Transcript No Slide Title

Time Complexity
UC Berkeley
Fall 2004, E77
http://jagger.me.berkeley.edu/~pack/e77
Copyright 2005, Andy Packard. This work is licensed under the Creative Commons Attribution-ShareAlike
License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/2.0/ or send a letter to
Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.
Time complexity of algorithms
Dependency of
– the time it takes to solve a problem
– as a function of the problem dimension/size
Examples:
– Sorting a list of length n
– Searching a list of length n
– Multiplying a n×n matrix by an n×1 vector
Time to solve problem might depend on data
– Average-case time
– Best-case time
• data is well suited for algorithm (can’t be counted on)
– Worst-case time
• data is such that algorithm performs poorly (time-wise)
Worst-Case gives an upper bound as to how much time
will be needed to solve any instance of the problem.
Example: N-by-N matrix, N-by-1 vector, multiply
Total = c1N+c2N+N(c3+c2N+N(c4+c5)+c5)
= (c2+c4+c5)N2 + (c1+c2+c3+c5)N
= c6N2 + c7N
N times
Y = zeros(N,1);
initialize space, c1N
initialize “for” loop, c2N
for i=1:N
Scalar assignment, c3
Y(i) = 0.0;
initialize “for” loop, c2N
for j=1:N
Y(i) = Y(i) + A(i,j)*x(j);
c4
End of loop, return/exit, c5
end
end
End of loop, return/exit, c5
N times
(3 accesses, 1 add, 1 multiply)
Asymptotic Time complexity of algorithms
Dependency of
– the time it takes to solve a problem
– as a function of the problem dimension/size
but
– formula may only be valid for “large” problems
So, we keep track of “growth rates” of the computational
workload as a function of problem dimension
Order, called “big O” notation
Given two functions, f and g, say that “f is of order g” if
– there is a constant c, and
– a value x0
such that
f x   c  g x  for all x  x0
So, apart from a fixed multiplicative constant, the function
g is an
– upper bound on the function f
– valid for large values of its argument.
Notation: write
f  Og  to mean “f is of order g”.
   Og x
Sometimes write f x
arguments are labled.
to remind us what the
Not equality,but “belongs to a class”
Recall that f n  Og n means that
– there is a constant c, and
– a value n0
such that f n   c  g n  for all n  n0
The = sign does not mean equality!
It means that f is an element of the set of functions which
are eventually bounded by (different) constant multiples of
g.
Therefore, it is correct/ok to write
3n  On,
 
3n  O n 2 ,

3n  O n3  log n

Big O: Examples
Example:
Note:
f n   31
g ( n)  1
for all n  0, 31  31
f n  31 g (n)
For all n, f is bounded above by 31g
Write
or
f n  Og n
31  O1
Big O: Examples
Example:
f n   4n 2  7n  12
2
g ( n)  n
Note: for n  8,
4n 2  7n  12  5n 2
f n 
For large enough n
f is bounded above by 5g
5 g ( n)
1200
1000
800
Write f n  Og n
or
f  Og 
or
4n 2  7n  12  O n 2
 
600
400
200
0
0
5
10
15
Big O: Relationships
Suppose f1 and f2 satisfy: There is a value n0
f1 n  f 2 n for all n  n0
Then f1 n   f 2 n   2 f 2 n  for all n  n0
Hence
f1  f 2  O f 2 
Example 3:
27 log n  19  n  On
f1 n 
f 2 (n)
Generalization: f1  O f 2   f1  f 2  O f 2 
Big O: Relationships
Suppose positive functions f1, f2, g1, g2, satisfy
f1  Og1  and
Then f1  f 2  Og1  g 2 
f 2  Og 2 
Why? There exist c1, c2, n1, n2 such that
f i n  ci  gi n for all n  ni
Take c0=c1c2, n0=max(n1,n2). Multiply to give
f1 n f n2  c0  g1 ng 2 n for all n  n0
Example:
12n
2

 
 2 log n 27 log n  19  n  O n3
f 2 (n)
f1 n 
Asymptotic: Relationships
Obviously, for any positive function g
g  Og 
Let  be a positive constant. Then   g  Og  as well.
 
Example 4: 13n3  O n3
Message: Bounding of growth rate. If n doubles, then the
bound grows by 8.
Example 5:
log n
 Olog n 
512
N times
Y = zeros(N,1); initialize space, c1N
initialize “for” loop, c2N
for i=1:N
Scalar assignment, c3
Y(i) = 0.0;
initialize “for” loop, c2N
for j=1:N
Y(i) = Y(i) + A(i,j)*x(j);
c4
End of loop, return/exit, c5
end
end
End of loop, return/exit, c5
N times
Example: N-by-N matrix, N-by-1 vector, multiply
Total = c6N2 + c7N
•Problem size affects (is, in fact) N
•Processor speed, processor and language
architecture, ie., technology, affect c6 and c7
Hence, “this algorithm of matrix-vector multiply has O(N2)
complexity.”
Time complexity familiar tasks
Task
Matrix/vector multiply
Getting a specific element from a list
Dividing a list in half, dividing one halve in half, etc
Binary Search
Scanning (brute force search) a list
Nested for loops (k levels)
MergeSort
BubbleSort
Generate all subsets of a set of data
Generate all permutations of a set of data
Growth rate
O(N2)
O(1)
O(log2N)
O(log2N)
O(N)
O(Nk)
O(N log2N)
O(N2)
O(2N)
O(N!)