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56
VLSI cell placement techniques
 ACM Computing Surveys
, 1991
"... VLSI cell placement problem is known to be NP complete. A wide repertoire of heuristic algorithms exists in the literature for efficiently arranging the logic cells on a VLSI chip. The objective of this paper is to present a comprehensive survey of the various cell placement techniques, with emphasi ..."
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Cited by 94 (0 self)
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VLSI cell placement problem is known to be NP complete. A wide repertoire of heuristic algorithms exists in the literature for efficiently arranging the logic cells on a VLSI chip. The objective of this paper is to present a comprehensive survey of the various cell placement techniques, with emphasis on standard ce11and macro
Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems
 SIAM Journal on Optimization
, 2004
"... A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for gene ..."
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Cited by 55 (6 self)
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A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for general nonlinear constraints. In generalizing existing algorithms, new theoretical convergence results are presented that reduce seamlessly to existing results for more specific classes of problems. While no local continuity or smoothness assumptions are required to apply the algorithm, a hierarchy of theoretical convergence results based on the Clarke calculus is given, in which local smoothness dictate what can be proved about certain limit points generated by the algorithm. To demonstrate the usefulness of the algorithm, the algorithm is applied to the design of a loadbearing thermal insulation system. We believe this is the first algorithm with provable convergence results to directly target this class of problems.
Characterization Of Signals By The Ridges Of Their Wavelet Transforms
 IEEE Trans. on Signal Processing
, 1994
"... We present a couple of new algorithmic procedures for the detection of ridges in the modulus of the (continuous) wavelet transform of onedimensional signals. These detection procedures are shown to be robust to additive white noise. We also derive and test a new reconstruction procedure. The latter ..."
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Cited by 42 (5 self)
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We present a couple of new algorithmic procedures for the detection of ridges in the modulus of the (continuous) wavelet transform of onedimensional signals. These detection procedures are shown to be robust to additive white noise. We also derive and test a new reconstruction procedure. The latter uses only information from the restriction of the wavelet transform to a sample of points from the ridge. This provides with a very efficient way to code the information contained in the signal. Partially supported by ONR N00014911010 y Supported by NSF IBN 9405146 1 Introduction The characterization and the separation of amplitude and frequency modulated signals is a classical problem of signal analysis and signal processing. Applications can be found in many situations, such as for instance radar/sonar detection and speech processing [9]. Many methods have been proposed in the past few years to analyze the timefrequency localization of signals. The most noticeable are the family...
Stochastic and Deterministic Networks for Texture Segmentation
, 1990
"... This paper describes several texture segmentation algorithms based on deterministic and stochastic relaxation principles, and their implementation on parallel networks. The segmentation problem is posed as an optimization problem and two different optimality criteria a re considered. The first crite ..."
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Cited by 37 (1 self)
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This paper describes several texture segmentation algorithms based on deterministic and stochastic relaxation principles, and their implementation on parallel networks. The segmentation problem is posed as an optimization problem and two different optimality criteria a re considered. The first criterion involves maximizing the posterior distribution of the intensity field given the label field (maximum a posteriori (MAP) estimate). The posterior distribution of the texture labels is derived by modeling the textures as Gauss Markov random field (GMRF) and characterizing the distribution of different texture labels by a discrete multilevel Markov model. Fast approximate solutions for MAP a re obtained using deterministic relaxation techniques implemented on a Hopfield neural network and are compared with those of simulated annealing in obtaining the MAP estimate. A stochastic algorithm which introduces learning into the iterations of the Hopfield network is proposed. This iterated hillclimbing algorithm combines fast convergence of deterministic relaxation with the sustained exploration of the stochastic algorithms, but is guaranteed to find only a local minimum. The second optimality criterion requires minimizing the expected percentage of misclassification per pixel by maximizing the posterior marginal distribution, and the maximum posterior marginal (MPM) algorithm is used to obtain the corresponding solution. All these methods implemented on parallel networks can he easily extended for hierarchical segmentation and we present rewlts of the various schemes in classifying some real textured images.
MultiRidge Detection and TimeFrequency Reconstruction
 IEEE Transactions on Signal Processing
, 1996
"... The ridges of the wavelet transform, the Gabor transform or any timefrequency representation of a signal contain crucial information on the characteristics of the signal. Indeed they mark the regions of the timefrequency plane where the signal concentrates most of its energy. We introduce a new ..."
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Cited by 25 (9 self)
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The ridges of the wavelet transform, the Gabor transform or any timefrequency representation of a signal contain crucial information on the characteristics of the signal. Indeed they mark the regions of the timefrequency plane where the signal concentrates most of its energy. We introduce a new algorithm to detect and identify these ridges. The procedure is based on an original penalization of the transitions of the random walk in a bounded domain of the plane. We show that this detection algorithm is especially useful for noisy signals with multiridge transforms. It is a common practice among practitioners to reconstruct a signal from the skeleton of a transform of the signal (i.e. the restriction of the transform to the ridges). After reviewing several known procedures we introduce a new reconstruction algorithm and we illustrate its usefulness on speech signals. Partially supported by ONR N00014911010 y Supported by NSF IBN 9405146 1 1 Introduction and Notations ...
Simulated Annealing with Extended Neighbourhood
, 1991
"... Simulated Annealing (SA) is a powerful stochastic search method applicable to a wide range of problems for which little prior knowledge is available. It can produce very high quality solutions for hard combinatorial optimization problems. However, the computation time required by SA is very large. V ..."
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Cited by 24 (16 self)
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Simulated Annealing (SA) is a powerful stochastic search method applicable to a wide range of problems for which little prior knowledge is available. It can produce very high quality solutions for hard combinatorial optimization problems. However, the computation time required by SA is very large. Various methods have been proposed to reduce the computation time, but they mainly deal with the careful tuning of SA's control parameters. This paper first analyzes the impact of SA's neighbourhood on SA's performance and shows that SA with a larger neighbourhood is better than SA with a smaller one. The paper also gives a general model of SA, which has both dynamic generation probability and acceptance probability, and proves its convergence. All variants of SA can be unified under such a generalization. Finally, a method of extending SA's neighbourhood is proposed, which uses a discrete approximation to some continuous probability function as the generation function in SA, and several impo...
Distributed coverage games for mobile visual sensors (i): Reaching the set of nash equilibria
 In Proc. of the 48th IEEE Conf. on Decision and Control and 28th Chinese Control Conference
, 2009
"... the set of global optima ..."
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Global Optimization For Constrained Nonlinear Programming
, 2001
"... In this thesis, we develop constrained simulated annealing (CSA), a global optimization algorithm that asymptotically converges to constrained global minima (CGM dn ) with probability one, for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based on the necessary ..."
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Cited by 14 (2 self)
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In this thesis, we develop constrained simulated annealing (CSA), a global optimization algorithm that asymptotically converges to constrained global minima (CGM dn ) with probability one, for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based on the necessary and sufficient condition for constrained local minima (CLM dn ) in the theory of discrete constrained optimization using Lagrange multipliers developed in our group. The theory proves the equivalence between the set of discrete saddle points and the set of CLM dn, leading to the firstorder necessary and sufficient condition for CLM dn. To find
Tuning Strategies In Constrained Simulated Annealing For Nonlinear Global Optimization
 Int’l J. of Artificial Intelligence Tools
, 2000
"... This paper studies various strategies in constrained simulated annealing (CSA), a global optimization algorithm that achieves asymptotic convergence to constrained global minima (CGM) with probability one for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based ..."
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Cited by 10 (1 self)
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This paper studies various strategies in constrained simulated annealing (CSA), a global optimization algorithm that achieves asymptotic convergence to constrained global minima (CGM) with probability one for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based on the necessary and sufficient condition for discrete constrained local minima (CLM) in the theory of discrete Lagrange multipliers and its extensions to continuous and mixedinteger constrained NLPs. The strategies studied include adaptive neighborhoods, distributions to control sampling, acceptance probabilities, and cooling schedules. We report much better solutions than the bestknown solutions in the literature on two sets of continuous benchmarks and their discretized versions.
DISTRIBUTED COVERAGE GAMES FOR ENERGYAWARE MOBILE SENSOR NETWORKS
"... Abstract. Inspired by current challenges in dataintensive and energylimited sensor networks, we formulate a coverage optimization problem for mobile sensors as a (constrained) repeated multiplayer game. Each sensor tries to optimize its own coverage while minimizing the processing/energy cost. The ..."
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Cited by 8 (2 self)
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Abstract. Inspired by current challenges in dataintensive and energylimited sensor networks, we formulate a coverage optimization problem for mobile sensors as a (constrained) repeated multiplayer game. Each sensor tries to optimize its own coverage while minimizing the processing/energy cost. The sensors are subject to the informational restriction that the environmental distribution function is unknown a priori. We present two distributed learning algorithms where each sensor only remembers its own utility values and actions played during the last plays. These algorithms are proven to be convergent in probability to the set of (constrained) Nash equilibria and global optima of certain coverage performance metric, respectively. Numerical examples are provided to verify the performance of our proposed algorithms. 1. Introduction. There