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127
An InteriorPoint Method for Semidefinite Programming
, 2005
"... We propose a new interior point based method to minimize a linear function of a matrix variable subject to linear equality and inequality constraints over the set of positive semidefinite matrices. We show that the approach is very efficient for graph bisection problems, such as maxcut. Other appli ..."
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Cited by 207 (18 self)
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We propose a new interior point based method to minimize a linear function of a matrix variable subject to linear equality and inequality constraints over the set of positive semidefinite matrices. We show that the approach is very efficient for graph bisection problems, such as maxcut. Other applications include maxmin eigenvalue problems and relaxations for the stable set problem.
A Spectral Bundle Method for Semidefinite Programming
 SIAM JOURNAL ON OPTIMIZATION
, 1997
"... A central drawback of primaldual interior point methods for semidefinite programs is their lack of ability to exploit problem structure in cost and coefficient matrices. This restricts applicability to problems of small dimension. Typically semidefinite relaxations arising in combinatorial applica ..."
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Cited by 139 (6 self)
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A central drawback of primaldual interior point methods for semidefinite programs is their lack of ability to exploit problem structure in cost and coefficient matrices. This restricts applicability to problems of small dimension. Typically semidefinite relaxations arising in combinatorial applications have sparse and well structured cost and coefficient matrices of huge order. We present a method that allows to compute acceptable approximations to the optimal solution of large problems within reasonable time. Semidefinite programming problems with constant trace on the primal feasible set are equivalent to eigenvalue optimization problems. These are convex nonsmooth programming problems and can be solved by bundle methods. We propose replacing the traditional polyhedral cutting plane model constructed from subgradient information by a semidefinite model that is tailored for eigenvalue problems. Convergence follows from the traditional approach but a proof is included for completene...
A Framework for Evolutionary Optimization with Approximate Fitness Functions
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 2002
"... It is a common engineering practice to use approximate models instead of the original computationally expensive model in optimization. When an approximate model is used for evolutionary optimization, the convergence properties of the evolutionary algorithm are unclear due to the approximation error. ..."
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Cited by 74 (13 self)
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It is a common engineering practice to use approximate models instead of the original computationally expensive model in optimization. When an approximate model is used for evolutionary optimization, the convergence properties of the evolutionary algorithm are unclear due to the approximation error. In this paper, extensive empirical studies on convergence of an evolution strategy are carried out on two benchmark problems. It is found that incorrect convergence will occur if the approximate model has false optima. To address this problem, individual and generation based evolution control is introduced and the resulting effects on the convergence properties are presented. A framework for managing approximate models in generationbased evolution control is proposed. This framework is well suited for parallel evolutionary optimization that is able to guarantee the correct convergence of the evolutionary algorithm and to reduce the computation costs as much as possible. Control o...
A robust gradient sampling algorithm for nonsmooth, nonconvex optimization
 SIAM Journal on Optimization
"... Let f be a continuous function on R n, and suppose f is continuously differentiable on an open dense subset. Such functions arise in many applications, and very often minimizers are points at which f is not differentiable. Of particular interest is the case where f is not convex, and perhaps not eve ..."
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Cited by 53 (19 self)
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Let f be a continuous function on R n, and suppose f is continuously differentiable on an open dense subset. Such functions arise in many applications, and very often minimizers are points at which f is not differentiable. Of particular interest is the case where f is not convex, and perhaps not even locally Lipschitz, but whose gradient is easily computed where it is defined. We present a practical, robust algorithm to locally minimize such functions, based on gradient sampling. No subgradient information is required by the algorithm. When f is locally Lipschitz and has bounded level sets, and the sampling radius ǫ is fixed, we show that, with probability one, the algorithm generates a sequence with a cluster point that is Clarke ǫstationary. Furthermore, we show that if f has a unique Clarke stationary point ¯x, then the set of all cluster points generated by the algorithm converges to ¯x as ǫ is reduced to zero.
Practical Aspects of the MoreauYosida Regularization I: Theoretical Properties
, 1994
"... When computing the infimal convolution of a convex function f with the squared norm, one obtains the socalled MoreauYosida regularization of f . Among other things, this function has a Lipschitzian gradient. We investigate some more of its properties, relevant for optimization. Our main result co ..."
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Cited by 49 (2 self)
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When computing the infimal convolution of a convex function f with the squared norm, one obtains the socalled MoreauYosida regularization of f . Among other things, this function has a Lipschitzian gradient. We investigate some more of its properties, relevant for optimization. Our main result concerns secondorder differentiability and is as follows. Under assumptions that are quite reasonable in optimization, the MoreauYosida is twice diffferentiable if and only if f is twice differentiable as well. In the course of our development, we give some results of general interest in convex analysis. In particular, we establish primaldual relationship between the remainder terms in the firstorder development of a convex function and its conjugate.
BundleBased Relaxation Methods For Multicommodity Capacitated Fixed Charge Network Design
, 1999
"... To efficiently derive bounds for largescale instances of the capacitated fixedcharge network design problem, Lagrangian relaxations appear promising. This paper presents the results of comprehensive experiments aimed at calibrating and comparing bundle and subgradient methods applied to the optimi ..."
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Cited by 45 (25 self)
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To efficiently derive bounds for largescale instances of the capacitated fixedcharge network design problem, Lagrangian relaxations appear promising. This paper presents the results of comprehensive experiments aimed at calibrating and comparing bundle and subgradient methods applied to the optimization of Lagrangian duals arising from two Lagrangian relaxations. This study substantiates the fact that bundle methods appear superior to subgradient approaches because they converge faster and are more robust relative to different relaxations, problem characteristics, and selection of the initial parameter values. It also demonstrates that effective lower bounds may be computed efficiently for largescale instances of the capacitated fixedcharge network design problem. Indeed, in a fraction of the time required by a standard simplex approach to solve the linear programming relaxation, the methods we present attain very high quality solutions.
Variable Metric Bundle Methods: from Conceptual to Implementable Forms
, 1996
"... To minimize a convex function, we combine MoreauYosida regularizations, quasiNewton matrices and bundling mechanisms. First we develop conceptual forms using "reversal " quasiNewton formulae and we state their global and local convergence. Then, to produce implementable versions, ..."
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Cited by 41 (8 self)
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To minimize a convex function, we combine MoreauYosida regularizations, quasiNewton matrices and bundling mechanisms. First we develop conceptual forms using &quot;reversal &quot; quasiNewton formulae and we state their global and local convergence. Then, to produce implementable versions, we incorporate a bundle strategy together with a "curvesearch". No convergence results are given for the implementable versions; however some numerical illustrations show their good behaviour even for largescale problems.
Graph Partitioning Algorithms With Applications To Scientific Computing
 Parallel Numerical Algorithms
, 1997
"... Identifying the parallelism in a problem by partitioning its data and tasks among the processors of a parallel computer is a fundamental issue in parallel computing. This problem can be modeled as a graph partitioning problem in which the vertices of a graph are divided into a specified number of su ..."
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Cited by 40 (0 self)
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Identifying the parallelism in a problem by partitioning its data and tasks among the processors of a parallel computer is a fundamental issue in parallel computing. This problem can be modeled as a graph partitioning problem in which the vertices of a graph are divided into a specified number of subsets such that few edges join two vertices in different subsets. Several new graph partitioning algorithms have been developed in the past few years, and we survey some of this activity. We describe the terminology associated with graph partitioning, the complexity of computing good separators, and graphs that have good separators. We then discuss early algorithms for graph partitioning, followed by three new algorithms based on geometric, algebraic, and multilevel ideas. The algebraic algorithm relies on an eigenvector of a Laplacian matrix associated with the graph to compute the partition. The algebraic algorithm is justified by formulating graph partitioning as a quadratic assignment p...
Nonpolyhedral Relaxations of GraphBisection Problems
, 1993
"... We study the problem of finding the minimum bisection of a graph into two parts of prescribed sizes. We formulate two lower bounds on the problem by relaxing node and edgeincidence vectors of cuts. We prove that both relaxations provide the same bound. The main fact we prove is that the duality be ..."
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Cited by 39 (8 self)
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We study the problem of finding the minimum bisection of a graph into two parts of prescribed sizes. We formulate two lower bounds on the problem by relaxing node and edgeincidence vectors of cuts. We prove that both relaxations provide the same bound. The main fact we prove is that the duality between the relaxed edge and nodevectors preserves very natural cardinality constraints on cuts. We present an analogous result also for the maxcut problem, and show a relation between the edge relaxation and some other optimality criteria studied before. Finally, we briefly mention possible applications for a practical computational approach.