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### Representing Algorithms

Objectives

In this chapter, you will learn about:

- Representing algorithms
- Examples of algorithmic problem solving

Introduction

- This chapter discusses algorithms and algorithmic problem solving using three problems:
- Searching lists
- Finding maxima and minima
- Matching patterns

Natural Language - too verbose, ambiguous and lacks structure.

Formal programming languages – forces low level details, punctuation, grammar syntax.

Pseudocode - English like statements, very readable, no grammatical rules, etc., is easily translated into a programming language.

Representing Algorithms

- Natural language
- Language spoken and written in everyday life
- Examples: English, Spanish, Arabic, etc.
- Problems with using natural language for algorithms
- Verbose
- Imprecise
- Relies on context and experiences

to give precise meaning to a word or phrase

Representing Algorithms

- High-level programming language
- Examples: C++, Java
- Problem with using a high-level programming language for algorithms
- During the initial phases of design, we are forced to deal with detailed language issues

The Beginning of the Addition Algorithm

Expressed in a High-Level Programming Language

Representing Algorithms

- Pseudocode
- English language constructs modeled to look like statements available in most programming languages
- Steps presented in a structured manner (numbered, indented, etc.)
- No fixed syntax for most operations is required

Representing Algorithms

- Pseudocode ( continued)
- Less ambiguous and more readable than natural language
- Emphasis is on process, not notation
- Well-understood forms allow logical reasoning about algorithm behavior
- Can be easily translated into a programming language

Types of algorithmic operations

- Sequential
- Conditional
- Iterative

Sequential Operations

- Sequential algorithm
- Also called straight-line algorithm
- Executes its instructions in a straight line from top to bottom and then stops
- Computation

Set the value of “variable” to “arithmetic expression”

where a Variable is a named storage location that can hold a data value

For Example:

Set the value of c to a+b

or Add a and b to get c

Sequential Operations

- Input operations
- To receive data values from the outside world
- Examples:

Get values for “variable1”, ‘variable2”,...

Get a value for r, the radius of the circle

- Output operations
- To send results to the outside world for display
- Examples:

Print values for “variable1”, “variable2”,...

Print the value of Area

Conditional and Iterative Operations

- Control operations
- Conditional operations
- “Question asking” operations
- Often use “if”
- Iterative operations
- Looping operations
- Often use while or repeat

Conditional Operations

- Conditional operations
- Ask questions and choose alternative actions based on the answers
- Also called branch or decision operations
- Example
- if x is greater than 25 then

print x

else

print x times 100

Iterative Operations

- Iterative operations
- Perform “looping” behavior; repeating actions until a continuation condition becomes false
- Loop
- The repetition of a block of instructions

Iterative Operations [loops]

Pseudocode ( Counting Loop – For Loop)

Repeat step I to step J until the “True/False” condition becomes true

Step I: operation

……..

Step J :operation

Iterative Operations [loops]

Pseudocode ( While loop)

Repeat while a “True/False” Condition is false

operation

…..

operation

End of the loop

Iterative Operations [loops]

Pseudocode (Repeat Until Loop)

Repeat until a “True/False” Condition becomes true

operation

…..

operation

End of the loop

Iterative Operations (loops)

- Examples
- For J = 1 to 10

print J

- while j > 0 do

set s to s + aj

set j to j - 1

- repeat

print ak

set k to k + 1

until k > n

Iterative Operations [ loops]

- Components of a loop
- Continuation condition
- Loop body
- Infinite loop
- The continuation condition never becomes false
- An error

Iterative Operations [ loops]

- Pretest loop
- Continuation condition tested at the beginning of each pass through the loop
- It is possible for the loop body to never be executed
- While loop

Iterative Operations [ loops]

- Posttest loop
- Continuation condition tested at the endof loop body
- Loop body must be executed at least once
- Do/While loop

Example 1: Go Forth and Multiply

A Simple looping problem

Multiply two numbers using repeated addition

- Get values for a and b
- Determine the value of a+a+a+… a+ (b times)
- Use a loop that executes b times and adds the value of a to a total each time
- Print the value of the product

Example 2: Looking, Looking, Looking

- Examples of algorithmic problem solving
- Sequential search: find a particular value in an unordered collection
- Find maximum: find the largest value in a collection of data
- Pattern matching: determine if and where a particular pattern occurs in a piece of text

Example 2: Looking, Looking, Looking (continued)

- Task
- Find a particular person’s name from an unordered list of telephone subscribers
- Algorithm outline
- Start with the first entry and check its name, then repeat the process for all entries

Example 2: Looking, Looking, Looking (continued)

- Algorithm discovery
- Finding a solution to a given problem
- Naïve sequential search algorithm
- For each entry, write a separate section of the algorithm that checks for a match
- Problems
- Only works for collections of exactly one size
- Duplicates the same operations over and over

Example 2: Looking, Looking, Looking (continued)

- Correct sequential search algorithm
- Uses iteration to simplify the task
- Refers to a value in the list using an index (or pointer)
- Handles special cases (like a name not found in the collection)
- Uses the variable Found to exit the iteration as soon as a match is found

Example 2: Looking, Looking, Looking (continued)

- The selection of an algorithm to solve a problem is greatly influenced by the way the data for that problem are organized

Example 3: Big, Bigger, Biggest

- Task
- Find the largest value from a list of values
- Algorithm outline
- Keep track of the largest value seen so far (initialized to be the first in the list)
- Compare each value to the largest seen so far, and keep the larger as the new largest

Example 3: Big, Bigger, Biggest (continued)

- Once an algorithm has been developed, it may itself be used in the construction of other, more complex algorithms
- Algorithms can be used as the building blocks of software
- Library
- A collection of useful algorithms
- An important tool in algorithm design and development

Example 3: Big, Bigger, Biggest (continued)

- Find Largest algorithm
- Uses iteration and indices like previous example
- Updates location and largest so far when needed in the loop

Example 4: Meeting Your Match

- Task
- Find if and where a pattern string occurs within a longer piece of text
- Algorithm outline
- Try each possible location of pattern string in turn
- At each location, compare pattern characters against string characters

Example 4: Meeting Your Match (continued)

- Abstraction
- Separating high-level view from low-level details
- Key concept in computer science
- Makes difficult problems intellectually manageable
- Allows piece-by-piece development of algorithms

Example 4: Meeting Your Match (continued)

- Top-down design
- When solving a complex problem:
- Create high-level operations in first draft of an algorithm
- After drafting the outline of the algorithm, return to the high-level operations and elaborate each one
- Repeat until all operations are primitives

Example 3: Meeting Your Match (continued)

- Pattern-matching algorithm
- Contains a loop within a loop
- External loop iterates through possible locations of matches to pattern
- Internal loop iterates through corresponding characters of pattern and string to evaluate match

Summary

- Algorithm design is a first step in developing an algorithm
- Must also:
- Ensure the algorithm is correct
- Ensure the algorithm is sufficiently efficient
- Pseudocode is used to design and represent algorithms

Pattern Matching

- Some applications
- Matching phrases in Literature or text
- Find a pattern and replace it with another
- Web search ( “Google”)- matching strings
- Patterns in X-rays, CAT scans, etc.
- Patterns in the human genome – whole new field of bioinformatics

Summary

- Pseudocode is readable, unambiguous, and analyzable
- Algorithm design is a creative process; uses multiple drafts and top-down design to develop the best solution
- Abstraction is a key tool for good design

Important Algorithms

- Sequential search
- ( Get values) and Start at the beginning of list
- Set found = false
- Repeat until found or end of list
- Look at each element
- If element = target
- set found = true and
- print desired element

else

move pointer to next element

- End loop
- If found = false then print message “not on list”
- Stop

Important Algorithms

- Find Largest
- ( Get values) and Start at the beginning of list
- Set found = false and Largest to first element
- Repeat until end of list
- Look at each element
- If element > largest
- set (found = true) and
- (largest = element) and (location = i)
- move pointer to next element
- End loop
- Print largest and location
- Stop

Important Algorithms

Pattern matching in text

- Location 123456789……………….
- Text A manand a woman
- Pattern an
- Output There is a match at position 4, etc.

Patterns in genes

- Gene: TCAGGCTAATCGGAAGT
- Probe: TAATC
- Match: Yes!

Definitions

- Algorithm Discovery – finding a correct and efficient solution to a problem
- Library – collection of useful algorithms
- Iteration – repeated operations
- Index – the position of an element in a list

Definitions

- Abstraction- ability to separate the high level view of an object from the low level details
- Top- Down Design – viewing an operation at a high level of abstraction and later adding the details in steps
- ( stepwise refinement)

Research

- Write an algorithm that generates a Caesar cipher ( see p. 76) and Chapter 13
- Investigate the field of bioinformatics
- Basic Explanations

http://en.wikipedia.org/wiki/Bioinformatics

http://biotech.icmb.utexas.edu/pages/bioinfo.html

- BLAST – tool used to search protein databases

http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/information3.html

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