CS代考 COMP90038 – cscodehelp代写

COMP90038
Algorithms and Complexity
Lecture 3: Growth Rate and Algorithm Efficiency (with thanks to Harald Søndergaard)

DMD 8.17 (Level 8, Doug McDonell Bldg)
http://people.eng.unimelb.edu.au/tobym @tobycmurray

Algorithm Efficiency
Two algorithms for computing gcd:
Why is one more efficient than the other?
What does “efficient” even mean?
How can we talk about these things precisely?
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Assessing Algorithm “Efficiency”
Resources consumed: time and space
We want to assess efficiency as a function of input
Mathematical vs empirical assessment Average case vs worst case
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size
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Knowledge about input peculiarities may affect the
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choice of algorithm
The right choice of algorithm may also depend on
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the programming language used for implementation

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Running Time Dependencies
There are many things that a program’s running time
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depends on:
1.Complexity of the algorithms used
2.Input to the program
3.Underlying machine, including memory architecture 4.Language/compiler/operating system
Since we want to compare algorithms we ignore (3) and Use a natural number n to quantify (2)—size of the input
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(4); just consider units of time
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Express (1) as a function of n
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Linear Search Example
function find(A,x,n) j←0
while j < n if A[j] = x return j j ← j+1 n = the length of the array How should we quantify the cost to run this algorithm? roughly, number of times the loop runs (later in this lecture we will be more precise) return -1 How should we measure the size, n, of the input to this algorithm? Linear Search Example 6 (since the loop runs n times in that case) Copyright University of Melbourne 2016, provided under Creative Commons Attribution License function find(A,x,n) j←0 while j < n if A[j] = x return j j ← j+1 return -1 What is the worst case input? an array that doesn’t contain the item, x, we are searching for Worst case time complexity: n Linear Search Example 7 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License function find(A,x,n) j←0 while j < n if A[j] = x return j j ← j+1 return -1 What is the best case input? an array that has the item, x, we are searching for in the first position Best case time complexity: 1 (since the loop runs once in that case) 8 then running time t(n) ≈ c ⋅ g(n) Copyright University of Melbourne 2016, provided under Creative Commons Attribution License Estimating Time Consumption Number of loop iterations is not a good estimate of running • time. Better is to identify the algorithm’s basic operation and • how many times it is performed • Basic Operation is the most important operation the algorithm performs: the one that it could not be implemented without If c is the cost of a basic operation and g(n) is the number • of times the operation is performed for input size n, Linear Search Example 9 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License function find(A,x,n) j←0 while j < n if A[j] = x return j j ← j+1 return -1 What is the basic operation here? the comparison A[j] = x Rule of thumb: the most expensive operation executed each time in the inner-most loop of the program Examples: Input Size and Basic Operation Problem Size Measure Basic Operation Key comparison Search in a list of n items n Multiply two Matrix size matrices of floats (rows x columns) Float multiplication Compute an log n Float multiplication Graph problem Number of nodes Visiting a node and edges 10 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License 11 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License Best, Average and Worst Case The running time t(n) may well depend on more than just n Worse case: analysis makes the most pessimistic assumptions about the input Best case: analysis makes the most optimistic assumptions about the input Average case: analysis aims to find the expected running time across all possible input of size n (Note: not an average of the worst and best cases) • • • • Amortised analysis takes context of running an algorithm • into account, calculates cost spread over many runs. Used for “self-organising” data structures that adapt to their usage Large Input is what Matters Small input does not properly stress an algorithm for small values of m and n, their cost is similar • 12 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License Only as we let m and n grow large do we witness • (big) differences in performance. 13 Guessing Game Example Guess which number I am thinking of, between 1 and n (inclusive). I will tell you if it is higher or lower than each guess. 5051 75 100 Wrong. My number is lhoiwgheer rththaann750. . We are halving the search space each time. Basic operation: guessing a number. (Worse case) complexity: log n Copyright University of Melbourne 2016, provided under Creative Commons Attribution License • 1 The Tyranny of Growth Rate 3 101 3 ·101 102 103 103 4 · 106 7 102 7 · 102 104 106 1030 9 · 10157 10 103 1 · 104 106 109 - - 1030 is 1,000 times the number of nano-seconds since the Big Bang. At a rate of a trillion (1012) operations per second, executing 2100 
 operations would take a computer in the order of 1010 years. That is more than the estimated age of the Earth n log2 n n n log2 n n2 n3 2n n! 101 102 103 14 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License The Tyranny of Growth Rate 15 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License 16 Functions Often Met in Algorithm Classification • 1: Running time independent of input log n: typical for “divide an conquer” solutions, for example • lookup in a balanced search tree • • Linear (n): When each input must be processed once n log n: Each input element processed once and processing involves other elements too, for example, sorting. n2, n3: Quadratic, cubic. Processing all pairs (triples) of • elements. • 2n: Exponential. Processing all subsets of elements. Copyright University of Melbourne 2016, provided under Creative Commons Attribution License 17 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License Asymptotic Analysis • • • We are interested in the growth rate of functions Ignore constant factors Ignore small input sizes 18 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License Asymptotics f(n) ≺ g(n) iff lim f(n) = 0 • • • • g(n) 1≺logn≺nε ≺nc ≺nlogn ≺cn ≺nn n→∞ That is, g approaches infinity faster than f where 0 < ε < 1 < c In asymptotic analysis, think big! e.g., log n ≺ n0.0001, even though for n = 10100, • 100 > 1.023.
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Try it for n = 101000000

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Big-Oh Notation
O(g(n)) denotes the set of functions that grow no Formal definition: We write
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faster than g, asymptotically.
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t(n) ∈ O(g(n)) when, for some c and n0 



n > n0 ⇒ t(n) < c ·g(n) For example: 1 + 2 + ... + n ∈ O(n2) Copyright University of Melbourne 2016, provided under Creative Commons Attribution License Big-Oh: What t(n) ∈ O(g(n)) Means 20 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License 21 Big-Oh Pitfalls Levitin’s notation t(n) ∈ O(g(n)) is meaningful, but Other authors use t(n) = O(g(n)) for the same thing. As O provides an upper bound, it is correct to say both 3n ∈ O(n2) and 3n ∈ O(n) (so you can see why using ‘=’ is confusing); the latter, 3n ∈ O(n), is of course more precise and useful. Note that c and n0 may be large. Copyright University of Melbourne 2016, provided under Creative Commons Attribution License • not standard. • • • 22 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License Big-Omega and Big-Theta Big Omega: Ω(g(n)) denotes the set of functions for lower bounds. • that grow no slower than g, asymptotically, so Ω is • • t(n) ∈ Θ(g(n)) iff t(n) ∈ O(g(n)) and t(n) ∈ Ω(g(n)). t(n) ∈ Ω(g(n)) iff n >n0 ⇒ t(n) > c ·g(n), Big Theta: Θ is for exact order of growth.
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for some n0 and c.

Big-Omega: What t(n) ∈ Ω(g(n)) Means
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Big-Theta: What t(n) ∈ Θ(g(n)) Means
24 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License

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Establishing Growth Rate
We can use the definition of O directly.
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t(n) ∈ O(g(n)) iff: n > n0 ⇒ t(n) < c ·g(n) Exercise: use this to show that Also show that: • 1 + 2 + ... + n ∈ O(n2) • 17n2 + 85n + 1024 ∈ O(n2) 1 + 2 + ... + n ∈ O(n2) Find some c and n0 such that, for all n > n0
1 + 2 + … + n < c · n2 1 + 2 +... + n n(n+1) 2 n2 + n 2 n2 + n n2 + n2 Choosen0 =1,c=2 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License = = < < = (for n > 0) (for n > 1)
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2n2

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17n2 + 85n + 1024 ∈ O(n2)
Find some c and n0 such that, for all n > n0
17n2 + 85n + 1024 < c · n2 Guess c = 18 Need to prove: 17n2 + 85n + 1024 < 18n2 i.e. 85n + 1024 < n2 Guess n0 = 1024 Check if: 85n0+ 1024 < n02 85·1024 + 1024 < 1024·1024 i.e. 86·1024 < 1024·1024 Clearly true. Choose c = 18, n0 = 1024 28 17n2 + 85n + 1024 < c · n2 Alternative: Let c = 17 + 85 + 1024 17n2 + 85n + 1024 < 17n2 + 85n2 + 1024n2 (for n > 1)
= (17 + 85 + 1024)n2 Choosec=17+85+1024,n0 =1
Of course, this works for any polynomial. Copyright University of Melbourne 2016, provided under Creative Commons Attribution License
17n2 + 85n + 1024 ∈ O(n2)
Find some c and n0 such that, for all n > n0