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91
Quadratic forms on graphs
 Invent. Math
, 2005
"... We introduce a new graph parameter, called the Grothendieck constant of a graph G = (V, E), which is defined as the least constant K such that for every A: E → R, ..."
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Cited by 51 (10 self)
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We introduce a new graph parameter, called the Grothendieck constant of a graph G = (V, E), which is defined as the least constant K such that for every A: E → R,
Fast Algorithms for Approximate Semidefinite Programming using the Multiplicative Weights Update Method
"... Semidefinite programming (SDP) relaxations appear inmany recent approximation algorithms but the only general technique for solving such SDP relaxations is via interior point methods. We use a Lagrangianrelaxation based technique (modified from the papers of Plotkin, Shmoys,and Tardos (PST), and ..."
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Cited by 45 (6 self)
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Semidefinite programming (SDP) relaxations appear inmany recent approximation algorithms but the only general technique for solving such SDP relaxations is via interior point methods. We use a Lagrangianrelaxation based technique (modified from the papers of Plotkin, Shmoys,and Tardos (PST), and Klein and Lu) to derive faster algorithms for approximately solving several families of SDPrelaxations. The algorithms are based upon some improvements to the PST ideas which lead to new results even fortheir framework as well as improvements in approximate eigenvalue computations by using random sampling.
Correlation Clustering in General Weighted Graphs
 Theoretical Computer Science
, 2006
"... We consider the following general correlationclustering problem [1]: given a graph with real nonnegative edge weights and a 〈+〉/〈− 〉 edge labeling, partition the vertices into clusters to minimize the total weight of cut 〈+ 〉 edges and uncut 〈− 〉 edges. Thus, 〈+ 〉 edges with large weights (represen ..."
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Cited by 44 (0 self)
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We consider the following general correlationclustering problem [1]: given a graph with real nonnegative edge weights and a 〈+〉/〈− 〉 edge labeling, partition the vertices into clusters to minimize the total weight of cut 〈+ 〉 edges and uncut 〈− 〉 edges. Thus, 〈+ 〉 edges with large weights (representing strong correlations between endpoints) encourage those endpoints to belong to a common cluster while 〈− 〉 edges with large weights encourage the endpoints to belong to different clusters. In contrast to most clustering problems, correlation clustering specifies neither the desired number of clusters nor a distance threshold for clustering; both of these parameters are effectively chosen to be the best possible by the problem definition. Correlation clustering was introduced by Bansal, Blum, and Chawla [1], motivated by both document clustering and agnostic learning. They proved NPhardness and gave constantfactor approximation algorithms for the special case in which the graph is complete (full information) and every edge has the same weight. We give an O(log n)approximation algorithm for the general case based on a linearprogramming rounding and the “regiongrowing ” technique. We also prove that this linear program has a gap of Ω(log n), and therefore our approximation is tight under this approach. We also give an O(r 3)approximation algorithm for Kr,rminorfree graphs. On the other hand, we show that the problem is equivalent to minimum multicut, and therefore APXhard and difficult to approximate better than Θ(logn). 1
Convergence and Approximation in Potential Games
, 2006
"... We study the speed of convergence to approximately optimal states in two classes of potential games. We provide bounds in terms of the number of rounds, where a round consists of a sequence of movements, with each player appearing at least once in each round. We model the sequential interaction betw ..."
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Cited by 38 (3 self)
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We study the speed of convergence to approximately optimal states in two classes of potential games. We provide bounds in terms of the number of rounds, where a round consists of a sequence of movements, with each player appearing at least once in each round. We model the sequential interaction between players by a bestresponse walk in the state graph, where every transition in the walk corresponds to a best response of a player. Our goal is to bound the social value of the states at the end of such walks. In this paper, we focus on two classes of potential games: selfish routing games, and cut games (or party affiliation games [7]).
ModularityMaximizing Graph Communities via Mathematical Programming
"... In many networks, it is of great interest to identify communities, unusually densely knit groups of individuals. Such communities often shed light on the function of the networks or underlying properties of the individuals. Recently, Newman suggested modularity as a natural measure of the quality ..."
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Cited by 37 (1 self)
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In many networks, it is of great interest to identify communities, unusually densely knit groups of individuals. Such communities often shed light on the function of the networks or underlying properties of the individuals. Recently, Newman suggested modularity as a natural measure of the quality of a network partitioning into communities. Since then, various algorithms have been proposed for (approximately) maximizing the modularity of the partitioning determined. In this paper, we introduce the technique of rounding mathematical programs to the problem of modularity maximization, presenting two novel algorithms. More specifically, the algorithms round solutions to linear and vector programs. Importantly, the linear programing algorithm comes with an a posteriori approximation guarantee: by comparing the solution quality to the fractional solution of the linear program, a bound on the available “room for improvement ” can be obtained. The vector programming algorithm provides a similar bound for the best partition into two communities. We evaluate both algorithms using experiments on several standard test cases for network partitioning algorithms, and find that they perform comparably or better than past algorithms, while being more efficient than exhaustive techniques.
Correlation clustering with a fixed number of clusters
 Theory of Computing
"... Abstract: We continue the investigation of problems concerning correlation clustering or clustering with qualitative information, which is a clustering formulation that has been studied recently (Bansal, Blum, Chawla (2004), Charikar, Guruswami, Wirth (FOCS’03), Charikar, Wirth (FOCS’04), Alon et al ..."
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Abstract: We continue the investigation of problems concerning correlation clustering or clustering with qualitative information, which is a clustering formulation that has been studied recently (Bansal, Blum, Chawla (2004), Charikar, Guruswami, Wirth (FOCS’03), Charikar, Wirth (FOCS’04), Alon et al. (STOC’05)). In this problem, we are given a complete graph on n nodes (which correspond to nodes to be clustered) whose edges are labeled + (for similar pairs of items) and − (for dissimilar pairs of items). Thus our input consists of only qualitative information on similarity and no quantitative distance measure between items. The quality of a clustering is measured in terms of its number of agreements, which is simply the number of edges it correctly classifies, that is the sum of number of − edges whose endpoints it places in different clusters plus the number of + edges both of whose endpoints it places within the same cluster. In this paper, we study the problem of finding clusterings that maximize the number of agreements, and the complementary minimization version where we seek clusterings that minimize the number of disagreements. We focus on the situation when the number of clusters is stipulated to be a small constant k. Our main result is that for every k, there is a polynomial time approximation scheme for both maximizing agreements and minimizing disagreements.
Towards Sharp Inapproximability For Any 2CSP
"... We continue the recent line of work on the connection between semidefinite programmingbased approximation algorithms and the Unique Games Conjecture. Given any boolean 2CSP (or more generally, any nonnegative objective function on two boolean variables), we show how to reduce the search for a good ..."
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Cited by 32 (1 self)
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We continue the recent line of work on the connection between semidefinite programmingbased approximation algorithms and the Unique Games Conjecture. Given any boolean 2CSP (or more generally, any nonnegative objective function on two boolean variables), we show how to reduce the search for a good inapproximability result to a certain numeric minimization problem. The key objects in our analysis are the vector triples arising when doing clausebyclause analysis of algorithms based on semidefinite programming. Given a weighted set of such triples of a certain restricted type, which are “hard ” to round in a certain sense, we obtain a Unique Gamesbased inapproximability matching this “hardness ” of rounding the set of vector triples. Conversely, any instance together with an SDP solution can be viewed as a set of vector triples, and we show that we can always find an assignment to the instance which is at least as good as the “hardness ” of rounding the corresponding set of vector triples. We conjecture that the restricted type required for the hardness result is in fact no restriction, which would imply that these upper and lower bounds match exactly. This conjecture is supported by all existing results for specific 2CSPs. As an application, we show that MAX 2AND is hard to approximate within 0.87435. This improves upon the best previous hardness of αGW + ɛ ≈ 0.87856, and comes very close to matching the approximation ratio of the best algorithm known, 0.87401. It also establishes that balanced instances of MAX 2AND, i.e., instances in which each variable occurs positively and negatively equally often, are not the hardest to approximate, as these can be approximated within a factor αGW.
Complex Quadratic Optimization and Semidefinite Programming
 SIAM Journal on Optimization
, 2006
"... In this paper we study the approximation algorithms for a class of discrete quadratic optimization problems in the Hermitian complex form. A special case of the problem that we study corresponds to the max3cut model used in a recent paper of Goemans and Williamson. We first develop a closedform f ..."
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Cited by 31 (12 self)
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In this paper we study the approximation algorithms for a class of discrete quadratic optimization problems in the Hermitian complex form. A special case of the problem that we study corresponds to the max3cut model used in a recent paper of Goemans and Williamson. We first develop a closedform formula to compute the probability of a complexvalued normally distributed bivariate random vector to be in a given angular region. This formula allows us to compute the expected value of a randomized (with a specific rounding rule) solution based on the optimal solution of the complex SDP relaxation problem. In particular, we study the limit of that model, in which the problem remains NPhard. We show that if the objective is to maximize a positive semidefinite Hermitian form, then the randomizationrounding procedure guarantees a worstcase performance ratio of π/4 ≈ 0.7854, which is better than the ratio of 2/π ≈ 0.6366 for its counterpart in the real case due to Nesterov. Furthermore, if the objective matrix is realvalued positive semidefinite with nonpositive offdiagonal elements, then the performance ratio improves to 0.9349.
On nonapproximability for quadratic programs
 IN 46TH ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE
, 2005
"... This paper studies the computational complexity of the following type of quadratic programs: given an arbitrary matrix whose diagonal elements are zero, find x ∈ {−1, 1} n that maximizes x T Mx. This problem recently attracted attention due to its application in various clustering settings, as well ..."
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Cited by 30 (4 self)
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This paper studies the computational complexity of the following type of quadratic programs: given an arbitrary matrix whose diagonal elements are zero, find x ∈ {−1, 1} n that maximizes x T Mx. This problem recently attracted attention due to its application in various clustering settings, as well as an intriguing connection to the famous Grothendieck inequality. It is approximable to within a factor of O(log n), and known to be NPhard to approximate within any factor better than 13/11 − ɛ for all ɛ> 0. We show that it is quasiNPhard to approximate to a factor better than O(log γ n) for some γ> 0. The integrality gap of the natural semidefinite relaxation for this problem is known as the Grothendieck constant of the complete graph, and known to be Θ(log n). The proof of this fact was nonconstructive, and did not yield an explicit problem instance where this integrality gap is achieved. Our techniques yield an explicit instance for which the integrality gap is log n Ω ( log log n), essentially answering one of the open problems of Alon et al. [AMMN].
Efficient Algorithms Using The Multiplicative Weights Update Method
, 2006
"... Abstract Algorithms based on convex optimization, especially linear and semidefinite programming, are ubiquitous in Computer Science. While there are polynomial time algorithms known to solve such problems, quite often the running time of these algorithms is very high. Designing simpler and more eff ..."
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Abstract Algorithms based on convex optimization, especially linear and semidefinite programming, are ubiquitous in Computer Science. While there are polynomial time algorithms known to solve such problems, quite often the running time of these algorithms is very high. Designing simpler and more efficient algorithms is important for practical impact. In this thesis, we explore applications of the Multiplicative Weights method in the design of efficient algorithms for various optimization problems. This method, which was repeatedly discovered in quite diverse fields, is an algorithmic technique which maintains a distribution on a certain set of interest, and updates it iteratively by multiplying the probability mass of elements by suitably chosen factors based on feedback obtained by running another algorithm on the distribution. We present a single metaalgorithm which unifies all known applications of this method in a common framework. Next, we generalize the method to the setting of symmetric matrices rather than real numbers. We derive the following applications of the resulting Matrix Multiplicative Weights algorithm: 1. The first truly general, combinatorial, primaldual method for designing efficient algorithms for semidefinite programming. Using these techniques, we obtain significantly faster algorithms for obtaining O(plog n) approximations to various graph partitioning problems, such as Sparsest Cut, Balanced Separator in both directed and undirected weighted graphs, and constraint satisfaction problems such as Min UnCut and Min 2CNF Deletion. 2. An ~O(n3) time derandomization of the AlonRoichman construction of expanders using Cayley graphs. The algorithm yields a set of O(log n) elements which generates an expanding Cayley graph in any group of n elements. 3. An ~O(n3) time deterministic O(log n) approximation algorithm for the quantum hypergraph covering problem. 4. An alternative proof of a result of Aaronson that the flfatshattering dimension of quantum states on n qubits is O ( nfl2).