![We notice that if a[i] = b[j] (connected), then we can use them in our solution and
optimal solution becomes c[i-1,j-1]+1(because we just used a new route).
In all other cases, we just consider the case where we do not use b[j] (i.e c[i,j-1])
or we do not use a[i] (i.e c[i-1,j]
Dynamic Programming](https://image.slidesharecdn.com/dynamicprogramming-190319174538/85/Dynamic-programming-10-320.jpg)
Dynamic programming
- 1. Dynamic Programming • Is a powerful design technique that can be used to solve problems of a computational nature • Is typically applied to optimization problems • Note:- Programming in these term refers to a tabular method.
- 2. Dynamic Programming • Problem: – The Palmia country is divided by a river into the north and south bank. There are N towns on both the north and south bank. Each town on the north bank has its unique friend town on the south bank. No two towns have the same friend. Each pair of friend towns would like to have a ship line connecting them. They applied for permission to the government. Because it is often foggy on the river the government decided to prohibit intersection of ship lines (if two lines intersect there is a high probability of ship crash).
- 3. Dynamic Programming • Problem (in mathematical terms) – There exists a sequences a1,, a2,… an and b1,,b2,… bn such that as < aj if s<j bs < bj if s<j for each s, there exists a s’ such that as and bs’ connected. - Find the maximum value m such that there exist x1 <x2<x3 <… xm. m<=n such that
- 4. Dynamic Programming • Example Problem a: 2 4 9 10 15 17 22 b: 2 3 4 6 8 12 17
- 5. Dynamic Programming • Example Problem a: 2 4 9 10 15 17 22 b: 2 3 4 6 8 12 17
- 6. Dynamic Programming • Simple Brute Force Algorithm • Pick all possible sets of routes. • Check if selection of routes is valid
- 7. Dynamic Programming • Simple Brute Force Algorithm (Analysis) • Pick all possible sets of routes. …. O(2n) possible routes • Check if selection of routes is valid… O(n) time to check • In total will take O(2n * n) time. • EXPONENTIAL TIME = SLOW!!!!!
- 8. Dynamic Programming • Elegant Solution • Let c(i,j) be the maximum possible routes using the first i sequences in a, and j sequences in b. • We notice that c(I,j) is connected to c(i- 1,j), c(i,j-1) and c(i-1,j-1).
- 9. Dynamic Programming RULE 1 of Dynamic Programing
- 10. We notice that if a[i] = b[j] (connected), then we can use them in our solution and optimal solution becomes c[i-1,j-1]+1(because we just used a new route). In all other cases, we just consider the case where we do not use b[j] (i.e c[i,j-1]) or we do not use a[i] (i.e c[i-1,j] Dynamic Programming
- 14. Dynamic Programming RULE 2 of Dynamic Programing
- 16. Dynamic Programming RULE 3 of Dynamic Programing