6 Randomized rounding of semidefinite programs

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1 6 Randomized rounding of semidefinite programs We now turn to a new tool which gives substantially improved performance guarantees for some problems We now show how nonlinear programming relaxations can give us better algorithms than we know how to obtain via LP; in particular we use a type of nonlinear program called a semidefinite program (SDP) Part of the power of SDP is that semidefinite programs can be solved in polynomial time MAT ApprAl, Spring May A brief introduction to semidefinite programming In what follows, is the transpose of the matrix, and vectors are assumed to be column vectors, so that is the inner product of with itself, while is an by matrix Definition 6.1: A matrix is positive semidefinite iff for all, 0. MAT ApprAl, Spring May

2 We abbreviate positive semidefinite as psd 0denotes that a matrix is psd Assume that any psd matrix is also symmetric Fact 6.2: If is a symmetric matrix, then the following statements are equivalent: 1. is psd 2. has non-negative eigenvalues; 3. for some where ; 4. = for some 0and vectors such that =1and =0for = MAT ApprAl, Spring May A SDP is similar to a LP in that there is a linear objective function and linear constraints In addition, a square symmetric matrix of variables can be constrained to be psd In the example below the variables are for max or min subject to, =( )0,, MAT ApprAl, Spring May

3 Given some technical conditions, semidefinite programs can be solved to within an additive error of in time that is polynomial in the size of the input and log(1/) We will usually ignore the additive error when discussing SDPs We assume that the SDPs can be solved exactly, since the algorithms we will use do not assume exact solutions, and one can usually analyze the algorithm that has additive error in the same way with only a small loss in performance guarantee MAT ApprAl, Spring May We often use SDP in the form of vector programming (VP) The variables of VPs are vectors, where the dimension of the space is the number of vectors in the VP The VP has an objective function and constraints that are linear in the inner product of these vectors We write the inner product of and as, or sometimes as Below we give an example of a VP MAT ApprAl, Spring May

4 max or min subject to,, = 1,, We claim that in fact the previous SDP and this VP are equivalent This follows from Fact 6.2 In particular, it follows since a symmetric is psd if and only if = for some matrix MAT ApprAl, Spring May Given a solution to the SDP, we can take the solution, compute in polynomial time a matrix for which (to within small error) We set to be the th column of Then, and the are a feasible solution of the same value to the VP Similarly, given a solution to the vector program, we construct a matrix whose th column is, and let Then is symmetric and psd, with, so that is a feasible solution of the same value for the SDP MAT ApprAl, Spring May

5 6.2 Finding large cuts For this problem, the input is an undirected graph = ( ), and nonnegative weights 0for each edge The goal is to partition the vertex set into two parts, and, so as to maximize the weight of the edges whose two endpoints are in different parts, one in and one in Fair coin flipping gives a -approximation algorithm for the maximum cut problem MAT ApprAl, Spring May We now use SDP to give a.878-approximation algorithm for the problem in general graphs We start by considering the following formulation of the maximum cut problem: maximize 1 2 (1 ) subject to 1,+1, = 1,, We claim that if we can solve this formulation, then we can solve the MAX CUT problem MAT ApprAl, Spring May

6 Lemma 6.3: The previous program models the maximum cut problem. Proof: Consider the cut = { 1} and = { = +1}. Note that if an edge ) is in this cut, then 1, while if the edge is not in the cut, =1. Thus 1 2 (1 ) gives the weight of all the edges in the cut. Hence finding the setting of the to ±1 that maximizes this sum gives the maximum-weight cut. MAT ApprAl, Spring May Consider now the following vector programming relaxation of the program: maximize 1 2 (1 ) subject to = 1, = 1,,,, = 1,, This is a relaxation: take any feasible solution and produce a feasible solution to this program of the same value by setting = (, 0,0,, 0): clearly =1& for this solution Thus if is the value of an optimal solution to the vector program, it must be that OPT MAT ApprAl, Spring May

7 We can solve the relaxation in polynomial time We would now like to round the solution to obtain a near-optimal cut To do this, we introduce a form of randomized rounding suitable for vector programming We pick a random vector = (,, )by drawing each component from (0,1), the normal distribution with mean 0 and variance 1 (0,1) can be simulated by drawing repeatedly from the uniform distribution on [0,1] Given a solution, iterate through all the vertices and put if 0 and otherwise MAT ApprAl, Spring May Another way of looking at this algorithm is that we consider the hyperplane with normal containing the origin All vectors lie on the unit sphere, since =1and they are unit vectors The hyperplane with normal containing the origin splits the sphere in half; all vertices in one half (the half such that 0) are put into, and all vertices in the other half are put into As we see below, the vector / is uniform over the unit sphere, so this is equivalent to randomly splitting the unit sphere in half MAT ApprAl, Spring May

8 For this reason, this technique is sometimes called choosing a random hyperplane MAT ApprAl, Spring May Fact 6.4: The normalization of, /, is uniformly distributed over the -dimensional unit sphere. Fact 6.5: The projections of onto two unit vectors and are independent and are normally distributed with mean 0 and variance 1 iff and are orthogonal. Corollary 6.6: Let be the projection of onto a two-dimensional plane. Then the normalization of, /, is uniformly distributed on a unit circle in the plane. MAT ApprAl, Spring May

9 Let us begin the proof that choosing a random hyperplane gives a.878-approximation algorithm for the problem We will need the following two lemmas Lemma 6.7: The probability that edge ) is in the cut is arccos. Proof: Let be the projection of onto the plane defined by and. If, then is orthogonal to both and, and + =. Similarly =. MAT ApprAl, Spring May Consider the situation, where line is perpendicular to the vector and line is perpendicular to. By Corollary 6.6, the vector with its tail at the origin is oriented with respect to the vector by an angle chosen uniformly from [0,2). If is to the right of the line, will have a nonnegative inner product with, otherwise not. If is above the line, will have nonnegative inner product with, otherwise not. Thus we have and if is in the sector and and if is in the sector. MAT ApprAl, Spring May

10 If the angle formed by and is radians, then the angles and are also radians. Hence the fraction of values for which, the angle of, corresponds to the event in which ) is in the cut is /2. Thus the probability that ) is in the cut is. We know that = cos. Since and are both unit length vectors, we have that = arccos, which completes the proof of the lemma. MAT ApprAl, Spring May Lemma 6.8: For 1,1], 1 arccos Proof: The proof follows from simple calculus. MAT ApprAl, Spring May

11 Theorem 6.9: Rounding the vector program by choosing a random hyperplane is a.878- approximation algorithm for the max cut problem. Proof: Let be a RV for edge ) s.t. =1if ) is in the cut given by the algorithm, and 0 otherwise. Let be a RV which gives the weight of the cut; that is, =. Then by Lemma 6.7, ]= Pr edge isincut = 1 arccos. MAT ApprAl, Spring May By Lemma 6.8, we can bound each term arccos below by 0.878, so that = OPT. We know that OPT The proof of the theorem above shows that there is a cut of value at least 0.878, so that OPT MAT ApprAl, Spring May

12 Thus we have that OPT OPT It has been shown that there are graphs for which the upper inequality is met with equality This implies that we can get no better performance guarantee for the maximum cut problem by using as an upper bound on OPT Currently,.878 is the best performance guarantee known for MAX CUT Theorem 6.10: If there is an -approximation algorithm for the maximum cut problem with > 0.941, then P = NP. 7-May-15 MAT ApprAl, Spring Theorem 6.11: Given the unique games conjecture there is no -approximation algorithm for the maximum cut problem with constant 1 > unless P = NP. min arccos It is possible to derandomize by a sophisticated application of conditional expectations The derandomization incurs a loss in the performance guarantee that can be made as small as desired (by increasing the running time) MAT ApprAl, Spring May

13 6.3 Approximating quadratic programs We can extend the algorithm above for the MAX CUT to the following more general problem We wish to approximate the quadratic program: maximize subject to 1,+1, = 1,, We need to be careful in this case since it is possible that the value of an optimal solution is negative (e.g., if the values of are negative and all other are zero) MAT ApprAl, Spring May Thus far we have only been considering problems in which all feasible solutions have nonnegative value so that the definition of an approximation algorithm makes sense To see that the definition might not make sense in the case of negative solution values, suppose we have an -approximation algorithm for a maximization problem with <1and we have a problem instance in which OPT is negative Then the approximation algorithm guarantees the value of our solution is at least OPT, which means that the value of our solution will be greater than that of OPT MAT ApprAl, Spring May

14 In order to get around this difficulty, we will restrict the objective function matrix = ( ) to itself be positive semidefinite Observe then that for any feasible solution, the value of the objective function will be and will be nonnegative by the definition of psd matrices As in the case of the maximum cut problem, we can then have the following VP relaxation maximize subject to = 1, = 1,,, = 1,, MAT ApprAl, Spring May Let be the value of an optimal solution for this vector program By the same argument as in the previous section, OPT We can also use the same algorithm as we did for the maximum cut problem We solve the VP in polynomial time and obtain vectors We choose a random hyperplane with normal, and generate a solution for the QP by setting = +1 if 0and 1 otherwise This gives a 2 -approx. algorithm for the QP MAT ApprAl, Spring May

15 Lemma 6.12: = arcsin Proof: Recall from Lemma 6.7 that the probability and will be on different sides of the random hyperplane is arccos. Thus the probability that and have different values is arccos. Observe that if and have different values, then their product must be 1, while if they have the same value, their product must be 1. Thus the probability that the product is 1 must be arccos. MAT ApprAl, Spring May = Pr = 1 Pr 1 = 1 arccos 1 arccos =1 2 arccos. Using arcsin) + arccos) =, we get =1 2 2 arcsin 2 arcsin. MAT ApprAl, Spring May

16 In order to analyze this algorithm, we have to use a global analysis, rather than a term-by-term analysis We then can show the following theorem Theorem 6.16: Rounding the vector program by choosing a random hyperplane is a - approximation algorithm for the quadratic program when the objective function matrix is positive semidefinite. MAT ApprAl, Spring May Finding a correlation clustering In this problem we are given an undirected graph in which each edge is given two nonnegative weights, 0and 0 The goal is to cluster the vertices into sets of similar vertices; the degree to which and are similar is given by and the degree to which they are different is given by We represent a clustering of the vertices by a partition of the vertex set into non-empty subsets MAT ApprAl, Spring May

17 Let ) be the set of edges that have endpoints in different sets of the partition, and let ) be the set of edges that have both endpoints in the same part of the partition Then the goal of the problem is to find a partition that maximizes the total weight of edges inside the sets of the partition plus the total weight of edges between sets of the partition; in other words, we find to maximize ) + ) MAT ApprAl, Spring May It is easy to get a (1/2)-approximation algorithm for this problem If we put all the vertices into a single cluster (that is, = {}), then the value of this solution is If we make each vertex its own cluster (that is, = {{}: }), then the value of this solution is Since OPT, at least one of these two solutions has a value of at least OPT MAT ApprAl, Spring May

18 We can obtain a -approximation algorithm by using semidefinite programming Let be the th unit vector; that is, it has a one in the th coordinate and zeros elsewhere We will have a vector for each vertex ; we set if is in the th cluster The model of the problem then becomes maximize subject to,,,, since = 1 if = and 0 otherwise MAT ApprAl, Spring May We can now relax this model to the following VP: maximize subject to = 1, 0,, Let be the value of an optimal solution for the vector program Observe that the VP is a relaxation, since any feasible solution to the model above is feasible for the VP and has the same value Thus OPT MAT ApprAl, Spring May

19 Previously we chose a random hyperplane to partition the vectors into two sets; in MAX CUT, this gave the two sides of the cut In QP this gave the variables of value +1 and 1 Now we will choose two random hyperplanes, with two independent random vectors and as their normals This partitions the vertices into 2 =4sets: in particular, we partition the vertices into the sets = { 0, 0} = { 0, < 0} = { <0, 0} = { < 0, < 0} MAT ApprAl, Spring May The solution consisting of these four clusters, with =, comes within a factor of 34of the optimal value Lemma 6.17: For [0,1], 1 arccos.75and 1 arccos (1 ).75. MAT ApprAl, Spring May

20 Theorem 6.18: Rounding the VP by using two random hyperplanes as above gives a (34)- approximation algorithm for the correlation clustering problem. Proof: Let be a RV which is 1 if vertices and end up in the same cluster, and is 0 otherwise. Note that the probability that a single random hyperplane has the vectors and on different sides of the hyperplane is arccos by Lemma 6.7. Then the probability that and are on the same side of a single random hyperplane is 1 arccos. MAT ApprAl, Spring May Furthermore, the probability that the vectors are both on the same sides of the two random hyperplanes defined by and is arccos, since and are chosen independently. Thus = arccos. Let be a random variable denoting the weight of the partition. Observe that =. MAT ApprAl, Spring May

21 ]= ]+ ] 1 arccos 1 arccos We now want to use Lemma 6.17 to bound each term in the sum from below; we can do so because the constraints of the VP imply that 0,1. Thus.75 = OPT.. MAT ApprAl, Spring May

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