binary search is O(log n).

Conclusion: Expressing time complexity as O() allows to evaluate the essence of an algorithm, ignoring hardware and implementation differences.

Abstract Data Type (ADT)

• Combination of data with functions operating on data
• The data can only be accessed/changed using the functions (encapsulation)
• Goals
  • Data integrity (examples: birthday and age; bank account)
  • Modularization of big software projects
• Related to type theory
• Often implemented by objects in object-oriended programming languages
  • Type → class
  • Function → member function/method

Typical Examples of Abstract Data Types

• Stack
• Queue
• Linear list
• Dictionary
  Caution: A dictionary ADT is not the same as a book dictionary
• Priority queue

Stack

Principle:

last-in-first-out (LIFO)

General example:

Stack of trays in cafeteria

Example from IT:

Function stack (local variables, return address, ...)

Main methods:

new, add/push, delete/pop

Other methods:

empty? (check whether the stack is empty or not)
top (return the topmost element without removing it from the stack)

Axioms for Stacks

It is possible to define a stack using the following four axioms:

1. Stack.new.empty? ↔ true
2. s.push(e).empty? ↔ false
3. s.push(e).top ↔ e
4. s.push(e).pop ↔ s (here, pop returns the new stack, not the top element)

(s is any arbitrary stack, e is any arbitrary data item)

Axioms can define a contract between implementation and users

Queue

Principle:

first-in-first-out (FIFO)

General example:

Queue in cafeteria waiting for food

Example from IT:

Queue of processes waiting for execution

Main methods:

add/enqueue, remove/delete/dequeue

Explain the meaning of GIGO: Garbage in, garbage out.

Comparing ADTs

Implementation: 4ADTs.rb; some complexities can be improved by using additional variables

Summary

• The order (of growth)/(asymptotic) time complexity of an algorithm can be calculated from the number of the most frequent basic operations
• Calculation can use a summation or a recurrence (relation)
• The big-O notation compactly express the inherent efficiency of an algorithm
• An abstract data type (ADT) combines data and the operations on this data
• Stack and queue are typical examples of ADTs

Homework

(no need to submit)

1. Order the following orders of growth, and explain the reason for your order:

   O(n²), O(n!), O(n log log n), O(n log n), O(20ⁿ)

2. Write a simple program that uses the classes in 4ADTs.rb.
   Use this program to compare the implementations.
   Hint: Use the second part of 2search.rb as an example.
3. Implement the priority queue ADT (use Ruby or any other programming language)

   A priority queue keeps a priority (e.g. integer) for each data item.
   In the simplest case, the only data is the priority.
   The items with the highest priority leave the queue first.
   Your implementation can use an array or a linked list or any other data structure.

Glossary

polynomial growth

多項式増加

exponential growth

指数的増加

integers with unlimited precision

非固定長整数

summation

総和

recurrence (relation)

漸化式

ceiling function

天井関数

substitution

置換

abstract data type

抽象データ型

encapsulation

カプセル化

data integrity

データの完全性

modularization

モジュール化

type theory

型理論

object-oriended

オブジェクト指向 (形容詞)

type

型

class

クラス

member function

メンバ関数

method

メソッド

stack

スタック

cafeteria

食堂

axiom

公理

queue

待ち行列、キュー

ring buffer

リングバッファ

priority queue

順位キュー、優先順位キュー、優先順位付き待ち行列