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Dynamic Programming. Algorithmic Strategies Divide and Conquer Greedy Approach Dynamic Programming.

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Presentation on theme: "Dynamic Programming. Algorithmic Strategies Divide and Conquer Greedy Approach Dynamic Programming."— Presentation transcript:

1 Dynamic Programming

2 Algorithmic Strategies Divide and Conquer Greedy Approach Dynamic Programming

3 Algorithm Strategies: Key Differences Divide and Conquer – Divide a problem into “disjoint” sub-problems – Solve the sub-problems first (bottom-up solution) – Combine the results somehow Greedy Approach – Apply a greedy choice first – The choice will create sub-problems to solve (top-down solution) – Hopefully, local optimal will lead to global optimal

4 Algorithm Strategies: Key Differences (Cont’d) Dynamic Programming – Divide a problem into sub-problems – Solve the sub-problems first, and then combine them to solve larger problem (bottom-up solution) – The sub-problems are overlapping Divide and conquer will solve the same sub-problem again ad again Dynamic programming will solve each sub-problem once, and remembers the answer – Similar to Greedy approach, it solves optimization problems Uses more space to save time

5 Dynamic Programming Trades space (to save the sub-problems solutions) to save time Can reduce exponential-time complexity to polynomial because of this extra storage Tries to search all possible combinations. At each step it will select the best combination for each sub-problem

6 Rod Cutting

7 Rod Cutting: Problem Definition Given a rod of length n with n-1 cutting points Also given a revenue for each length Find the best cutting for the rod that maximize the revenue

8 Example: Rod of size 4 How many possible cuttings? – Exponential  2 (n-1) = 2 3 = 8 Each cutting point is a random variable (0 or 1) – We have n-1 cutting points – The total possibilities is 2 (n-1) Naïve Approach (exponential) – Try all possibilities and select the maximum – Best choice: Two pieces each of size 2  revenue = = 10

9 Rod Cutting: Optimization Problem Rod cutting is an optimization problem Has the optimal sub-structure property – You must has the optimal cut for each sub-problem to get the global optimal Has recursive exponential solution Has polynomial dynamic programming solution

10 Recursive Top-Down Solution Where to have the first cut? (Position i above) This will create a sub-problem that will be solved recursively

11 Recursive Top-Down Solution Where to have the first cut? (Position i above) This will create a sub-problem that will be solved recursively If the first cut is at position i Solve the rest recursively Condition for ending the recursion Recursion Tree (for n = 4) i=1 i=2 i=4 i=3

12 Notice: Sub-Problems Repeat Recursion Tree (for n = 4) i=1 i=2 i=4 i=3 >> Sub-problem of size 2 is repeated twice >> Sub-problem of size 1 is repeated 4 times

13 Dynamic Programming Solution Store sub-problem results, don’t re-compute – Time vs. Memory trade-off Turn a exponential-time solution into a polynomial- time solution

14 Version 1: Top-down with Memoization r[x] is the best revenue for size x If this size is solved before, just return its optimal solution Same as before (i is the first-cut position, the rest is solved recursively)

15 Version 2: Bottom-Up Without Recursion Solve the sub-problems in order (smaller to larger) Size 0 has revenue 0 Increment the problem size by 1 each time This loop gets the maximum among all possible cuts All smaller sub-problems are already computed Time Complexity  O( n 2 ) Space Complexity  O(n) Time Complexity  O( n 2 ) Space Complexity  O(n)

16 Reconstructing The Optimal Solution So far, the algorithm tells you the optimal revenue But, does not tell how to get it Tells you for size 4, the optimal revenue is 10 !! We need extra space  Only store the 1 st -cut position for each size (not all positions) – S[4] = 2 // meaning the 1 st -cut position for size 4 is 2 – Now we have pieces, each of size 2 To know their cut, go to S[2]

17 Code Extension Problem size Max revenue 1 st cut position


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