CHAPTER 2 Analysis of Algorithms

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CHAPTER 2Analysis of Algorithms
Algorithm
Input
Output
An algorithm is a step-by-step procedure
for solving a problem in a finite amount of time.
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Algorithm Analysis Do NOT worry !!!
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Math you need to Review

• Series summations
• Logarithms and Exponents
• Proof techniques
• Basic probability

• properties of logarithms
• logb(xy) logbx logby
• logb (x/y) logbx - logby
• logbxa alogbx
• logba logxa/logxb
• properties of exponentials
• a(bc) aba c
• abc (ab)c
• ab /ac a(b-c)
• b a logab
• bc a clogab

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Sum series-examples
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Recurrence Relations
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Assumptions for the computational model

• Basic computer with sequentially executed
  instructions
• Infinite memory
• Has standard operations addition,
  multiplication, comparison in 1 time unit unless
  stated otherwise
• Has fixed-size (32bits) integers such that
  no-fancy operations are allowed!!! eg.matrix
  inversion,
• Proof techniques
• Basic probability

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Running Time

• Most algorithms transform input objects into
  output objects.
• The running time of an algorithm typically grows
  with the input size.
• Average case time is often difficult to
  determine.
• We focus on the worst case running time.
• Easier to analyze
• Crucial to applications such as games, finance,
  image,robotics, AI, etc.

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Experimental Studies

• Write a program implementing the algorithm
• Run the program with inputs of varying size and
  composition
• Assume a subprogram is written to get an accurate
  measure of the actual running time
• Plot the results

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Limitations of Experiments

• It is necessary to implement the algorithm, which
  may be difficult
• Results may not be indicative of the running time
  on other inputs not included in the experiment.
• In order to compare two algorithms, the same
  hardware and software environments must be used

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Theoretical Analysis

• Uses a high-level description of the algorithm
  instead of an implementation
• Characterizes running time as a function of the
  input size, n.
• Takes into account all possible inputs
• Allows us to evaluate the speed of an algorithm
  independent of the hardware/software environment

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Pseudocode

• High-level description of an algorithm
• More structured than English prose
• Less detailed than a program
• Preferred notation for describing algorithms
• Hides program design issues

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Pseudocode Details

• Control flow
• if then else
• while do
• repeat until
• for do
• Indentation replaces braces
• Method declaration
• Algorithm method (arg , arg)
• Input
• Output

• Method call
• var.method (arg , arg)
• Return value
• return expression
• Expressions
• Assignment(like ? in Java)
• Equality testing(like ?? in Java)
• n2 Superscripts and other mathematical formatting
  allowed

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Important Functions

• Seven functions that often appear in algorithm
  analysis
• Constant ? 1
• Logarithmic ? log n
• Linear ? n
• N-Log-N ? n log n
• Quadratic ? n2
• Cubic ? n3
• Exponential ? 2n
• In a log-log chart, the slope of the line
  corresponds to the growth rate of the function

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Primitive Operations

• Examples
• Evaluating an expression
• Assigning a value to a variable
• Indexing into an array
• Calling a method
• Returning from a method

• Basic computations performed by an algorithm
• Identifiable in pseudocode
• Largely independent from the programming language
• Exact definition not important (we will see why
  later)
• Assumed to take a constant amount of time in the
  RAM model

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Detailed Model 4 counting operations-1
The time require for the following operations are
all constants.
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Detailed Model 4 counting operations-2
The time require for the following operations are
all constants.
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Detailed Model 4 counting operations-3
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Detailed Model... Example1Sum

• ...
• public static int sum(int n)
• int result 0
• for (int i 1 i lt n i)
• result i
• return result
• ...

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Detailed Model... Example2 ()

• y a i
• 3?fetch ?. ?store
• operations
• fetch a (the base address of the array)
• fetch i (the index into the array)
• address calculation
• fetch array element ai
• store the result

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Detailed Model...Example3 Horner ()

• public class Horner
• public static void main(String args)
• Horner h new Horner()
• int a 1, 3, 5
• System.out.println("a(1)" h.horner(a,
  a.length - 1, 1))
• System.out.println("a(2)" h.horner(a,
  a.length - 1, 2))
• int horner(int a, int n, int x)
• int result an
• for (int i n - 1 i gt 0 --i)
• result result x ai
• //System.out.println("i" i " result"
  result)
• return result

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Detailed Model...Example-4 ()

• public class FindMaximum
• public static void main(String args)
• FindMaximum h new FindMaximum()
• int a 1, 3, 5
• System.out.println("max" h.findMaximum(a))
• int findMaximum(int a)
• int result a0
• for (int i 0 i lt a.length i)
• if (result lt ai)
• result ai
• System.out.println("i" i " result"
  result)
• return result

3?fetch ?. ?store ?fetch
?store (2?fetch ?lt) n (2?fetch ? ?store)
(n-1) (4?fetch ?. ?lt) (n-1) (3?fetch
?. ?store) ? ?fetch ?store
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Simplified Model More Simplification

• All timing parameters are expressed in units of
  clock cycles. In effect, T1.
• The proportionality constant, k, for all timing
  parameters is assumed to be the same k1.

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Simplified Model Example_FindMax

• public class FindMaximum
• public static void main(String args)
• FindMaximum h new FindMaximum()
• int a 1, 3, 5
• System.out.println("max" h.findMaximum(a))
• int findMaximum(int a)
• int result a0
• for (int i 0 i lt a.length i)
• if (result lt ai)
• result ai
• System.out.println("i" i " result"
  result)
• return result

1 2 3 4 5 6 7 8 9 10
detailed 2 3?fetch ?. ?store 3a ?fetch
?store 3b (2?fetch ?lt) n 3c (2?fetch ?
?store) (n-1) 4 (4?fetch ?. ?lt)
(n-1) 6 (3?fetch ?. ?store) ? 9 ?fetch
?store
simple 2 5 3a 2 3b (3)n 3c (4)(n-1) 4 (6)(n-1)
6 (5)? 9 2
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Simplified Model gt Algorithm1Geometric Series
Sum

• public class GeometrikSeriesSum
• public static void main(String args)
• System.out.println("1, 4 "
  geometricSeriesSum(1, 4))
• System.out.println("2, 4 "
  geometricSeriesSum(2, 4))
• public static int geometricSeriesSum(int x, int
  n)
• int sum 0
• for (int i 0 i lt n i)
• int prod 1
• for (int j 0 j lt i j)
• prod x
• sum prod
• return sum

1 2 3 4 5 6 7 8 9 10 11 12
simple 1 2 2 3a 2 3b 3(n2) 3c 4(n1) 4 2(n1) 5a
2(n1) 5b 3 (i1) 5c 4 i 6 4
i 7 8 4(n1) 9 10 2 11 12 Total 11/2 n2 47/2 n
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Simplified Model gt Algorithm_SumHornerGeometric
Series Sum
1 2 3 4 5 6 7

• public class GeometrikSeriesSumHorner
• public static void main(String args)
• System.out.println("1, 4 "
  geometricSeriesSum(1, 4))
• System.out.println("2, 4 "
  geometricSeriesSum(2, 4))
• public static int geometricSeriesSum(int x, int
  n)
• int sum 0
• for (int i 0 i lt n i)
• sum sum x 1
• return sum

• 2 2
• 3a 2
• 3b 3(n2)
• 3c 4(n1)
• 4 6(n1)
• 6 2
• Total 13 n 22

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Simplified Model gt Algorithm_SumPower Geometric
Series Sum

• public class GeometrikSeriesSumPower
• public static void main(String args)
• System.out.println("1, 4 " powerA(1,
  4))
• System.out.println("1, 4 " powerB(1,
  4))
• System.out.println("2, 4 " powerA(2,
  4))
• System.out.println("2, 4 " powerB(2,
  4))
• ...
• public static int powerA(int x, int n)
• int result 1
• for (int i 1 i lt n i)
• result x
• return result
• public static int powerB(int x, int n)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1
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powerB ? 1 n0 xn ? (x2)?n/2? 0ltn, n is
even ? x(x2)?n/2? 0ltn, n is odd
powerB 0ltn 0ltn n0 n even n
odd 10 3 3 3 11 2 - - 12 - 5 5 13 - 10T(?n/2?)
- 15 - - 12T(?n/2?) Total 5 18T(?n/2?) 20T(?n/
2?)
powerB ? 5 n0 xn ? 18T(?n/2?) 0ltn, n is
even ? 20T(?n/2?) 0ltn, n is odd
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Simplified Model gt Calculation of PowerB
Geometric Series Sum ()

• Let n 2k for some kgt0.
• Since n is even, ?n/2? n/2 2k-1.
• For n 2k, T(2k) 18 T(2k-1).
• Using repeated substitution
• T(2k) 18 T(2k-1)
• 18 18 T(2k-2)
• 18 18 18 T(2k-3)
• 18j T(2k-j)
• Substitution stops when k j
• T(2k) 18k T(1)
• 18k 20 T(0)
• 18k 20 5
• 18k 25.
• n 2k then k log2 n

n is even
powerB xn ? 18T(?n/2?) 0ltn
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Simplified Model gt Calculation of PowerB
Geometric Series Sum ()
Suppose n 2k1 for some kgt0. Since n is odd,
?n/2? ?(2k1)/2?
(2k2)/2 2k-1 For n 2k-1, T(2k-1) 20
T(2k-1-1), kgt1. Using repeated
substitution T(2k-1) 20 T(2k-1-1) 20 20
T(2k-2-1) 20 20 20 T(2k-3-1)
20j T(2k-j-1) Substitution stops when k
j T(2k-1) 20k T(20-1) 20k T(0) 20k
5. n 2k-1 then k log2 (n1) T(n) 20 log2
(n1) 5
n is odd
Therefore, powerB ? 5 n0 xn ? 18T(?n/2?)
0ltn, n is even ? 20T(?n/2?) 0ltn, n is
odd Average 19(?log2(n1)? 1) 18
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Simplified Model gt Algorithm_SumPower Geometric
Series Sum

• public class GeometrikSeriesSumPower
• public static void main(String args)
• System.out.println(s 2, 4 "
  geometrikSeriesSumPower (2, 4))
• ...
• public static int geometrikSeriesSumPower (int
  x, int n)
• return powerB(x, n 1) 1 / (x - 1)
• public static int powerB(int x, int n)
• if (n 0)
• return 1
• else if (n 2 0) // n is even
• return powerB(x x, n / 2)
• else // n is odd
• return x powerB(x x, n / 2)

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Comparison of 3 Algorithms
algorithm T(n) Sum 11/2 n2 47/2 n
27 Horner 13n 22 Power 19(?log2(n1)? 1) 18
sum
Horner
Power
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Counting Primitive Operations for Pseudocodes

• By inspecting the pseudocode, we can determine
  the maximum number of primitive operations
  executed by an algorithm, as a function of the
  input size

• Algorithm arrayMax(A, n)
• operations
• currentMax ? A0 2
• for i ? 1 to n ? 1 do 2n
• if Ai ? currentMax then 2(n ? 1)
• currentMax ? Ai 2(n ? 1)
• increment counter i 2(n ? 1)
• return currentMax 1
• Total 8n ? 2

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Estimating Running Time

• Algorithm arrayMax executes 8n ? 2 primitive
  operations in the worst case. Define
• a Time taken by the fastest primitive operation
• b Time taken by the slowest primitive
  operation
• Let T(n) be worst-case time of arrayMax. Then a
  (8n ? 2) ? T(n) ? b(8n ? 2)
• Hence, the running time T(n) is bounded by two
  linear functions

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Asymptotic Algorithm Analysis

• The asymptotic analysis of an algorithm
  determines the running time in big-Oh notation
• To perform the asymptotic analysis
• We find the worst-case number of primitive
  operations executed as a function of the input
  size
• We express this function with big-Oh notation
• Example
• We determine that algorithm arrayMax executes at
  most 8n ? 2 primitive operations
• We say that algorithm arrayMax runs in O(n)
  time
• Since constant factors and lower-order terms are
  eventually dropped anyhow, we can disregard them
  when counting primitive operations

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Growth Rate of Running Time

• Changing the hardware/ software environment
• Affects T(n) by a constant factor, but
• Does not alter the growth rate of T(n)
• The linear growth rate of the running time T(n)
  is an intrinsic property of algorithm arrayMax

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Constant Factors

• The growth rate is not affected by
• constant factors or
• lower-order terms
• Examples
• 102n 105 is a linear function
• 105n2 108n is a quadratic function

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Big-Oh Notation

• Given functions f(n) and g(n), we say that f(n)
  is O(g(n)) if there are positive constantsc and
  n0 such that
• f(n) ? cg(n) for n ? n0
• Example 2n 10 is O(n)
• 2n 10 ? cn
• (c ? 2) n ? 10
• n ? 10/(c ? 2)
• Pick c 3 and n0 10

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Big-Oh Example

• Example the function n2 is not O(n)
• n2 ? cn
• n ? c
• The above inequality cannot be satisfied since c
  must be a constant

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More Big-Oh Examples

• 7n-2

• 7n-2 is O(n)
• need c gt 0 and n0 ? 1 such that 7n-2 ? cn for n
  ? n0
• this is true for c 7 and n0 1

• 3n3 20n2 5

3n3 20n2 5 is O(n3) need c gt 0 and n0 ? 1
such that 3n3 20n2 5 ? cn3 for n ? n0 this
is true for c 4 and n0 21

• 3 log n 5

3 log n 5 is O(log n) need c gt 0 and n0 ? 1
such that 3 log n 5 ? clog n for n ? n0 this
is true for c 8 and n0 2
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Big-Oh and Growth Rate

• The big-Oh notation gives an upper bound on the
  growth rate of a function
• The statement f(n) is O(g(n)) means that the
  growth rate of f(n) is no more than the growth
  rate of g(n)
• We can use the big-Oh notation to rank functions
  according to their growth rate

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More Example
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Example
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Big-Oh Rules

• Theorem
• Consider polynomial where
  amgt0.Then f(n) O(nm). i.e.,
• Drop lower-order terms
• Drop constant factors

• Use the smallest possible class of functions
• Say 2n is O(n) instead of 2n is O(n2)
• Use the simplest expression of the class
• Say 3n 5 is O(n) instead of 3n 5 is O(3n)

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Big-Oh Rules-2

• logk n O(n) for any constant k?Z

• f(n) O (f(n))
• c O(f(n)) O (f(n))
• O(f(n)) O(f(n)) O(f(n))
• O(O(f(n))) O(f(n))
• O(f(n)) O(g(n)) O(f(n) g(n))

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Relatives of Big-Oh

• big-Omega
• f(n) is ?(g(n)) if there is a constant c gt 0
• and an integer constant n0 ? 1 such that
• f(n) ? cg(n) for n ? n0
• big-Theta
• f(n) is ?(g(n)) if there are constants c gt 0 and
  c gt 0 and an integer constant n0 ? 1 such that
  cg(n) ? f(n) ? cg(n) for n ? n0

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Intuition for Asymptotic Notation

• Big-Oh
• f(n) is O(g(n)) if f(n) is asymptotically less
  than or equal to g(n)
• big-Omega
• f(n) is ?(g(n)) if f(n) is asymptotically greater
  than or equal to g(n)
• big-Theta
• f(n) is ?(g(n)) if f(n) is asymptotically equal
  to g(n)

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Example Uses of the Relatives of Big-Oh

• 5n2 is ?(n2)

f(n) is ?(g(n)) if there is a constant c gt 0 and
an integer constant n0 ? 1 such that f(n) ?
cg(n) for n ? n0 let c 5 and n0 1

• 5n2 is ?(n)

f(n) is ?(g(n)) if there is a constant c gt 0 and
an integer constant n0 ? 1 such that f(n) ?
cg(n) for n ? n0 let c 1 and n0 1

• 5n2 is ?(n2)

f(n) is ?(g(n)) if it is ?(n2) and O(n2). We have
already seen the former, for the latter recall
that f(n) is O(g(n)) if there is a constant c gt 0
and an integer constant n0 ? 1 such that f(n) lt
cg(n) for n ? n0 Let c 5 and n0 1
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Comparison of Orders

• O(1) lt O(log n) lt O(n) lt O(n log n) lt O(n2) lt
  O(n3) lt O(2n)

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Comparison of Orders

• O(1) lt O(log n) lt O(n) lt O(n log n) lt O(n2) lt
  O(n3) lt O(2n)

O(1)
O(log n)
O(n)
O(n log n)
O(n2)
O(n3)
O(2n)
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O(n) Analysis of Running Time Example

• Worst-case running time if statement 5
  executes all the time
• Best-case running time if statement 5 never
  executes
• On-the-average running time if statement 5
  executes half of the time????

int findMaximum(int a) int result
a0 for (int i 1 i lt a.length i) if
(result lt ai) result ai return
result
1 2 3 4 5 6 7 8 9
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Algorithm Complexity as a function of size n

• A comparison-based algorithm for searching or
  sorting a list is
• based on
• making comparisons involving list elements
• then making decisions based on these comparisons.
• SOON U ll know all about these!!!

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HWLA

• Solve Exercises in Chapter2 1, 7, 9 ,12, 25,
  29, 31

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Computing Prefix Averages()

• We further illustrate asymptotic analysis with
  two algorithms for prefix averages
• The i-th prefix average of an array X is average
  of the first (i 1) elements of X
• Ai (X0 X1 Xi)/(i1)
• Computing the array A of prefix averages of
  another array X has applications to financial
  analysis such as ARMA, ARIMA, FARIMA, models.

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Prefix Averages (Quadratic) ()

• The following algorithm computes prefix averages
  in quadratic time by applying the definition

Algorithm prefixAverages1(X, n) Input array X of
n integers Output array A of prefix averages of
X operations A ? new array of n integers
n for i ? 0 to n ? 1 do n s ? X0
n for j ? 1 to i do 1 2 (n ?
1) s ? s Xj 1 2 (n ? 1) Ai
? s / (i 1) n return A
1
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Arithmetic Progression ()

• The running time of prefixAverages1 isO(1 2
  n)
• The sum of the first n integers is n(n 1) / 2
• There is a simple visual proof of this fact
• Thus, algorithm prefixAverages1 runs in O(n2)
  time

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Prefix Averages (Linear) ()

• The following algorithm computes prefix averages
  in linear time by keeping a running sum

Algorithm prefixAverages2(X, n) Input array X of
n integers Output array A of prefix averages of
X operations A ? new array of n
integers n s ? 0 1 for i ? 0 to n ? 1
do n s ? s Xi n Ai ? s / (i 1)
n return A 1

• Algorithm prefixAverages2 runs in O(n) time

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The Random Access Machine (RAM) Model ()

• A CPU
• A potentially unbounded bank of memory cells,
  each of which can hold an arbitrary number or
  character

• Memory cells are numbered and accessing any cell
  in memory takes unit time.

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Quiz-1
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Quiz-1 Solution
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Quiz-2
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Quiz-2 Solution