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Analysis of Algorithms
AlgorithmInput Output
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Analysis of Algorithms 2
Outline and Reading
Running time (§1.1)Pseudo-code (§1.1)Counting primitive operations (§1.1)Asymptotic notation (§1.2)Asymptotic analysis (§1.2)Case study (§1.3.1, §1.4)
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Analysis of Algorithms 3
Running TimeThe running time of an algorithm varies with the input and typically grows with the input sizeAverage case difficult to determineWe focus on the worst case running time
Easier to analyze Crucial to applications
such as games, finance and robotics
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Analysis of Algorithms 4
Experimental Studies
Write a program implementing the algorithmRun the program with inputs of varying size and compositionUse a method like System.currentTimeMillis() to get an accurate measure of the actual running timePlot the results
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Analysis of Algorithms 5
Limitations of Experiments
It is necessary to implement the algorithm, which may be difficultResults 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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Analysis of Algorithms 6
Theoretical Analysis
Uses a high-level description of the algorithm instead of an implementationTakes into account all possible inputsAllows us to evaluate the speed of an algorithm independent of the hardware/software environment
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Analysis of Algorithms 7
PseudocodeHigh-level description of an algorithmMore structured than English proseLess detailed than a programPreferred notation for describing algorithmsHides program design issues
Algorithm arrayMax(A, n)Input array A of n integersOutput maximum element of A
currentMax A[0]for i 1 to n 1 do
if A[i] currentMax thencurrentMax A[i]
return currentMax
Example: find max element of an array
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Analysis of Algorithms 8
Pseudocode Details
Control flow if … then … [else …] while … do … repeat … until … for … do … Indentation replaces
braces
Method declarationAlgorithm method (arg [, arg…])
Input …
Output …
Method callvar.method (arg [, arg…])
Return valuereturn expression
Expressions Assignment
(like in Java) Equality testing
(like in Java)n2 Superscripts and
other mathematical formatting allowed
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Analysis of Algorithms 9
Primitive Operations
Basic computations performed by an algorithmIdentifiable in pseudocodeLargely independent from the programming languageExact definition not important (we will see why later)
Examples: Evaluating an
expression Assigning a
value to a variable
Indexing into an array
Calling a method Returning from a
method
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Analysis of Algorithms 10
Counting Primitive Operations
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 A[0] 2for i 1 to n 1 do 1 n
if A[i] currentMax then 2(n 1)currentMax A[i] 2(n 1)
{ increment counter i } 2(n 1)return currentMax 1
Total 7n 2
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Analysis of Algorithms 11
Estimating Running Time
Algorithm arrayMax executes 7n 2 primitive operations in the worst case Definea Time taken by the fastest primitive operationb Time taken by the slowest primitive operation
Let T(n) be the actual worst-case running time of arrayMax. We have
a (7n 2) T(n) b(7n 2)
Hence, the running time T(n) is bounded by two linear functions
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Analysis of Algorithms 12
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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Analysis of Algorithms 13
Growth Rates
Growth rates of functions:
Linear n Quadratic n2
Cubic n3
In a log-log chart, the slope of the line corresponds to the growth rate of the function
1E+01E+21E+41E+61E+8
1E+101E+121E+141E+161E+181E+201E+221E+241E+261E+281E+30
1E+0 1E+2 1E+4 1E+6 1E+8 1E+10n
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Analysis of Algorithms 14
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 function1E+01E+21E+41E+61E+8
1E+101E+121E+141E+161E+181E+201E+221E+241E+26
1E+0 1E+2 1E+4 1E+6 1E+8 1E+10n
T(n
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Quadratic
Quadratic
Linear
Linear
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Analysis of Algorithms 15
Big-Oh NotationGiven 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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Analysis of Algorithms 16
Big-Oh Notation (cont.)
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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Analysis of Algorithms 17
Big-Oh and Growth RateThe big-Oh notation gives an upper bound on the growth rate of a functionThe 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
f(n) is O(g(n)) g(n) is O(f(n))
g(n) grows more
Yes No
f(n) grows more No Yes
Same growth Yes Yes
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Analysis of Algorithms 18
{n3}
{n2}
Classes of Functions
Let {g(n)} denote the class (set) of functions that are O(g(n))
We have{n} {n2} {n3} {n4} {n5} … where the containment is strict
{n}
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Analysis of Algorithms 19
Big-Oh Rules
If is f(n) a polynomial of degree d, then f(n) is O(nd), i.e.,
1. Drop lower-order terms
2. 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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Analysis of Algorithms 20
Asymptotic Algorithm Analysis
The asymptotic analysis of an algorithm determines the running time in big-Oh notationTo 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 7n 1 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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Analysis of Algorithms 21
Computing Prefix Averages
We further illustrate asymptotic analysis with two algorithms for prefix averagesThe i-th prefix average of an array X is average of the first (i 1) elements of X
A[i] X[0] X[1] … X[i]
Computing the array A of prefix averages of another array X has applications to financial analysis
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Analysis of Algorithms 22
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 integersOutput array A of prefix averages of X #operations A new array of n integers nfor i 0 to n 1 do n
s X[0] nfor j 1 to i do 1 2 … (n 1)
s s X[j] 1 2 … (n 1)A[i] s (i 1) n
return A 1
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Analysis of Algorithms 23
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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Analysis of Algorithms 24
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 integersOutput array A of prefix averages of X #operationsA new array of n integers ns 0 1for i 0 to n 1 do n
s s X[i] nA[i] s (i 1) n
return A 1Algorithm prefixAverages2 runs in O(n) time