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Algorithms Algorithm: what is it ?

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Algorithms Algorithm: what is it ? Some representative problems : - Interval Scheduling

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Algorithms Algorithm: what is it ? Some representative problems : - Interval Scheduling - Bipartite Matching

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Algorithms Algorithm: what is it ? Some representative problems : - Interval Scheduling - Bipartite Matching - Independent Set

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Algorithms Algorithm: what is it ? Some representative problems : - Interval Scheduling - Bipartite Matching - Independent Set - Area of a Polygon

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Algorithms How to decide which algorithm is better ? Search problem Input : a sequence of n numbers (in an array A) and a number x Output : YES, if A contains x, NO otherwise

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Algorithms How to decide which algorithm is better ? Search problem Input : a sequence of n numbers (in an array A) and a number x Output : YES, if A contains x, NO otherwise What if A is already sorted ?

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Running Time O(n) – running time of the linear search O(log n) – running time of the binary search Def : Big-Oh (asymptotic upper bound) f(n) = O(g(n)) if there exists a constant c > 0 and a constant n 0 such that for every n ¸ n 0 we have f(n) · c g(n) Examples : n, n 3, log n, 2 n, 7n 2 + n 3 /3, 1, 1 + log n, n log n, n + log n

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Running Time Def : Big-Oh (asymptotic upper bound) f(n) = O(g(n)) if there exists a constant c > 0 and a constant n 0 such that for every n ¸ n 0 we have f(n) · c g(n) Example: Prove that n = O(n 3 )

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Running Time Def : Big-Oh (asymptotic upper bound) f(n) = O(g(n)) if there exists a constant c > 0 and a constant n 0 such that for every n ¸ n 0 we have f(n) · c g(n) Example: Prove that n 3 = O(7n 2 +n 3 /3)

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Running Time Def : Big-Oh (asymptotic upper bound) f(n) = O(g(n)) if there exists a constant c > 0 and a constant n 0 such that for every n ¸ n 0 we have f(n) · c g(n) Example: Prove that log 10 n = O(log n)

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Running Time Def : Big-Oh (asymptotic upper bound) f(n) = O(g(n)) if there exists a constant c > 0 and a constant n 0 such that for every n ¸ n 0 we have f(n) · c g(n) Example: what about 3 n and 2 n

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Running Time O(n) – running time of the linear search O(log n) – running time of the binary search Def : Big-Omega (asymptotic lower bound) f(n) = (g(n)) if there exists a constant c > 0 and a constant n 0 such that for every n ¸ n 0 we have f(n) ¸ c g(n) Examples : n, n 3, log n, 2 n, 7n 2 + n 3 /3, 1, 1 + log n, n log n, n + log n

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Running Time O(n) – running time of the linear search O(log n) – running time of the binary search Def : Theta (asymptotically tight bound) f(n) = (g(n)) if there exists constants c 1, c 2 > 0 and a constant n 0 such that for every n ¸ n 0 we have c 1 g(n) · f(n) · c 2 g(n) Examples : n, n 3, log n, 2 n, 7n 2 + n 3 /3, 1, 1 + log n, n log n, n + log n

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A survey of common running times 1. for i=1 to n do 2. something Linear Also linear : 1. for i=1 to n do 2. something 3. for i=1 to n do 4. something else

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A survey of common running times Example (linear time): Given is a point A=(a x, a y ) and n points (x 1,y 1 ), (x 2,y 2 ), …, (x n,y n ) specifying a polygon. Decide if A lies inside or outside the polygon.

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A survey of common running times Example (linear time): Given are n points (x 1,y 1 ), (x 2,y 2 ), …, (x n,y n ) specifying a polygon. Compute the area of the polygon.

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A survey of common running times Example (linear time): Given are n points (x 1,y 1 ), (x 2,y 2 ), …, (x n,y n ) specifying a polygon. Compute the area of the polygon.

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A survey of common running times O(n log n) Or: 1. for i=1 to n do 2. for j=1 to log(n) do 3. something 1. for i=1 to n do 2. j=n 3. while j>1 do 4. something 5. j = j/2

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A survey of common running times Quadratic 1. for i=1 to n do 2. for j=1 to n do 3. something

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A survey of common running times Cubic

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A survey of common running times O(n k ) – polynomial (if k is a constant)

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A survey of common running times Exponential, e.g., O(2 k )

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CSE Lecture 3 – Algorithms I

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