2IL05 Data Structures 2IL06 Introduction to Algorithms

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2IL05 Data Structures 2IL06 Introduction to
Algorithms

• Spring 2009Lecture 2 Analysis of Algorithms

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Analysis of algorithms
the formal way
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Analysis of algorithms

• Can we say something about the running time of an
  algorithm without implementing and testing it?

• InsertionSort(A)
• initialize sort A1
• for j ? 2 to lengthA
• do key ? Aj
• i ? j -1
• while i gt 0 and Ai gt key
• do Ai1 ? Ai
• i ? i -1
• Ai 1 ? key

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Analysis of algorithms

• Analyze the running time as a function of n ( of
  input elements)
• best case
• average case
• worst case
• elementary operationsadd, subtract, multiply,
  divide, load, store, copy, conditional and
  unconditional branch, return

An algorithm has worst case running time T(n) if
for any input of size n the maximal number of
elementary operations executed is T(n).
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Analysis of algorithms example
InsertionSort 15 n2 7n 2
MergeSort 300 n lg n 50 n
The rate of growth of the running time as a
function of the input is essential!
n 1,000,000 InsertionSort 1.5 x 1013
MergeSort 6 x
109 2500 x faster !
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T-notation

• Intuition concentrate on the leading term,
  ignore constants
• 19 n3 17 n2 - 3n becomes T(n3)
• 2 n lg n 5 n1.1 - 5 becomes
• n - ¾ n vn becomes

T(n1.1)
---
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T-notation

• Let g(n) N ? N be a function. Then we have
• T(g(n)) f(n) there exist positive constants
  c1, c2, and n0 such that 0
  c1g(n) f(n) c2g(n) for all n n0
• T(g(n)) is the set of functions that grow as
  fast as g(n)

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T-notation

• Let g(n) N ? N be a function. Then we have
• T(g(n)) f(n) there exist positive constants
  c1, c2, and n0 such that 0
  c1g(n) f(n) c2g(n) for all n n0

Notation f(n) T(g(n))
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T-notation

• Let g(n) N ? N be a function. Then we have
• T(g(n)) f(n) there exist positive constants
  c1, c2, and n0 such that 0
  c1g(n) f(n) c2g(n) for all n n0
• Claim 19n3 17n2 - 3n T(n3)
• Proof Choose c1 19, c2 36 and n0 1.
• Then we have for all n n0
• 0 c1n3 (trivial)
• 19n3 17n2 - 3n
  (since 17n2 gt 3n for n 1)
• c2n3
  (since 17n2 17n3 for n 1)

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T-notation

• Let g(n) N ? N be a function. Then we have
• T(g(n)) f(n) there exist positive constants
  c1, c2, and n0 such that 0 c1g(n)
  f(n) c2g(n) for all n n0
• Claim 19n3 17n2 - 3n ? T(n2)
• Proof Assume that there are positive constants
  c1, c2, and n0 such that for all n n0
• 0 c1n2 19n3 17n2 - 3n c2n2
• Since 3n 17n2 0 we would have for all n
  n0 19n3 c2n2
• and hence 19n c2.

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O-notation

• Let g(n) N ? N be a function. Then we have
• O(g(n)) f(n) there exist positive constants
  c and n0 such that 0
  f(n) cg(n) for all n n0
• O(g(n)) is the set of functions that grow at
  most as fast as g(n)

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O-notation

• Let g(n) N ? N be a function. Then we have
• O(g(n)) f(n) there exist positive constants
  c and n0 such that 0
  f(n) cg(n) for all n n0

Notation f(n) O(g(n))
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O-notation

• Let g(n) N ? N be a function. Then we have
• O(g(n)) f(n) there exist positive constants
  c and n0 such that 0
  cg(n) f(n) for all n n0
• O(g(n)) is the set of functions that grow at
  least as fast as g(n)

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O-notation

• Let g(n) N ? N be a function. Then we have
• O(g(n)) f(n) there exist positive constants
  c and n0 such that 0
  cg(n) f(n) for all n n0

Notation f(n) O(g(n))
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Asymptotic notation

• T() is an asymptotically tight bound
• O() is an asymptotic upper bound
• O() is an asymptotic lower bound
• other asymptotic notation o() ? grows
  strictly slower than ?() ? grows strictly
  faster than

asymptotically equal
asymptotically smaller or equal
asymptotically greater or equal
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More notation

• f(n) n3 T(n2) means
• f(n) means
• O(1) or T(1) means
• 2n2 O(n) T(n2) means

• there is a function g(n) such that
• f(n) n3 g(n) and g(n) T(n2)
• there is one function g(i) such that
• f(n) and g(i) O(i)
• a constant
• for each function g(n) with g(n)O(n)
• we have 2n2 g(n) T(n2)

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Quiz

• O(1) O(1) O(1)
• O(1) O(1) O(1)
• O(n2) O(n3)
• O(n3) O(n2)
• T(n2) O(n3)
• An algorithm with worst case running time O(n log
  n) is always slower than an algorithm with worst
  case running time O(n) if n is sufficiently large.

• true
• false
• true
• true
• false
• true
• false

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Quiz

• n log2 n T(n log n)
• n log2 n O(n log n)
• n log2 n O(n4/3)
• O(2n) O(3n)
• O(2n) T(3n)

• false
• true
• true
• true
• false

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Analysis of algorithms
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Analysis of InsertionSort

• InsertionSort(A)
• initialize sort A1
• for j ? 2 to lengthA
• do key ? Aj
• i ? j -1
• while i gt 0 and Ai gt key
• do Ai1 ? Ai
• i ? i -1
• Ai 1 ? key
• Get as tight a bound as possible on the worst
  case running time.
• ? lower and upper bound for worst case running
  time
• Upper bound Analyze worst case number of
  elementary operations
• Lower bound Give bad input example

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Analysis of InsertionSort

• InsertionSort(A)
• initialize sort A1
• for j ? 2 to lengthA
• do key ? Aj
• i ? j -1
• while i gt 0 and Ai gt key
• do Ai1 ? Ai
• i ? i -1
• Ai 1 ? key
• Upper bound Let T(n) be the worst case running
  time of InsertionSort on an array of
  length n. We have
• T(n)
• Lower bound

O(1)
O(1)
worst case(j-1) O(1)
O(1)
O(1) (j-1)O(1) O(1)
O(j)
O(n2)
O(1)
Array sorted in de-creasing order ?
O(n2)
The worst case running time of InsertionSort is
T(n2).
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Analysis of MergeSort

• MergeSort(A)
• ? divide-and-conquer algorithm that sorts array
  A1..n
• if lengthA 1
• then skip
• else
• n ? lengthA n1 ? floor(n/2) n2 ?
  ceil(n/2)
• copy A1.. n1 to auxiliary array
  A11.. n1
• copy An11..n to auxiliary array
  A21.. n2
• MergeSort(A1) MergeSort(A2)
• Merge(A, A1, A2)

O(1)
O(1)
O(n)
O(n)
??
O(n)
T( n/2 ) T( n/2 )
MergeSort is a recursive algorithm ? running
time analysis leads to recursion
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Analysis of MergeSort

• Let T(n) be the worst case running time of
  MergeSort on an array of length n. We have
• O(1) if n
  1
• T(n)
• T( n/2 ) T( n/2 ) T(n)
  if n gt 1

frequently omitted since it (nearly) always holds
often written as 2T(n/2)
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Solving recurrences
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Solving recurrences

• Easiest Master theoremcaveat not always
  applicable
• Alternatively Guess the solution and use the
  substitution method to prove that your guess it
  is correct.
• How to guess
• expand the recursion
• draw a recursion tree

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The master theorem

• Let a and b be constants, let f(n) be a function,
  and let T(n)
• be defined on the nonnegative integers by the
  recurrence
• T(n) aT(n/b) f(n)
• Then we have
• If f(n) O(nlog a e) for some constant e gt 0,
  then T(n) T(nlog a).
• If f(n) T(nlog a), then T(n) T(nlog a log
  n)
• If f(n) O(nlog a e) for some constant e gt 0,
  and if af(n/b) cf(n) for some constant c lt 1
  and all sufficiently large n, then T(n) T(f(n))

can be rounded up or down
note logba - e
b
b
b
b
b
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The master theorem Example

• T(n) 4T(n/2) T(n3)
• Master theorem with a 4, b 2, and f(n) n3
• logba log24 2
• ? n3 f(n) O(nlog a e) O(n2 e) with,
  for example, e 1
• Case 3 of the master theorem gives T(n) T(n3),
  if the regularity condition holds.
• choose c ½ and n0 1
• ? af(n/b) 4(n/2)3 n3/2 cf(n) for n
  n0
• ? T(n) T(n3)

b
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The substitution method

• The Master theorem does not always apply
• In those cases, use the substitution method
• Guess the form of the solution.
• Use induction to find the constants and show that
  the solution works
• Use expansion or a recursion-tree to guess a good
  solution.

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Recursion-trees

• T(n) 2T(n/2) n

n
n/2 n/2
n/4 n/4 n/4 n/4
n/2i n/2i
n/2i
T(1) T(1)
T(1)
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Recursion-trees

• T(n) 2T(n/2) n

n
2 (n/2) n
4 (n/4) n
log n
2i (n/2i) n
n T(1) T(n)

T(n log n)
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Recursion-trees

• T(n) 2T(n/2) n2

n2
(n/2)2 (n/2)2
(n/4)2 (n/4)2 (n/4)2 (n/4)2
(n/2i)2 (n/2i)2
(n/2i)2
T(1) T(1)
T(1)
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Recursion-trees

• T(n) 2T(n/2) n2

n2
2 (n/2)2 n2/2
4 (n/4)2 n2/4
2i (n/2i) 2 n2/2i
n T(1) T(n)

T(n2)
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Recursion-trees

• T(n) 4T(n/2) n

n
n/2 n/2 n/2 n/2
n/4 n/4 n/4 n/4
T(1) T(1) T(1)
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Recursion-trees

• T(n) 4T(n/2) n

n
n
n/2 n/2 n/2 n/2
4 (n/2) 2n
n/4 n/4 n/4 n/4
16 (n/4) 4n
T(1) T(1) T(1)
n2 T(1) T(n2)

T(n2)
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The substitution method
2 if n 1 2T( n/2 ) n if n
gt 1
T(n)

• Claim T(n) O(n log n)
• Proof by induction on n
• to show there are constants c and n0 such that
  
• T(n) c n log n for all n n0
• T(1) 2 ? choose c 2 (for now) and n0 2
• Why n0 2?
• How many base cases?
• Base cases n 2 T(2) 2T(1) 2
  22 2 6 c 2 log 2 for c 3
• n 3 T(3) 2T(1) 2 22 2 6 c
  3 log 3

log 1 0 ? can not prove bound for n 1
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The substitution method
2 if n 1 2T( n/2 ) n if n
gt 1
T(n)

• Claim T(n) O(n log n)
• Proof by induction on n
• to show there are constants c and n0 such that
  
• T(n) c n log n for all n n0
• choose c 3 and n0 2
• Inductive step n gt 3
• T(n) 2T( n/2 ) n
• 2 c n/2
  log n/2 n (ind. hyp.)
• c n
  ((log n) - 1) n
• c n log n
  

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The substitution method
T(1) if n 1 2T( n/2 ) n if n gt 1
T(n)

• Claim T(n) O(n)
• Proof by induction on n
• Base case n n0
• T(2) 2T(1) 2 2c 2 O(2)
• Inductive step n gt n0
• T(n) 2T( n/2 ) n
• 2O( n/2 ) n (ind.
  hyp.)
• O(n)

Never use O, T, or O in a proof by induction!
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Analysis of algorithms
one more example
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Example

• Example (A)
• ? A is an array of length n
• n ? lengthA
• if n1
• then return A1
• else begin
• Copy A1 n/2 to auxiliary
  array B1... n/2
• Copy A1 n/2 to auxiliary
  array C1 n/2
• b ? Example(B) c ? Example(C)
• for i ? 1 to n
• do for j ? 1 to i
• do Ai ?
  Aj
• return 43
• end

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Example

• Let T(n) be the worst case running time of
  Example on an array of length n.
• Lines 1,2,3,4,11, and 12 take T(1) time.
• Lines 5 and 6 take T(n) time.
• Line 7 takes T(1) 2 T( n/2 ) time.
• Lines 8 until 10 take
• time.
• If n1 lines 1,2,3 are executed,
• else lines 1,2, and 4 until 12 are executed.
• ? T(n)
• ? use master theorem

• Example (A)
• ? A is an array of length n
• n ? lengthA
• if n1
• then return A1
• else begin
• Copy A1 n/2 to auxiliary
  array B1... n/2
• Copy A1 n/2 to auxiliary
  array C1 n/2
• b ? Example(B) c ? Example(C)
• for i ? 1 to n
• do for j ? 1 to i
• do Ai ?
  Aj
• return 43
• end

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Tips

• Analysis of recursive algorithmsfind the
  recursion and solve with master theorem if
  possible
• Analysis of loops summations
• Some standard recurrences and sums
• T(n) 2T(n/2) T(n) ?
• ½ n(n1) T(n2)
• T(n3)

T(n) T(n log n)
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Tutorials this week

• Small tutorials on Tuesday 34.
• Wednesday 78 big tutorial.
• Small tutorial Friday 78.