  # Algorithms Theory 14 Dynamic Programming (3) Optimal binary - PowerPoint PPT Presentation

## Algorithms Theory 14 Dynamic Programming (3) Optimal binary search trees Prof. Dr. S. Albers Winter term 07/08 Method of dynamic programming Recursive approach: Solve a problem by solving several smaller analogous subproblems of the same

1. Algorithms Theory 14 – Dynamic Programming (3) Optimal binary search trees Prof. Dr. S. Albers Winter term 07/08

2. Method of dynamic programming Recursive approach: Solve a problem by solving several smaller analogous subproblems of the same type. Then combine these solutions to generate a solution to the original problem. Drawback: Repeated computation of solutions Dynamic-programming method: Once a subproblem has been solved, store its solution in a table so that it can be retrieved later by simple table lookup. Winter term 07/08 2

3. Optimal substructure Dynamic programming is typically applied to optimization problems . An optimal solution to the original problem contains optimal solutions to smaller subproblems. Winter term 07/08 3

4. Construction of optimal binary search trees (- ∞ ,k 1 ) k 1 (k 1 ,k 2 ) k 2 (k 2 ,k 3 ) k 3 (k 3 ,k 4 ) k 4 (k 4 , ∞ ) 4 1 0 3 0 3 0 3 10 3 k 2 1 3 k 1 k 4 4 0 3 10 (- ∞ , k 1 ) ( k 4 , ∞ ) ( k 1 , k 2 ) k 3 0 ( k 2 , k 3 ) 0 ( k 3 , k 4 ) weighted path length: 3 ⋅ 1 +2 ⋅ (1 + 3) + 3 ⋅ 3 + 2 ⋅ (4 + 10) Winter term 07/08 4

5. Construction of optimal binary search trees Given : set S of keys S = { k 1 ,....k n } - ∞ = k 0 < k 1 < ... < k n < k n+1 = ∞ a i : (absolute) frequency of requests to key k i b j : (absolute) frequency of requests to x ∈ ( k j , k j+1 ) Weighted path length P(T) of a binary search tree T for S : n n ∑ ∑ = + + P ( T ) ( depth ( k ) 1 ) a depth (( k , k )) b + i i j j 1 j = = i 1 j 0 Goal : Binary search tree with minimum weighted path length P for S . Winter term 07/08 5

6. Construction of optimal binary search trees 4 2 k 2 k 1 2 5 1 4 k 1 k 2 1 3 3 5 P(T 2 ) = 27 P(T 1 ) = 21 Winter term 07/08 6

7. Construction of optimal binary search trees a i k i b j k j , k j + 1 An optimal binary search tree is a binary search tree with minimum weighted path length. Winter term 07/08 7

8. Construction of optimal binary search trees T a k P ( T ) = P( T l ) + W( T l ) + P( T r ) + W( T r ) + a root k = P( T l ) + P(T r ) + W(T) mit W(T) := total weight of all nodes in T T l T r If T is a tree with minimum weighted path length for S , then subtrees T l and T r are trees with minimum weighted path length for subsets of S . Winter term 07/08 8

9. Construction of optimal binary search trees Let T(i, j): optimal binary search tree for (k i , k i+1 ) k i+1 ... k j (k j , k j+1 ), W(i, j): weight of T(i, j), i.e. W(i, j) = b i + a i+1 + ... + a j + b j , P(i, j): weighted path length of T(i, j). Winter term 07/08 9

10. Construction of optimal binary search trees T(i, j) = k l . . . . . . . . . . . . . . . . . . . . . . . . . . . . T(i, l - 1) . T(l, j) . . . . . . . . . . ( k i , k i + 1 ) k i + 1 ..... k l – 1 (k l –1 , k l ) ( k l , k l + 1 ) k l + 1 ..... k j (k j , k j + 1 ) . . . . . ...... ...... ...... ...... ...... ...... ...... ...... . . . request . frequency: b i a i + 1 a l – 1 b l – 1 a l b l a l + 1 a j b j Winter term 07/08 10

11. Construction of optimal binary search trees , for 0 ≤ i ≤ n W(i, i) = b i W(i, j) = W(i, j – 1) + a j + b j , for 0 ≤ i < j ≤ n , for 0 ≤ i ≤ n P(i, i) = 0 P(i, j) = W(i, j) + min { P(i, l –1 ) + P(l, j) }, for 0 ≤ i < j ≤ n (*) i < l ≤ j r(i, j) = the index l for which the minimum is achieved in (*) Winter term 07/08 11

12. Construction of optimal binary search trees Base cases Case 1: h = j – i = 0 T(i, i) = (k i , k i +1 ) W(i, i) = b i P(i, i) = 0, r(i, i) not defined Winter term 07/08 12

13. Construction of optimal binary search trees Case 2: h = j – i = 1 T(i, i+1) k i+1 a i+1 k i , k i+1 k i+1 , k i+2 b i b i+1 W(i ,i +1) = b i + a i+1 + b i+1 = W(i, i) + a i+1 + W(i+1, i+1) P(i, i+1) = W(i, i + 1) r(i, i +1) = i + 1 Winter term 07/08 13

14. Computating the minimum weighted path length using dynamic programming Case 3: h = j - i > 1 for h = 2 to n do for i = 0 to (n – h) do { j = i + h; determine (greatest) l, i < l ≤ j, s.t. P(i, l – 1) + P(l, j) is minimal P(i, j) = P(i, l –1) + P(l, j) + W(i, j); r(i, j) = l; } Winter term 07/08 14

15. Construction of optimal binary search trees Define: = ⎫ P ( i , j ) : min. weighted path length for ⎬ K b a b a b + + = i i 1 i 1 j j ⎭ W ( i , j ) : sum of Then: = ⎧ b if i j = i ⎨ W ( i , j ) − + + W ( i , j 1 ) a W ( j , j ) otherwise ⎩ j = ⎧ 0 if i j ⎪ { } = ⎨ + − + P ( i , j ) W ( i , j ) min P ( i , l 1 ) P ( l , j ) otherwise ⎪ < ≤ ⎩ i l j � Computing the solution P (0, n ) takes O ( n 3 ) time. and requires O ( n 2 ) space. Winter term 07/08 15

16. Construction of optimal binary search trees Theorem An optimal binary search tree for n keys and n + 1 intervals with known request frequencies can be constructed in O ( n 3 ) time. Winter term 07/08 16

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