Unit-5 Dynamic Programming 2016

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1 5 Dynamic programming Overview, Applications - shortest path in graph, matrix multiplication, travelling salesman problem, Fibonacci Series. 20% 12 Origin: Richard Bellman, 1957 Programming referred to a series of choices Dynamic: choices are made on the fly, not in beginning Dynamic programming (usually referred to as DP ) is a very powerful technique to solve a particular class of problems. The idea is simple, If you have solved a problem with the given input, then save the result for future reference, so as to avoid solving the same problem again. If the given problem can be broken up in to smaller sub-problems and these smaller subproblems are in turn divided in to still-smaller ones, and in this process, if you observe some over-lappping subproblems, then dynamic programming can be applied. Also, the optimal solutions to the subproblems contribute to the optimal solution of the given problem which is referred to as the Optimal Substructure Property. A method for solving complex problems by breaking them up into sub-problems first. This technique can be used when a given problem can be split into overlapping sub-problems and when there is an optimal sub-structure to the problem. It can be used to solve many problems in time O(n 2 ) or O(n 3 ) for which a naive approach would take exponential time. Dynamic programming is a technique for solving problems recursively and is applicable when the computations of the subproblems overlap. Dynamic programming is typically implemented using tabulation, but can also be implemented using memoization There are two ways of doing this. 1.) Top-Down : Start solving the given problem by breaking it down. If you see that the problem has been solved already, then just return the saved answer. If it has not been solved, solve it and save the answer. This is usually easy to think of and very intuitive. This is referred to as Memoization. If you use memoization to solve the problem you do it by maintaining a map of already solved sub problems. You do it "top down" in the sense that you solve the "top" problem first (which typically recurses down to solve the sub-problems). 2.) Bottom-Up : Analyze the problem and see the order in which the sub-problems are solved and start solving from the trivial subproblem, up towards the given problem. In this process, Analysis and Design of Algorithm Page 1

2 it is guaranteed that the subproblems are solved before solving the problem. This is referred to as Tabulation. When you solve a dynamic programming problem using tabulation you solve the problem "bottom up", i.e., by solving all related sub-problems first, typically by filling up an n- dimensional table. Based on the results in the table, the solution to the "top" / original problem is then computed. Principle of Optimality (Optimal Substructure Property) A problem is said to have optimal substructure if an optimal solution can be constructed efficiently from optimal solutions of its subproblems. This property is used to determine the usefulness of dynamic programming and greedy algorithms for a problem. Suppose that in solving a problem, we have to make a sequence of decisions D1, D2 Dn. If this sequence is optimal, then the last k decisions, 1 k n must be optimal. Ex: The shortest path problem If i1, i2 j is a shortest path from i to j, then i1, i2 j must be a shortest path from i1to j Difference Between Dynamic Programming and Divide and Conquer: Dynamic programming is similar to the divide-and-conquer approach in that the solution of a large problem depends on previously obtained solutions to easier subproblems. The significant difference, however, is that dynamic programming permits subproblems to overlap. By overlap, we mean that the subproblem can be used in the solution of two different subproblems. In contrast, the divide-and-conquer approach creates subproblems that are completely separate and can be solved independently. the problem to be solved is shown as the root of a tree, where children are easier subproblems. The leaves of the trees are trivial subproblems that can be solved directly in D.P., these leaves are often the input to the algorithm. The primary difference between divide and conquer and D.P. is clear: subproblems in divide and conquer do not interact, while in D.P., they might. Analysis and Design of Algorithm Page 2

The primary difference is that the subproblems of the divide-and-conquer approach are independent, while in dynamic programming they interact.

3 The primary difference is that the subproblems of the divide-and-conquer approach are independent, while in dynamic programming they interact. Also, dynamic programming solves problems in a bottom-up manner as opposed to divide-and-conquer s top-down approach. A second difference is also illustrated by the figure: while the divide-and-conquer approach is recursive, top-down, D.P is best thought as bottom up. Example The following computer problems can be solved using dynamic programming approach Fibonacci number series Knapsack problem Tower of Hanoi All pair shortest path by Floyd-Warshall Shortest path by Dijkstra Project scheduling Dynamic programming can be used in both top-down and bottom-up manner. Most of the times, referring to previous solution output is cheaper than re-computing in terms of CPU cycles thus by reduction in time complexity. Application: 1) Fibonacci Series 1. Consider the Fibonacci Series : 0,1,1,2,3,5,8,13,21... Analysis and Design of Algorithm Page 3

4 F(0)=0 ; F(1) = 1; F(N)=F(N-1)+F(N-2) Pseudo-code for a simple recursive function will be : fib(int n) { if (n==0) return 0; if (n==1) return 1; return fib(n-1)+fib(n-2); Using the above function, if we want to compute fib(7), we end up making a tree of calls which computes the function on the same value, several times : fib(6) = fib(5) + fib(4) fib(6) = ( fib(4)+fib(3) ) + ( fib(3) + fib(2) ) fib(6) = ( (fib(3) + fib(2)) + ( fib(2) + fib(1)) ) + ( (fib(2) + fib(1)) + (fib(1) + fib(0) ) fib(6) = ( ( ( fib(2) + fib(1) ) +( fib(1) + fib(0) ) ) + ( ( fib(1) + fib(0) ) + fib(1) ) ) + ( ( (fib(1)+fib(0)) + fib(1) ) + ( fib(1) + fib(0) ) ) fib(6) = ( ( ((fib(1)+fib(0)) + fib(1) ) +( fib(1) + fib(0) ) ) + ( ( fib(1) + fib(0) ) + fib(1) ) ) + ( ( (fib(1)+fib(0)) + fib(1) ) + ( fib(1) + fib(0) ) ) As you can see : fib(5) has been computed once. fib(4) has been computed 2 times. fib(3) has been computed 3 times. fib(2) has been computed 5 times. Example: Write a function int fib(int n) that returns F n. For example, if n = 0, then fib() should return 0. If n = 1, then it should return 1. For n > 1, it should return F n-1 + F n-2 Method 1 ( Use recursion ) A simple method that is a direct recusrive implementation mathematical recurance relation given above. #include<stdio.h> int fib(int n) { if (n <= 1) return n; return fib(n-1) + fib(n-2); Analysis and Design of Algorithm Page 4

5 int main () { int n = 9; printf("%d", fib(n)); getchar(); return 0;. Time Complexity: T(n) = T(n-1) + T(n-2) which is exponential Extra Space: O(n) if we consider the function call stack size, otherwise O(1). fib(5) / \ fib(4) fib(3) / \ / \ fib(3) fib(2) fib(2) fib(1) / \ / \ / \ fib(2) fib(1) fib(1) fib(0) fib(1) fib(0) / \ fib(1) fib(0) Method 2 ( Use Dynamic Programming ) We can avoid the repeated work done is the method 1 by storing the Fibonacci numbers calculated so far. #include<stdio.h> int fib(int n) { /* Declare an array to store Fibonacci numbers. */ int f[n+1]; int i; /* 0th and 1st number of the series are 0 and 1*/ f[0] = 0; f[1] = 1; for (i = 2; i <= n; i++) { /* Add the previous 2 numbers in the series and store it */ f[i] = f[i-1] + f[i-2]; return f[n]; int main () { Analysis and Design of Algorithm Page 5

6 int n = 9; printf("%d", fib(n)); getchar(); return 0; Time Complexity: O(n) Extra Space: O(n) Analysis and Design of Algorithm Page 6

7 Travelling Salesman Problem (TSP): Given a set of cities and distance between every pair of cities, the problem is to find the shortest possible route that visits every city exactly once and returns to the starting point. TSP problem can be described as: find a tour of N cities in a country (assuming all cities to be visited are reachable), the tour should (a) visit every city just once, (b) return to the starting point and (c) be of minimum distance. Algorithm : Number the cities 1, 2,..., N and assume we start at city 1, and the distance between city i and city j is d ij. Consider subsets S {2,...,N of cities and, for c S, let D(S, c) be the minimum distance, starting at city 1, visiting all cities in S and finishing at city c. First phase: if S = {c, then D(S, c) = d 1,c. Otherwise: D(S, c) = min x S c (D(S c, x) + d x,c ) Second phase: the minimum distance for a complete tour of all cities is M = min c {2,...,N (D({2,..., N, c) + d c,1 ) A tour n 1,..., n N is of minimum distance just when it satisfies M = D({2,..., N, n N ) + d nn,1. Pseucodode: function algorithm TSP (G, n) for k := 2 to n do C({1, k, k) := d 1,k end for for s := 3 to n do for all S {1, 2,..., n, S = s do for all k S do {C(S, k) = min m 1,m k,m S [C(S {k, m) + d m,k ] end for end for end for opt := min k 1 [C({1, 2, 3,..., n, k) + d k,1 ] return (opt) end Analysis and Design of Algorithm Page 7

8 Example: Distance matrix: Functions description: g(x, S) - starting from 1, path min cost ends at vertex x, passing vertices in set S exactly once c xy - edge cost ends at x from y p(x, S) - the second-to-last vertex to x from set S. Used for constructing the TSP path back at the end. k = 0, null set: Set : g(2, ) = c 21 = 1 g(3, ) = c 31 = 15 g(4, ) = c 41 = 6 k = 1, consider sets of 1 element: Set {2: g(3,{2) = c 32 + g(2, ) = c 32 + c 21 = = 8 p(3,{2) = 2 g(4,{2) = c 42 + g(2, ) = c 42 + c 21 = = 4 p(4,{2) = 2 Set {3: g(2,{3) = c 23 + g(3, ) = c 23 + c 31 = = 21 p(2,{3) = 3 g(4,{3) = c 43 + g(3, ) = c 43 + c 31 = = 27 p(4,{3) = 3 Set {4: g(2,{4) = c 24 + g(4, ) = c 24 + c 41 = = 10 p(2,{4) = 4 g(3,{4) = c 34 + g(4, ) = c 34 + c 41 = = 14 p(3,{4) = 4 k = 2, consider sets of 2 elements: Set {2,3: g(4,{2,3) = min {c 42 + g(2,{3), c 43 + g(3,{2) = min {3+21, 12+8= min {24, 20= 20 p(4,{2,3) = 3 Set {2,4: Analysis and Design of Algorithm Page 8

9 g(3,{2,4) = min {c 32 + g(2,{4), c 34 + g(4,{2) = min {7+10, 8+4= min {17, 12 = 12 p(3,{2,4) = 4 Set {3,4: g(2,{3,4) = min {c 23 + g(3,{4), c 24 + g(4,{3) = min {6+14, 4+27= min {20, 31= 20 p(2,{3,4) = 3 Length of an optimal tour: f = g(1,{2,3,4) = min { c 12 + g(2,{3,4), c 13 + g(3,{2,4), c 14 + g(4,{2,3) = min {2 + 20, , = min {22, 21, 30 = 21 Successor of node 1: p(1,{2,3,4) = 3 Successor of node 3: p(3, {2,4) = 4 Successor of node 4: p(4, {2) = 2 The optimal TSP tour reaches: The worst-case time complexity of this algorithm is O(2 n n 2 ) and the space O(2 n n). Analysis and Design of Algorithm Page 9

10 Matrix Chain Multiplication Problem: Given a sequence of matrices, find the most efficient way to multiply these matrices together. The problem is not actually to perform the multiplications, but merely to decide in which order to perform the multiplications. We have many options to multiply a chain of matrices because matrix multiplication is associative. In other words, no matter how we parenthesize the product, the result will be the same. For example, if we had four matrices A, B, C, and D, we would have: (ABC)D = (AB)(CD) = A(BCD) =... However, the order in which we parenthesize the product affects the number of simple arithmetic operations needed to compute the product, or the efficiency. For example, suppose A is a matrix, B is a 30 5 matrix, and C is a 5 60 matrix. Then, (AB)C = ( ) + ( ) = = 4500 operations A(BC) = ( ) + ( ) = = operations. Clearly the first parenthesization requires less number of operations. Given an array p[] which represents the chain of matrices such that the ith matrix Ai is of dimension p[i-1] x p[i]. We need to write a function MatrixChainOrder() that should return the minimum number of multiplications needed to multiply the chain. Example: Input: p[] = {40, 20, 30, 10, 30 Output: There are 4 matrices of dimensions 40x20, 20x30, 30x10 and 10x30. Let the input 4 matrices be A, B, C and D. The minimum number of multiplications are obtained by putting parenthesis in following way (A(BC))D --> 20*30* *20* *10*30 Input: p[] = {10, 20, 30, 40, 30 Output: There are 4 matrices of dimensions 10x20, 20x30, 30x40 and 40x30. Let the input 4 matrices be A, B, C and D. The minimum number of multiplications are obtained by putting parenthesis in following way ((AB)C)D --> 10*20* *30* *40*30 Input: p[] = {10, 20, 30 Output: 6000 There are only two matrices of dimensions 10x20 and 20x30. So there is only one way to multiply the matrices, cost of which is 10*20*30 Analysis and Design of Algorithm Page 10

11 Dynamic Programming Approach Unit-5 Dynamic Programming 2016 The first step of the dynamic programming paradigm is to characterize the structure of an optimal solution. For the chain matrix problem, like other dynamic programming problems, involves determining the optimal structure (in this case, a parenthesization). We would like to break the problem into subproblems, whose solutions can be combined to obtain a solution to the global problem. For convenience, let us adopt the notation A i.. j, where i j, for the result from evaluating the product A i A i A j. That is, A i.. j A i A i A j, where i j, It is easy to see that is a matrix A i.. j is of dimensions p i p i + 1. In parenthesizing the expression, we can consider the highest level of parenthesization. At this level we are simply multiplying two matrices together. That is, for any k, 1 k n 1, A 1..n = A 1..k A k+1..n. Therefore, the problem of determining the optimal sequence of multiplications is broken up into two questions: Question 1: How do we decide where to split the chain? (What is k?) Question 2: How do we parenthesize the subchains A 1..k A k+1..n? The subchain problems can be solved by recursively applying the same scheme. On the other hand, to determine the best value of k, we will consider all possible values of k, and pick the best of them. Notice that this problem satisfies the principle of optimality, because once we decide to break the sequence into the product, we should compute each subsequence optimally. That is, for the global problem to be solved optimally, the subproblems must be solved optimally as well. The key observation is that the parenthesization of the "prefix" subchain A 1..k within this optimal parenthesization of A 1..n. must be anoptimal parenthesization of A 1..k. Step:2 The second step of the dynamic programming paradigm is to define the value of an optimal solution recursively in terms of the optimal solutions to subproblems. To help us keep track of solutions to subproblems, we will use a table, and build the table in a bottomup manner. For 1 i j n, let m[i, j] be the minimum number of scalar multiplications needed to compute the A i..j. The optimum cost can be described by the following recursive formulation. Basis: Observe that if i = j then the problem is trivial; the sequence contains only one matrix, and so the cost is 0. (In other words, there is nothing to multiply.) Thus, m[i, i] = 0 for i = 1, 2,..., n. Step: If i j, then we are asking about the product of the subchain A i..j and we take advantage of the structure of an optimal solution. We assume that the optimal parenthesization splits the product, A i..j into for each value of k, 1 k n 1 as A i..k. A k+1..j. Analysis and Design of Algorithm Page 11

12 The optimum time to compute is m[i, k], and the optimum time to compute is m[k + 1, j]. We may assume that these values have been computed previously and stored in our array. Since A i..k is a matrix, and A k+1..j is a matrix, the time to multiply them is p i 1. p k. p j. This suggests the following recursive rule for computing m[i, j]. To keep track of optimal subsolutions, we store the value of k in a table s[i, j]. Recall, k is the place at which we split the product A i..j to get an optimal parenthesization. That is, s[i, j] = k such that m[i, j] = m[i, k] + m[k + 1, j] + p i 1. p k. p j. Step-3: The third step of the dynamic programming paradigm is to construct the value of an optimal solution in a bottom-up fashion. It is pretty straight forward to translate the above recurrence into a procedure. As we have remarked in the introduction that the dynamic programming is nothing but the fancy name for divide-and-conquer with a table. But here in dynamic programming, as opposed to divide-and-conquer, we solve subproblems sequentially. It means the trick here is to solve them in the right order so that whenever the solution to a subproblem is needed, it is already available in the table. Consequently, in our problem the only tricky part is arranging the order in which to compute the values (so that it is readily available when we need it). In the process of computing m[i, j] we will need to access values m[i, k] and m[k + 1, j] for each value of k lying between i and j. This suggests that we should organize our computation according to the number of matrices in the subchain. So, lets work on the subchain: Let L = j i + 1 denote the length of the subchain being multiplied. The subchains of length 1 (m[i, i]) are trivial. Then we build up by computing the subchains of length 2, 3,..., n. The final answer is m[1, n]. Now set up the loop: Observe that if a subchain of length L starts at position i, then j = i + L 1. Since, we would like to keep j in bounds, this means we want j n, this, in turn, means that we want i + L 1 n, actually what we are saying here is that we want i n L +1. This gives us the closed interval for i. So our loop for i runs from 1 to n L + 1. Matrix-Chain(array p[1.. n], int n) { Array s[1.. n 1, 2.. n]; FOR i = 1 TO n DO m[i, i] = 0; FOR L = 2 TO n DO { FOR i = 1 TO n L + 1 do { j = i + L 1; m[i, j] = infinity; FOR k = i TO j 1 DO { q = m[i, k] + m[k + 1, j] + p[i 1] p[k] p[j]; IF (q < m[i, j]) { m[i, j] = q; s[i, j] = k; // initialize // L=length of subchain // check all splits Analysis and Design of Algorithm Page 12

E.G. return m[1, n](final cost) and s (splitting markers); The m-table computed by MatrixChain procedure for n = 6 matrices A 1, A 2, A 3, A 4, A 5, A 6 and their dimensions 30, 35, 15, 5, 10, 20, 25.

13 E.G. return m[1, n](final cost) and s (splitting markers); The m-table computed by MatrixChain procedure for n = 6 matrices A 1, A 2, A 3, A 4, A 5, A 6 and their dimensions 30, 35, 15, 5, 10, 20, 25. the m-table is rotated so that the main diagonal runs horizontally. Only the main diagonal and upper triangle is used. Complexity Analysis Clearly, the space complexity of this procedure Ο(n 2 ). Since the tables m and s require Ο(n 2 ) space. As far as the time complexity is concern, a simple inspection of the for-loop(s) structures gives us a running time of the procedure. Since, the three for-loops are nested three deep, and each one of them iterates at most n times (that is to say indices L, i, and j takes on at most n 1 values). Therefore, The running time of this procedure is Ο(n 3 ). Extracting Optimum Sequence This is Step 4 of the dynamic programming paradigm in which we construct an optimal solution from computed information. The arrays[i, j] can be used to extract the actual sequence. The basic idea is to keep a split marker in s[i, j] that indicates what is the best split, that is, what value of k leads to the minimum value of m[i, j]. s[i, j] = k tells us that the best way to multiply the subchain is to first multiply the subchain A i..k and then multiply the subchain A k+1..j, and finally multiply these two subchains together. Intuitively, s[i, j] tells us what multiplication to perform last. Note that we only need to store s[i, j] when we have at least two matrices, that is, if j > i. The actual multiplication algorithm uses the s[i, j] value to determine how to split the current sequence. Assume that the matrices are stored in an array of matrices A[1..n], and that s[i, j] is global to this recursive procedure. The procedure returns a matrix. Mult(i, j) { if (i = = j) return A[i]; else { k = s[i, j]; // Basis Analysis and Design of Algorithm Page 13

X = Mult(i, k]; Y = Mult(k + 1, j]; return XY; // X=A[i] A[k] // Y=A[k+1] A[j] // multiply matrices X and Y Again, we rotate the s-table so that the main diagonal runs horizontally but in this table

14 X = Mult(i, k]; Y = Mult(k + 1, j]; return XY; // X=A[i] A[k] // Y=A[k+1] A[j] // multiply matrices X and Y Again, we rotate the s-table so that the main diagonal runs horizontally but in this table we use only upper triangle (and not the main diagonal). In the example, the procedure computes the chain matrix product according to the parenthesization ((A 1 (A 2 A 3 ))((A 4 A 5 ) A 6 ). Shortest Path: Given a graph and a source vertex src in graph, find shortest paths from src to all vertices in the given graph. The graph may contain negative weight edges. Bellman-Ford is also simpler than Dijkstra and suites well for distributed systems. But time complexity of Bellman-Ford is O(VE), which is more than Dijkstra. Algorithm Following are the detailed steps. Input: Graph and a source vertex src Output: Shortest distance to all vertices from src. If there is a negative weight cycle, then shortest distances are not calculated, negative weight cycle is reported. 1) This step initializes distances from source to all vertices as infinite and distance to source itself as 0. Create an array dist[] of size V with all values as infinite except dist[src] where src is source vertex. 2) This step calculates shortest distances. Do following V -1 times where V is the number of vertices in given graph...a) Do following for each edge u-v If dist[v] > dist[u] + weight of edge uv, then update dist[v].dist[v] = dist[u] + weight of edge uv 3) This step reports if there is a negative weight cycle in graph. Do following for each edge u- v If dist[v] > dist[u] + weight of edge uv, then Graph contains negative weight cycle Analysis and Design of Algorithm Page 14

Like other Dynamic Programming Problems, the algorithm calculate shortest paths in bottom-up manner.

15 The idea of step 3 is, step 2 guarantees shortest distances if graph doesn t contain negative weight cycle. If we iterate through all edges one more time and get a shorter path for any vertex, then there is a negative weight cycle How does this work? Like other Dynamic Programming Problems, the algorithm calculate shortest paths in bottom-up manner. It first calculates the shortest distances for the shortest paths which have at-most one edge in the path. Then, it calculates shortest paths with at-nost 2 edges, and so on. After the ith iteration of outer loop, the shortest paths with at most i edges are calculated. There can be maximum V 1 edges in any simple path, that is why the outer loop runs v 1 times. Example Let the given source vertex be 0. Initialize all distances as infinite, except the distance to source itself. Total number of vertices in the graph is 5, so all edges must be processed 4 times. Let all edges are processed in following order: (B,E), (D,B), (B,D), (A,B), (A,C), (D,C), (B,C), (E,D). We get following distances when all edges are processed first time. The first row in shows initial distances. The second row shows distances when edges (B,E), (D,B), (B,D) and (A,B) are processed. The third row shows distances when (A,C) is processed. The fourth row shows when (D,C), (B,C) and (E,D) are processed. The first iteration guarantees to give all shortest paths which are at most 1 edge long. We get following distances when all edges are processed second time (The last row shows final values). Analysis and Design of Algorithm Page 15

16 The second iteration guarantees to give all shortest paths which are at most 2 edges long. The algorithm processes all edges 2 more times. The distances are minimized after the second iteration, so third and fourth iterations don t update the distances. Analysis and Design of Algorithm Page 16

Dynamic Programming. December 15, CMPE 250 Dynamic Programming December 15, / 60

Dynamic Programming. December 15, CMPE 250 Dynamic Programming December 15, / 60 Dynamic Programming December 15, 2016 CMPE 250 Dynamic Programming December 15, 2016 1 / 60 Why Dynamic Programming Often recursive algorithms solve fairly difficult problems efficiently BUT in other cases