1 BIG-O --- Algorithms zPurpose:Be able to evaluate the relative efficiency of various algorithms...

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BIG-O --- AlgorithmsPurpose: Be able to evaluate

the relative efficiency of various algorithms that are used to process data

We need to be able to evaluate all of the major sort and search algorithms as well as the various implementations of the Abstract Data Types

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Resources: Java Methods Data Structures Chapter 8 p.191

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Intro: As we began to discuss in the lecture on Algorithms, we need to be able to evaluate processes and algorithms based on a uniform criteria.

Big-O provides us with a method for such evaluations

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BIG-O --- Algorithms

Searching (locating an element with a target value in a list of values) and Sorting are 2 tasks used to illustrate the concept of an algorithm

Algorithms typically use iterations or recursion. This property differentiates it from straight forward code

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Algorithms are also generic in nature. The same algorithm applies to a whole set of initial states and produces corresponding final states for each of them.

A sorting algorithm, for example, must apply to any list regardless of the values of its elements

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Furthermore, an algorithm must be independent of the SIZE of the task

( n )

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Analyze the time efficiency and space requirements of algorithms in an abstract way by looking at an algorithm with regards to specific data types and other implementation details

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Criteria to evaluate an algorithm:

Space required

Amount of time

Complexity

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SPACE:

Number and size of simple variablesNumber and total size of

components of compound variableIs space dependent on size of input?

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TIME:

Not necessarily measured in real clock time

Look for operation(comparison)Express time required in terms of

this characteristic operation

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COMPLEXITY:Has little to do with how complex an

algorithm “looks”function of the size(number) of input valuesAverage time

based on probability of the occurrence of inputsWorst time

based on most unfavorable input

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We will focus on time efficiency

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Efficiency of TIME:

Number of Comparisons (if a > b)

Number of Assignments (a = c)

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We will also disregard the specifics of the hardware or the programming language so: we will not be measuring time

efficiency in real time

we will not measure in terms of the number of required program instructions/statements

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We will discuss performance in terms of abstract “steps” necessary to complete a task and we assume that each step takes the same amount of time

We can compare different algorithms that accomplish the same task

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The theory that we can predict the long-term behavior of functions without specific knowledge of the exact constants used in describing the function allows us to ignore constant factors in the analysis of execution time

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BIG-O --- Big-O Notation

We can evaluate algorithms in terms of Best Case , Worst Case and Average Case

We assume that the number of “steps” or n is a large number

Analyze loops especially nested loops

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Sequential Search algorithms grow linearly with the size (number) of the elements

We match the target value against each array value until a match is found or the entire array is scanned

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Sequential Search Worst Case is we locate the target

element in the last element

Best Case is the target value is found on the first attempt

Average Case finds the target value in the middle of the array

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Binary Search algorithms (compared as applied to the same task as the sequential search) grow logarithmicly with the size (number) of the elements

Elements must be ordered

We compare the target value against the middle element of the array and proceed left (if smaller) or right (larger) until a match if found

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Binary Search

For example, where n=7, we try a[3] then proceed left or right

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Binary SearchWorst Case , Average Case is where

we locate the target element in (3 ) log n comparisons (the average case is only one less than the worst case)

Best Case is the target value is found on the first attempt

THE execution time of a Binary Search is approx. proportional to the log of n

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(A) Linear Growth (B) Logarithmic Growth Logarithmic Growth is SLOWER than Linear

Growth Asymptotically, a Binary Search is FASTER

than a Sequential Search as Linear Time eventually surpasses Logarithmic Time

(A)

(B)

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Big-O represents the Order of Growth for an Algorithm

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Growth Rate Functions:

reference functions used to compare the rates of growth of algorithms

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BIG-O --- Big-O Notation Growth Rate Functions

O(1) Constant Time --- time required to process 1 set of steps

The algorithm requires the same fixed number of steps regardless of the size of the task: Push and Pop Stack operations Insert and Remove Queue

operations Finding the median value in a

sorted Array

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O(n) Linear Time --- increase in time is constant for a larger n (number of tasks)

The algorithm requires a number of steps proportional to the size of the task

20 tasks = 20

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O(n) Examples: Traversal of a List Finding min or max element in a List

Finding min or max element in a sequential search of unsorted elements

Traversing a Tree with n nodes

Calculating n-factorial, iterativly

Calculating nth Fibonacci number, iteratively

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O(n^2) Quadratic TimeThe number of operations is

proportional to the size of the task SQUARED

20 tasks = 400

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O(n^2) Examples:

Simplistic Sorting algorithms such as a Selection Sort of n elements

Comparing 2 2-Dimensional arrays, matrics, of size n by n

Finding Duplicates in an unsorted list of n elements (implemented with 2 nested loops)

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O(log n) Logarithmic Time --- the log of n is significantly lower than n

20 tasks = 4.3

Used in many “divide and conquer” algorithms, like binary search, and is the basis for using binary search trees and heaps

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O(log n)for example, a binary search tree

of one million elements would take, at most, 20 steps to locate a target

Binary Search in a Sorted list of n elements

Insert and Find Operations for a Binary Search Tree with n nodes

Insert and Find Operations for a Heap with n nodes

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O(n log n) “n log n” Time

20 tasks = 86.4

More advanced sorting algorithms like Quicksort, Mergesort (which will be discussed in detail in the next chapter)

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O(a^n) (a > 1) Exponential Time

20 tasks = 12^20 = very large number

Recursive Fibonacci implementation (a > 3/2)

Towers of Hanoi (a=2) Generating all permutations of n symbols

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The best time in the preceding growth rate functions is constant time O(1)

The worst time in the preceding growth rate functions is exponential time O(a^n) which quickly Overwhelms even the fastest computers even for a relatively small n

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Polymonial Growth:Linear, quadratic, cubic…

The Highest Power of N Dominates the Polymonial (see Ill Lam SDT p.42 Top)

is considered manageable as compared to exponential growth

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Linear

t

n

Log n

Exponential

QuadraticN log n

Constant O(1)

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Log n has a slower asymptotic growth rate when compared to linear growth as a thousand fold increase in the size of the task , n , results in a fixed, moderate increase in the number of operations required.

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For a given ALGORITHM, you can see how it falls on the following grid to determine its “Order of Growth” or time efficiency.

Use this grid as a “Rule of Thumb” when evaluating the BIG-O of an algorithm.

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Let this grid help narrow down possible solutions, but make sure you “memorize” the other charts and use them when attacking an order of growth problem

BIG-O Analysis handout has an example where the “rule of thumb” will result in an incorrect assumption

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BIG-O --- Big-O Notation Rule of Thumb

ALGORITHM | SINGLE LOOP | NESTED LOOP(S)

____________ |________________________ |__________________STRAIGHT | LINEAR |

QUADRATICFORWARD | O(N) | O(N^2)PROCESSING | | 0(N^3)…(SEQUENTIAL) | |____________ |________________________|___________________ |

|DIVIDE AND | LOGARITHMIC | N LOG NCONQUER | O(LOG N) | O(N LOG N)PROCESSING | |

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Sample Test Times; N = 50,000FUNCTION Running TimeLog N (Log Time) 15.6 (Binary Search)

N (Linear Time) 50,000 (L’List or Tree Traversal, Sequential Search)

N Log N 780,482 (Quick, MergeSort)

N^2 (Quadratic Time) 2.5 * 10^9 (Selection Sort,

Matrics, 2 nested loops)

a^N (Exponential ) 3.8 * 10^21 (recursion, fibonacci, permutations)

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REVIEW 3 EXAMPLES IN THE HANDOUT:

Lambert p.40-41 Examples 1.7, 1.8 &

1.9

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PROJECTS:

BIG-O Exercises 1 through 5

Workbook problems 12 through 20

Multiple Choice Problems

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TEST IS THE DAY AFTER THE LABS ARE DUE