Practice Final Exam 2: Solutions

Transcription

1 lgorithm Design Techniques Practice Final Exam 2: Solutions 1. The Simplex lgorithm. (a) Take the LP max x 1 + 2x 2 s.t. 2x 1 + x 2 3 x 1 x 2 2 x 1, x 2 0 and write it in dictionary form. Pivot: add x 1 to basis, remove x 3. z = x 1 + 2x 2 x 3 = 3 2x 1 x 2 x 4 = 2 x 1 + x 2 z = x 2 ) + 2x 2 2x 1 = 3 x 3 x 2 x 4 = 2 1(3 x 2 3 x 2 ) + x 2 Pivot: add x 2 to basis, remove x 1. z = 3 2 1x x 2 2 x 1 = 3 2 1x 2 3 1x 2 2 x 4 = 1 2 1x x 2 2 z = 3 2 1x (3 x 2 3 2x 1 ) x 2 = 3 x 3 2x 1 x 4 = 1 2 1x (3 x 2 3 2x 1 ) z = 6 2x 3 3x 1 x 2 = 3 x 3 2x 1 x 4 = 5 2x 3 3x 1 1

2 So the optimal solution is (x 1, x 2 ) = (0, 3) with objective value z = x 1 + 2x 2 = 6. (b) The dual is min 3y 1 + 2y 2 s.t. 2y 1 + y 2 1 y 1 y 2 2 y 1, y 2 0 The top row of the final dictionary was z = 6 3x 1 2x 3 We know that the dual variables y 1, y 2 should be set equal to the coefficients of the slack variables x 3, x 4. Thus (y 1, y 2 ) = (2, 0). Clearly this is dual feasible [You should verify this]. It has a dual value of 3y 1 + 2y 2 = 6. So, by weak duality, our solution of value 6 in a) to the primal shows that this must be dual optimal. 2

3 2. Local Search. (a) Given an undirected graph G = (V, E), the maximum cut problem is find a set S V such that δ(s) is maximised. (b) Consider any vertex v, and suppose it has degree deg(v). The final cut δ(s t ) contains at least 1 deg(v) edges incident to vertex v otherwise the 2 local search algorithm would not have terminated (since we could improve the cut by moving v). Summing over all vertices we see that δ(s t ) contains at least half the edges in the graph. Obviously, the optimal cut δ(s ) contains at most the total number of edges in the graph. The result follows. 3. Parameterised Complexity. (a) problem is fixed parameter tractable if it has an algorithm to solve it that runs in time f(k) p(n), where n is the problem input size and k is the size of the optimal solution. Here p() is a polynomial function but f() need not be. (b) Randomly colour the vertices with colours {1, 2,..., k}. We now search for a cycle whose vertices are coloured in that same order. Let V 1 be the vertices coloured 1, let V 2 be be the vertices coloured 2 and which have an edge to some vertex in V 1, let V 3 be the vertices coloured 3 and which have an edge to some vertex in V 2, etc. Thus V k is the set of vertices at the end of a path coloured 1, 2,..., k. For each vertex v in V k, do a reverse search to find all the vertices in V 1 that begin multi-coloured paths that end at v. For each such vertex u check if (u, v) is an edge. If so we have found a multi-coloured cycle. This process can be done in polynomial time in the graph size. The probability that a k-cycle is multi-coloured is at least ( 1 k )k so repeating this colouring experiment independently enough times (say k k log n times) will let us find a k-cycle with high probability in time O(f(k) p(n)) as desired. 3

4 4. Branch and Bound. The branch and bound tree is shown below. Dashed circles corresponded to feasible solutions (that is, perfect matchings); dotted circles correspond to suboptimal subtrees that can be pruned away as we have already found better integral solutions. Note that by the branching rule we first explore the subpath B then B to find the feasible solutions BCD and BDC. The latter has value 22, which allows us to prune the whole of the subtree rooted at as well as the remainder of the nodes in the subtree rooted at B. Next we branch down C and C but leads to no better solutions. Finally we search the subpath D and D leading to the solutions DBC and DCB both of value 21. Everything remaining in the subtree of D can now be pruned. So we have found an optimal solution. JOB 1 CBB : 22 B BCC : 19 C CBB : 19 D DBB : 20 JOB 2 BCC : 19 BDCC : 23 C BCD : 26 D CBB : 19 CDBB : 23 B D CBD : 29 DBB : 20 DBCC : 25 B C DCBB : 22 JOB 3 C D BCD : 23 BDC : 22 B D CBD : 24 CDB : 23 B C DBC : 21 DCB : 21 4

5 5. NP-Completeness. (a) Given n (positive) integers x 1, x 2,..., x n. The partition problem asks if there is a subset S [n] such that x i = x i i S i/ S (b) i. Bin Packing is in NP (we can easily check a proposed solution to confirm a YES instance). The reduction from Partition is as follows. Set k = 2 and C = 1 2 i s i, where s i = x i. Clearly, there is a bin packing that uses two bins if and only if there is a partition S [n] such that x i = x i i S i/ S ii. Bin Covering is in NP (we can easily check a proposed solution to confirm a YES instance). The reduction from Partition is as follows. Set k = 2 and R = 1 2 i s i, where s i = x i. Clearly, there is a bin covering that uses two bins if and only if there is a partition S [n] such that x i = x i i S i/ S 6. pproximation lgorithms. (a) n α-approximation algorithm for a maximisation problem P always returns in polynomial time a feasible solution S to any instance I P such that the value of the optimal solution for that instance is at most an α factor greater that value of S. (b) We use a greedy algorithm. First observe that any item i with s i R will be placed in its own bin in the optimal solution. So our greedy algorithm will do that as well. So we may assume that s i < R for all i. The greedy algorithm simply places items in Bin 1 until it is overfull. Then it places items in Bin 2 until it is overfull, etc. The greedy algorithm clearly runs in polynomial time and returns a feasible solution. Let s show it gives a factor 3-approximation guarantee. ssume that the optimal solution fills k bins, and the greedy algorithm fills l bins. So 5

6 i s i k R. Now since s i < R for all i, the greedy algorithm overfills a bin by at most max i s i < R. Thus each bin has items of total size at most R + R. The greedy algorithm may use one extra bin that is not full, that is, one bin that has items of total size at most R. s every item is used we have s i < l 2R + R But by the optimal solution we know s i k R i i So Thus k R < l 2R + R l 1 2 (k 1) 1 3 k provided k 3. If k < 3 then we trivially get a 2-approximation algorithm by placing all the items in one bin. So we have a 3-approximation algorithm. [Remark. We can actually refine the analysis to show that this is a 2- approximation algorithm. To do this, let item x i be the item that first overfills bin i. Items x 1,..., x l are clearly in at most l bins of the optimal solution. The remaining items have total size less than (l + 1)R because they don t fill bins 1 to l + 1 (recall one bin in greedy could be left unfull). These items thus may cover at most l bins of opt. So opt covers at most l + l = 2l bins.] 6