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Time Complexity

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Solving a computational program Describing the general steps of the solution –Algorithm’s course Use abstract data types and pseudo code –Data structures course Implement the solution using a programming language and concrete data structures –Introduction to computer science

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Pseudo Code Pseudo code is a meta language, used to describe an algorithm for a computer program. We use a notation similar to C or Pascal programming language. Unlike real code pseudo code uses a free syntax to describe a given problem

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Pseudo Code Pseudo code does not deal with problems regarding a specific programming language, as data abstraction and error checking Use indentation to distinguish between blocks of code (loop and conditional statements) Accessing values of an array is expressed with brackets

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Pseudo code Accessing an attribute of an object is expressed by the attribute name followed by the object in brackets (length[a]) More on pseudo code style in the text book

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What is the running time of these methods ? Proc2(A[1..n]) i 1; j 1; s 0 repeat if A[i] < A[j] s s + 1 if j=n j=i + 1; i i+1 else j j+1 until i+j > 2n Proc1(A[1..n]) s 0 for i = 1.. n do for j = (i +1).. n do if A[i] < A[j] s s + 1

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Analysis of Bubble sort Bubble sort (A) –1. n length[A] –2. for j n-1 to 1 –3. for i 0 to j – 1 –4.if A[i] > A[i + 1] –5. Swap (A[i], A[i +1])

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Time Analysis Best case – the array is already sorted and therefore no swap operations are required

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Time Analysis Worst case – the array is sorted in descending order and therefore all swap operations will be executed For both inputs the solution requires time

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Asymptotic Notation Considering two algorithms, A and B, and the running time for each algorithm for a problem of size n is T A (n) and T B (n) respectively It should be a fairly simple matter to compare the two functions T A (n) and T B (n) and determine which algorithm is the best!

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Asymptotic Notation Suppose the problem size is n 0 and that T A (n 0 ) < T B (n 0 ) Then clearly algorithm A is better than algorithm B for problem size n 0 What happens if we do not know the problem size in advance ? If we can show that T A (n) < T B (n) regardless of n then algorithm A is better then algorithm B regardless of the problem size Since we don’t know size in advance we tend to compare the asymptotic behavior of the two.

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Asymptotic Upper Bound - O O(g(n)) is the group of all functions f(n) which are non-negative for all integers, if there exists an integer n 0 and a constant c>0 such that for all integers

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Big O notation f(n) cg(n)

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Example

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Asymptotic lower Bound - is the group of all functions f(n) which are non-negative for all integers and if there exists an integer n 0 and a constant c>0 such that for all integers

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Asymptotic lower Bound - cg(n) f(n)

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Example

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Asymptotic tight bound - is the group of all functions f(n) which are non-negative for all integers and if there exists an integer n 0 and two constants such that for all integers

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Asymptotic tight Bound - cg(n) f(n) dg(n)

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Example

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Asymptotic Notation When we use the term f = O(n) we mean that the function f O(n) When we write we mean that the aside from the function the sum includes an additional function from O(n) which we have no interest of stating explicitly

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Example Show that the function f(n)=8n+128 =O(n 2 ) –lets choose c = 1, then

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Conventions for using Asymptotic notation drop all but the most significant terms –Instead of we write drop constant coefficients –Instead of we use

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Back to improved bubble sort We improve the sorting algorithm so that if in a complete iteration over the array no swap operations were performed, the execution stops Best case – Worst case – Average case –

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Comparing functions

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Example Compare the functions

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Example Compare the functions The logarithmic base does not change the order of magnitude

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Example Compare the functions

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Properties of asymptotic notation Transitivity:

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Properties of asymptotic notation Symmetry

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Properties of asymptotic notation Reflexivity:

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Example Give a proof or a counter example to the following statements:

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Example

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Example

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Example

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Example Show that –1. –2.

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Example Is it true that for any two functions f,g either f=O(g) or g=O(f) ?

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September 17, 2001 Algorithms and Data Structures Lecture II Simonas Šaltenis Nykredit Center for Database Research Aalborg University

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