The document discusses the greedy method in algorithm design, which focuses on making the best immediate choice at each stage to achieve a global optimum. It presents examples, including real-life scenarios like job recruitment and optimization problems like the knapsack problem. Additionally, it highlights situations where the greedy algorithm may fail to provide the optimal solution.

1. Greedy Method
PRESENTED BY
JUBAIDA QUADER JERIN
COMPUTER SCIENCE AND ENGINEERING (DEPT.)
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2. Topics
• Definition
• Problem Statement
• Real Life Example
• Algorithm
• Fail Condition
• Examples
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3. Greedy Method/ Algorithm
• Algorithmic approach
• Best optimal choice at each small stages
• From Local Optimal Leading to Global Optimal Choice
A Greedy Algorithm always makes the choice that looks best at the moment
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4. Problem: I have to go from A to B with minimum Cost and within 12 Hours.
P: A B
12 hours
Minimum cost
S: Walk Car Train Plane Boat ……..
Feasible Solution
Optimal Solution
1. Multiple Solutions: walk, car etc.
2. Two Feasible Solutions: Train and Boat
3. One Optimal Solution: Train
Hence, Greedy Method is applied Optimization Problems
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5. Real Life Example
Job Recruitment Process
80% 70% 40% 20% 1%
Call Online Screening Viva Final
Written
Selected Candidate
What method are they using?
Ans: Greedy Method
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6. Greedy Algorithm (A, n)
{
Solution <- 0
for i in range 1 to n do
{
s<- select (A)
if (Feasible (Solution, s)) then
Solution <- Union (Solution, s)
}
return Solution
}
* A is Domain from which solution is to be obtained of size n
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7. When Greedy Algorithm fails?
Example:
10 7 1
• 3 coins
• Sums up to 15
• Minimum Number of Coins Coin 1 Coin 2 Coin 3
Greedy Method: 1x 10 + 5x 1 = 6 coins
Correct Answer: 2x 7 + 1x 1 = 3 coins
Why Greedy Algorithm failed?
Local optimal doesn’t always result in Global Optimal
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8. Greedy Algorithm Example:
1. Job Sequenced with Deadline
2. Knapsack Problem
3. Huffman Coding
4. MST
5. Dijkstra Algorithm
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9. Thank You
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