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Efficiency of Algorithms

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1 Efficiency of Algorithms
Csci 107 Lecture 5

2 Last time Today Algorithms for Pattern matching
Find all occurences of target Find number of occurences of target Find number of values larger than target Find largest /smallest, sum, average Pattern matching Today Pattern matching algorithm Efficiency of algorithms Data cleanup algorithms Reading: start on Chapter 3, textbook

3 Pattern Matching Problem: Suppose we have a gene (text) T = TCAGGCTAATCGTAGG and a probe (pattern) P = TA. Design an algorithm that searches T to find the position of every instance of P that appears in T. E.g., for this text, the algorithm should return the answer: There is a match at position 7 There is a match at position 13 Algorithm: What is the idea? Check if pattern matches starting at position 1, then check if it matches starting at position 2,…and so on How to check if pattern matches text starting at position k? Check that every character of pattern matches corresponding character of text

4 Pattern Matching Input Output: Algorithm idea
Gene (text) of n characters T1, T2, …, Tn Probe (pattern) of m (m < n) characters P1, P2, …Pm Output: Location (index) of every occurrence of pattern within text Algorithm idea Get input (text and pattern) Set starting location k to 1 Repeat until reach end of text Attempt to match every character in the pattern beginning at pos k in text If there was a match, print k Add 1 to k Stop

5 Comparing Algorithms Algorithm There are many ways to solve a problem
Design Correctness Efficiency Also, clarity, elegance, ease of understanding There are many ways to solve a problem Conceptually Also different ways to write pseudocode for the same conceptual idea How to compare algorithms?

6 Efficiency of Algorithms
Efficiency: Amount of resources used by an algorithm Space (number of variables) Time (number of instructions) When designing an algorithm must be aware of its use of resources If there is a choice, pick the more efficient algorithm!

7 Efficiency of Algorithms
Does efficiency matter? Computers are so fast these days… Yes, efficiency matters a lot! There are problems (actually a lot of them) for which all known algorithms are so inneficient that they are impractical Remember the shortest-path-through-all-cities problem from Lab1…

8 Efficiency of Algorithms
How to measure time efficiency? Running time: let it run and see how long it takes On what machine? On what inputs? Time efficiency depends on input Example: the sequential search algorithm In the best case, how fast can the algorithm halt? In the worst case, how fast can the algorithm halt?

9 Time Efficiency We want a measure of time efficiency which is independent of machine, speed etc Look at an algorithm pseudocode and estimate its running time Look at 2 algorithm pseudocodes and compare them (Time) Efficiency of an algorithm: the number of pseudocode instructions (steps) executed Is this accurate? Not all instructions take the same amount of time… But..Good approximation of running time in most cases

10 (Time) Efficiency of an algorithm
worst case efficiency is the maximum number of steps that an algorithm can take for any input data values. best case efficiency is the minimum number of steps that an algorithm can take for any input data values. average case efficiency -the efficiency averaged on all possible inputs - must assume a distribution of the input - we normally assume uniform distribution (all keys are equally probable) If the input has size n, efficiency will be a function of n

11 Analysis of Sequential Search
Time efficiency Best-case : 1 comparison target is found immediately Worst-case: 3n + 5 comparisons Target is not found Average-case: 3n/2+4 comparisons Target is found in the middle Space efficiency How much space is used in addition to the input?

12 Worst Case Efficiency for Sequential Search
Get the value of target, n, and the list of n values 1 Set index to Set found to false Repeat steps 5-8 until found = true or index > n n 5 if the value of listindex = target then n Output the index Set found to true 0 8 else Increment the index by n 9 if not found then 10 Print a message that target was not found 0 Stop Total n+5

13 Order of Magnitude Worst-case of sequential search: Simplification:
3n+5 comparisons Are these constants accurate? Can we ignore them? Simplification: ignore the constants, look only at the order of magnitude n, 0.5n, 2n, 4n, 3n+5, 2n+100, 0.1n+3 ….are all linear we say that their order of magnitude is n 3n+5 is order of magnitude n: n+5 = (n) 2n +100 is order of magnitude n: 2n+100=(n) 0.1n+3 is order of magnitude n: 0.1n+3=(n) ….

14 Data Cleanup Algorithms
What are they? A systematic strategy for removing errors from data. Why are they important? Errors occur in all real computing situations. How are they related to the search algorithm? To remove errors from a series of values, each value must be examined to determine if it is an error. E.g., suppose we have a list d of data values, from which we want to remove all the zeroes (they mark errors), and pack the good values to the left. Legit is the number of good values remaining when we are done. d d2 d3 d4 d5 d d7 d8 Legit

15 Data Cleanup: Copy-Over algorithm
Idea: Scan the list from left to right and copy non-zero values to a new list Copy-Over Algorithm (Fig 3.2) Variables: n, A1, …, An, newposition, left, B1,…,Bn Get values for n and the list of n values A1, A2, …, An Set left to 1 Set newposition to 1 While left <= n do If Aleft is non-zero Copy A left into B newposition (Copy it into position newposition in new list Increase left by 1 Increase newposition by 1 Else increase left by 1 Stop

16 Data Cleanup: The Shuffle-Left Algorithm
Idea: go over the list from left to right. Every time we see a zero, shift all subsequent elements one position to the left. Keep track of nb of legitimate (non-zero) entries How does this work? How many loops do we need?

17 Shuffle-Left Algorithm (Fig 3.1)
Variables: n, A1,…,An, legit, left, right Get values for n and the list of n values A1, A2, …, An Set legit to n Set left to 1 Set right to 2 Repeat steps 6-14 until left > legit 6 if Aleftt ≠ 0 7 Increase left by 1 8 Increase right by 1 9 else 10 Reduce legit by 1 Repeat until right > n Copy Aight into Aright-1 Increase right by 1 14 Set right to left + 1 15 Stop

18 Exercising the Shuffle-Left Algorithm
d d2 d3 d4 d5 d d7 d8 legit

19 Data Cleanup: The Converging-Pointers Algorithm
Idea: One finger moving left to right, one moving right to left Move left finger over non-zero values; If encounter a zero value then Copy element at right finger into this position Shift right finger to the left

20 Converging Pointers Algorithm (Fig 3.3)
Variables: n, A1,…, An, legit, left, right Get values for n and the list of n values A1, A2,…,An Set legit to n Set left to 1 Set right to n Repeat steps 6-10 until left ≥ right If the value of Aleft≠0 then increase left by 1 Else Reduce legit by 1 Copy the value of Aright to Aleft 10 Reduce right by 1 if Aleft=0 then reduce legit by 1. Stop

21 Exercising the Converging Pointers Algorithm
d d2 d3 d4 d5 d d7 d8 legit

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