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22
Mesh Generation And Optimal Triangulation
, 1992
"... We survey the computational geometry relevant to finite element mesh generation. We especially focus on optimal triangulations of geometric domains in two and threedimensions. An optimal triangulation is a partition of the domain into triangles or tetrahedra, that is best according to some cri ..."
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Cited by 216 (7 self)
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We survey the computational geometry relevant to finite element mesh generation. We especially focus on optimal triangulations of geometric domains in two and threedimensions. An optimal triangulation is a partition of the domain into triangles or tetrahedra, that is best according to some criterion that measures the size, shape, or number of triangles. We discuss algorithms both for the optimization of triangulations on a fixed set of vertices and for the placement of new vertices (Steiner points). We briefly survey the heuristic algorithms used in some practical mesh generators.
Automatic Creation of BoundaryRepresentation Models from Single Line Drawings
, 2002
"... This thesis presents methods for the automatic creation of boundaryrepresentation models of polyhedral objects from single line drawings depicting the objects. This topic is important in that automated interpretation of freehand sketches would remove a bottleneck in current engineering design metho ..."
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Cited by 19 (12 self)
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This thesis presents methods for the automatic creation of boundaryrepresentation models of polyhedral objects from single line drawings depicting the objects. This topic is important in that automated interpretation of freehand sketches would remove a bottleneck in current engineering design methods. The thesis does not consider conversion of freehand sketches to line drawings or methods which require manual intervention or multiple drawings. Thge thesis contains a number of...
Generation of yieldaware Pareto surfaces for hierarchical circuit design space exploration
 in Proc. Des. Autom. Conf., 2006
"... Pareto surfaces in the performance space determine the range of feasible performance values for a circuit topology in a given technology. We present a nondominated sorting based global optimization algorithm to generate the nominal pareto front efficiently using a simulatorinaloop approach. Th ..."
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Cited by 15 (1 self)
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Pareto surfaces in the performance space determine the range of feasible performance values for a circuit topology in a given technology. We present a nondominated sorting based global optimization algorithm to generate the nominal pareto front efficiently using a simulatorinaloop approach. The solutions on this pareto front combined with efficient Monte Carlo approximation ideas are then used to compute the yieldaware pareto fronts. We show experimental results for both the nominal and yieldaware pareto fronts for power and phase noise for a voltage controlled oscillator (VCO) circuit. The presented methodology computes yieldaware pareto fronts in approximately 56 times the time required for a single circuit synthesis run and is thus practically efficient. We also show applications of yieldaware paretos to find the optimal VCO circuit to meet the system level specifications of a phase locked loop.
A General Purpose Distributed Implementation of Simulated Annealing
, 1992
"... In this paper, we present a problem independent general purpose parallel implementation of simulated annealing on distributed messagepassing multiprocessor systems. The sequential algorithm is studied and we give a classi#cation of combinatorial optimization problems together with their neighborhoo ..."
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Cited by 11 (2 self)
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In this paper, we present a problem independent general purpose parallel implementation of simulated annealing on distributed messagepassing multiprocessor systems. The sequential algorithm is studied and we give a classi#cation of combinatorial optimization problems together with their neighborhood structures. Several parallelization approaches are examinedconsidering their suitability for problems of the various classes. For typical representatives of the di#erent classes goodparallel simulated annealing implementations arepresented.
Chaos and asymptotical stability in discretetime neural networks
 Physica D
, 1997
"... This paper will be published in Physica D. ..."
The cryptanalysis of a three rotor machine using a genetic algorithm
 In Proceedings of the 7th International Conference on Genetics Algorithms ICGA’97
, 1997
"... ..."
Comparative Performance of Simulated Annealing and Genetic Algorithm in Solving Nurse Scheduling Problem
"... subclass of scheduling problems that are hard to solve. The goal is to find high quality shift and resource assignments, in accordance with the labor contract rules, satisfying the requirements of employees as well as the employers in healthcare institutions. The Nurse Scheduling Problems (NSP) can ..."
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Cited by 5 (0 self)
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subclass of scheduling problems that are hard to solve. The goal is to find high quality shift and resource assignments, in accordance with the labor contract rules, satisfying the requirements of employees as well as the employers in healthcare institutions. The Nurse Scheduling Problems (NSP) can be viewed as Constraint Satisfaction Problem (CSP) where the constraints are classified as hard and soft constraints. In this paper, a real case of a cyclic nurse Scheduling problem is introduced. This means that the generated roster can be repeated indefinitely if no further constraint is introduced. We use two different methods, namely, Simulated Annealing and Genetic Algorithm to solve this problem and compared their performances at different difficulty levels. Index Terms — constraints, genetic algorithm, nurse scheduling, simulated annealing. I.
Multiprocessor Scheduling Using MeanField Annealing
 in J. Rolim (Ed.), Parallel and Distributed Processing, LNCS 1388
, 1998
"... This paper presents our work on the static task scheduling model using the meanfield annealing (MFA) technique. Meanfield annealing is a technique of thermostatic annealing that takes the statistical properties of particles as its learning paradigm. It combines good features from the Hopfield neur ..."
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Cited by 2 (0 self)
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This paper presents our work on the static task scheduling model using the meanfield annealing (MFA) technique. Meanfield annealing is a technique of thermostatic annealing that takes the statistical properties of particles as its learning paradigm. It combines good features from the Hopfield neural network and simulated annealing, to overcome their weaknesses and improve on their performances. Our MFA model for task scheduling is derived from its prototype, namely, the graph partitioning problem. MFA is deterministic in nature and this gives the advantage of faster convergence to the equilibrium temperature, compared to simulated annealing. Our experimental work verifies this finding on various network and task graph sizes. Our work also includes the simulation of the MFA model on several network topologies and parameters.
Threshold Phenomena in NK Landscapes
, 2001
"... In this thesis, we study the threshold phenomena in the NK landscape, a combinatorial model widely used in the study of genetic algorithms and population genetic dynamics. We establish two random models for the decision problem of the NK landscape model, called the uniform probability model and the ..."
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Cited by 2 (2 self)
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In this thesis, we study the threshold phenomena in the NK landscape, a combinatorial model widely used in the study of genetic algorithms and population genetic dynamics. We establish two random models for the decision problem of the NK landscape model, called the uniform probability model and the fixed ratio model respectively. The aim of the study is to investigate the hardness of the NK landscape model in terms of the theory of threshold phenomena and phase transitions. We show theoretically that the uniform probability model is trivially insoluble as the problem size tends to infinity. For the fixed ratio model, we establish two upper bounds of insolubility on the control parameter of the model above which the problems are asymptotically insoluble with probability 1. We show that instances with parameters above the upper bounds contain some easy subproblems such as 2SAT, and hence can be solved by polynomial algorithms. The fixed ratio model is also studied empirically. The experimental results show that there is a threshold phenomenon in the model and our upper bound on the threshold is tight. From the experiments, we also observe that random instances of the fixed ratio model are also typically easy in the soluble region and phase transition region.
Some Remarks On The Backpropogation Algorithm For Neural Net Learning
, 1988
"... This report contains some remarks about the backpropagation method for neural net learning. We concentrate in particular in the study of local minima of error functions and the growth of weights during learning. Rutgers Center for Systems and Control, 1 Introduction Backpropagation is probably the ..."
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Cited by 1 (1 self)
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This report contains some remarks about the backpropagation method for neural net learning. We concentrate in particular in the study of local minima of error functions and the growth of weights during learning. Rutgers Center for Systems and Control, 1 Introduction Backpropagation is probably the most popular method currently being used for neural net learning. It was introduced in the neural literature by Rumelhart and Hinton in the seminal work [PDP]. See for instance [Hi87] for an introduction and references to current work. It can be understood as an iterative gradient technique for nonlinear least squares fitting, the cost function corresponding to a clustering problem to be solved. Its study gives rise to many open mathematical problems. This note makes some remarks about factors affecting the ultimate performance of backprop and its variants. A basic issue that must be understood when applying a gradient technique is that of the structure of the set of local minima of the err...