CSE 326: Data Structures

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CSE 326 Data Structures

• Introduction

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Class Overview

• Introduction to many of the basic data structures
  used in computer software
• Understand the data structures
• Analyze the algorithms that use them
• Know when to apply them
• Practice design and analysis of data structures.
• Practice using these data structures by writing
  programs.
• Make the transformation from programmer to
  computer scientist

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Goals

• You will understand
• what the tools are for storing and processing
  common data types
• which tools are appropriate for which need
• So that you can
• make good design choices as a developer, project
  manager, or system customer
• You will be able to
• Justify your design decisions via formal
  reasoning
• Communicate ideas about programs clearly and
  precisely

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Goals

• I will, in fact, claim that the difference
  between a bad programmer and a good one is
  whether he considers his code or his data
  structures more important. Bad programmers worry
  about the code. Good programmers worry about
  data structures and their relationships.
• Linus Torvalds, 2006

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Goals

• Show me your flowcharts and conceal your
  tables, and I shall continue to be mystified.
  Show me your tables, and I wont usually need
  your flowcharts theyll be obvious.
• Fred Brooks, 1975

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Data Structures

• Clever ways to organize information in order to
  enable efficient computation
• What do we mean by clever?
• What do we mean by efficient?

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Picking the best Data Structure for the job

• The data structure you pick needs to support the
  operations you need
• Ideally it supports the operations you will use
  most often in an efficient manner
• Examples of operations
• A List with operations insert and delete
• A Stack with operations push and pop

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Terminology

• Abstract Data Type (ADT)
• Mathematical description of an object with set of
  operations on the object. Useful building block.
• Algorithm
• A high level, language independent, description
  of a step-by-step process
• Data structure
• A specific family of algorithms for implementing
  an abstract data type.
• Implementation of data structure
• A specific implementation in a specific language

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Terminology examples

• A stack is an abstract data type supporting
  push, pop and isEmpty operations
• A stack data structure could use an array, a
  linked list, or anything that can hold data
• One stack implementation is java.util.Stack
  another is java.util.LinkedList

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Concepts vs. Mechanisms

• Abstract
• Pseudocode
• Algorithm
• A sequence of high-level, language independent
  operations, which may act upon an abstracted view
  of data.
• Abstract Data Type (ADT)
• A mathematical description of an object and the
  set of operations on the object.

• Concrete
• Specific programming language
• Program
• A sequence of operations in a specific
  programming language, which may act upon real
  data in the form of numbers, images, sound, etc.
  
• Data structure
• A specific way in which a programs data is
  represented, which reflects the programmers
  design choices/goals.

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Why So Many Data Structures?

• Ideal data structure
• fast, elegant, memory efficient
• Generates tensions
• time vs. space
• performance vs. elegance
• generality vs. simplicity
• one operations performance vs. anothers

The study of data structures is the study of
tradeoffs. Thats why we have so many of them!
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Todays Outline

• Introductions
• Administrative Info
• What is this course about?
• Review Queues and stacks

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First Example Queue ADT

• FIFO First In First Out
• Queue operations
• create
• destroy
• enqueue
• dequeue
• is_empty

F E D C B
dequeue
enqueue
G
A
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Circular Array Queue Data Structure
Q
size - 1
0
b
c
d
e
f
front
back

• enqueue(Object x)
• Qback x
• back (back 1) size

dequeue() x Qfront front (front 1)
size return x
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Linked List Queue Data Structure
void enqueue(Object x) if (is_empty()) front
back new Node(x) else back-gtnext new
Node(x) back back-gtnext bool is_empty()
return front null
Object dequeue() assert(!is_empty) return_data
front-gtdata temp front front
front-gtnext delete temp return return_data
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Circular Array vs. Linked List

• Too much space
• Kth element accessed easily
• Not as complex
• Could make array more robust

• Can grow as needed
• Can keep growing
• No back looping around to front
• Linked list code more complex

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Second Example Stack ADT

• LIFO Last In First Out
• Stack operations
• create
• destroy
• push
• pop
• top
• is_empty

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Stacks in Practice

• Function call stack
• Removing recursion
• Balancing symbols (parentheses)
• Evaluating Reverse Polish Notation

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Data StructuresAsymptotic Analysis
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Algorithm Analysis Why?

• Correctness
• Does the algorithm do what is intended.
• Performance
• What is the running time of the algorithm.
• How much storage does it consume.
• Different algorithms may be correct
• Which should I use?

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Recursive algorithm for sum

• Write a recursive function to find the sum of the
  first n integers stored in array v.

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Proof by Induction

• Basis Step The algorithm is correct for a base
  case or two by inspection.
• Inductive Hypothesis (nk) Assume that the
  algorithm works correctly for the first k cases.
• Inductive Step (nk1) Given the hypothesis
  above, show that the k1 case will be calculated
  correctly.

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Program Correctness by Induction

• Basis Stepsum(v,0) 0. ?
• Inductive Hypothesis (nk) Assume sum(v,k)
  correctly returns sum of first k elements of v,
  i.e. v0v1vk-1vk
• Inductive Step (nk1) sum(v,n) returns
• vksum(v,k-1) (by inductive hyp.)
• vk(v0v1vk-1)
• v0v1vk-1vk ?

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Algorithms vs Programs

• Proving correctness of an algorithm is very
  important
• a well designed algorithm is guaranteed to work
  correctly and its performance can be estimated
• Proving correctness of a program (an
  implementation) is fraught with weird bugs
• Abstract Data Types are a way to bridge the gap
  between mathematical algorithms and programs

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Comparing Two Algorithms

• GOAL Sort a list of names
• Ill buy a faster CPU
• Ill use C instead of Java wicked fast!
• Ooh look, the O4 flag!
• Who cares how I do it, Ill add more memory!
• Cant I just get the data pre-sorted??

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Comparing Two Algorithms

• What we want
• Rough Estimate
• Ignores Details
• Really, independent of details
• Coding tricks, CPU speed, compiler optimizations,
  
• These would help any algorithms equally
• Dont just care about running time not a good
  enough measure

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Big-O Analysis

• Ignores details
• What details?
• CPU speed
• Programming language used
• Amount of memory
• Compiler
• Order of input
• Size of input sorta.

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Analysis of Algorithms

• Efficiency measure
• how long the program runs time complexity
• how much memory it uses space complexity
• Why analyze at all?
• Decide what algorithm to implement before
  actually doing it
• Given code, get a sense for where bottlenecks
  must be, without actually measuring it

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Asymptotic Analysis
One detail wont ignore problem size,
elements

• Complexity as a function of input size n
• T(n) 4n 5
• T(n) 0.5 n log n - 2n 7
• T(n) 2n n3 3n
• What happens as n grows?

Asymptotic performance as N -gt infinity
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Why Asymptotic Analysis?

• Most algorithms are fast for small n
• Time difference too small to be noticeable
• External things dominate (OS, disk I/O, )
• BUT n is often large in practice
• Databases, internet, graphics,
• Difference really shows up as n grows!

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Exercise - Searching

• bool ArrayFind( int array, int n, int key)
• // Insert your algorithm here

What algorithm would you choose to implement this
code snippet?
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Analyzing Code

• Constant time
• Sum of times
• Larger branch plus test
• Sum of iterations
• Cost of function body
• Solve recurrence relation

• Basic Java operations
• Consecutive statements
• Conditionals
• Loops
• Function calls
• Recursive functions

Best case Worst Case (what are these?)
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Linear Search Analysis

• Best Case
• Worst Case

• bool LinearArrayFind(int array,
• int n,
• int key )
• for( int i 0 i lt n i ) if( arrayi
  key )
• // Found it!
• return true
• return false

Best T(n) 4, when at 0 Worst T(n) 3n2
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Binary Search Analysis

• Best case
• Worst case

• bool BinArrayFind( int array, int low,
• int high, int key )
• // The subarray is empty
• if( low gt high ) return false
• // Search this subarray recursively
• int mid (high low) / 2
• if( key arraymid )
• return true
• else if( key lt arraymid )
• return BinArrayFind( array, low,
• mid-1, key )
• else
• return BinArrayFind( array, mid1,
• high, key )

Best 4 when at mid Worst recursion! 4 log n
4
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Solving Recurrence Relations
For problem of size n, Time to get ready for
recursive call (4) time for that call.
T(n) 4 T(n/2) T(1) 4

1. Determine the recurrence relation. What is/are
   the base case(s)?
2. Expand the original relation to find an
   equivalent general expression in terms of the
   number of expansions.
3. Find a closed-form expression by setting the
   number of expansions to a value which reduces the
   problem to a base case

By repeated substitution, until see pattern
T(n) 4 (4 T( n/4 ) ) 4 (4 (4
T(n/8))) 43 T(n/23)
4k T(n/2k)
Want n/2k 1, mult by 2k,
Need (n/2k) 1, mult by 2k So k log n (all
logs to base 2) Hence, T(n) 4 log n 4
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Data StructuresAsymptotic Analysis

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Linear Search vs Binary Search
Linear Search Binary Search
Best Case 4 at 0 4 at middle
Worst Case 3n2 4 log n 4
4 at 0
4 at mid
4 log n 4
3n2
Depends on constants, input size, good/bad input
for alg., machine,
So which algorithm is better? What tradeoffs
can you make?
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Fast Computer vs. Slow Computer
Pentium IV was newer/faster machine
With same algorithm, faster machine wins!!
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Fast Computer vs. Smart Programmer (round 1)
With different algorithms, constants matter!!
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Fast Computer vs. Smart Programmer (round 2)
Eventually, better algorithm wins!!
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Asymptotic Analysis

• Asymptotic analysis looks at the order of the
  running time of the algorithm
• A valuable tool when the input gets large
• Ignores the effects of different machines or
  different implementations of an algorithm
• Intuitively, to find the asymptotic runtime,
  throw away the constants and low-order terms
• Linear search is T(n) 3n 2 ? O(n)
• Binary search is T(n) 4 log2n 4 ? O(log n)

Bases dont matter, more in a sec
Remember the fastest algorithm has the slowest
growing function for its runtime
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Asymptotic Analysis
4n 0.5 n log n 2n --- n n log n 2 n log n n
log n

• Eliminate low order terms
• 4n 5 ?
• 0.5 n log n 2n 7 ?
• n3 2n 3n ?
• Eliminate coefficients
• 4n ?
• 0.5 n log n ?
• n log n2 gt

Any base x log is equivalent to a base 2 log
within a constant factor
log_A B log_x B -------------
log_x A
log_x B log_2 B -------------
log_2 x
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Properties of Logs

• log AB log A log B
• Proof
• Similarly
• log(A/B) log A log B
• log(AB) B log A
• Any log is equivalent to log-base-2

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Order Notation Intuition
Why do we eliminate these constants, etc?
This is not the whole picture!
f(n) n3 2n2 g(n) 100n2 1000

• Although not yet apparent, as n gets
  sufficiently large, f(n) will be greater than
  or equal to g(n)

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Definition of Order Notation

• Upper bound T(n) O(f(n)) Big-O
• Exist positive constants c and n such that
• T(n) ? c f(n) for all n ? n
• Lower bound T(n) ?(g(n)) Omega
• Exist positive constants c and n such that
• T(n) ? c g(n) for all n ? n
• Tight bound T(n) ?(f(n)) Theta
• When both hold
• T(n) O(f(n))
• T(n) ?(f(n))

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Definition of Order Notation
Back to our two functions f and g from before

• O( f(n) ) a set or class of functions
• g(n) ? O( f(n) ) iff there exist positive
  consts c and n0 such that g(n) ? c f(n) for
  all n ? n0
• Example100n2 1000 ? 5 (n3 2n2) for all n ?
  19 So g(n) ? O( f(n) )

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Order Notation Example
Wait, crossover point at 100, not 19? Point is-
big_O captures the relationship doesnt tell
you exactly where the crossover point is. If we
pick c 1, then n0100

• 100n2 1000 ? 5 (n3 2n2) for all n ? 19
• So f(n) ? O( g(n) )

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Some Notes on Notation

• Sometimes youll see
• g(n) O( f(n) )
• This is equivalent to
• g(n) ? O( f(n) )
• What about the reverse?
• O( f(n) ) g(n)

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Big-O Common Names

• constant O(1)
• logarithmic O(log n) (logkn, log n2 ? O(log n))
• linear O(n)
• log-linear O(n log n)
• quadratic O(n2)
• cubic O(n3)
• polynomial O(nk) (k is a constant)
• exponential O(cn) (c is a constant gt 1)

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Meet the Family

• O( f(n) ) is the set of all functions
  asymptotically less than or equal to f(n)
• o( f(n) ) is the set of all functions
  asymptotically strictly less than f(n)
• ?( f(n) ) is the set of all functions
  asymptotically greater than or equal to f(n)
• ?( f(n) ) is the set of all functions
  asymptotically strictly greater than f(n)
• ?( f(n) ) is the set of all functions
  asymptotically equal to f(n)

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Meet the Family, Formally

• g(n) ? O( f(n) ) iff There exist c and n0 such
  that g(n) ? c f(n) for all n ? n0
• g(n) ? o( f(n) ) iff There exists a n0 such that
  g(n) lt c f(n) for all c and n ? n0
• g(n) ? ?( f(n) ) iffThere exist c and n0 such
  that g(n) ? c f(n) for all n ? n0
• g(n) ? ?( f(n) ) iffThere exists a n0 such that
  g(n) gt c f(n) for all c and n ? n0
• g(n) ? ?( f(n) ) iffg(n) ? O( f(n) ) and g(n) ?
  ?( f(n) )

Equivalent to limn?? g(n)/f(n) 0
Equivalent to limn?? g(n)/f(n) ?
Note only inequality change, or (there exists c)
or (for all c).
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Big-Omega et al. Intuitively
Asymptotic Notation Mathematics Relation
O ?
? ?
?
o lt
? gt
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Pros and Cons of Asymptotic Analysis
Pros quick-and-dirty comparison separates alg
from architecture Cons less precise (lose
consts) separates alg from arch. (bad for
graphics etc) doesnt capture implementation comp
lexity
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Perspective Kinds of Analysis

• Running time may depend on actual data input, not
  just length of input
• Distinguish
• Worst Case
• Your worst enemy is choosing input
• Best Case
• Average Case
• Assumes some probabilistic distribution of inputs
• Amortized
• Average time over many operations

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Types of Analysis
All of these can be applied to any analysis case

• Two orthogonal axes
• Bound Flavor
• Upper bound (O, o)
• Lower bound (?, ?)
• Asymptotically tight (?)
• Analysis Case
• Worst Case (Adversary)
• Average Case
• Best Case
• Amortized

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16n3log8(10n2) 100n2 O(n3log n)

• Eliminate low-order terms
• Eliminate constant coefficients

• 16n3log8(10n2) 100n2
• ?16n3log8(10n2)
• ?n3log8(10n2)
• ?n3(log8(10) log8(n2))
• ?n3log8(10) n3log8(n2)
• ?n3log8(n2)
• ?2n3log8(n)
• ?n3log8(n)
• ?n3log8(2)log(n)
• ?n3log(n)/3
• ?n3log(n)