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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 75 (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
Evolutionary Monte Carlo: Applications to C_p Model Sampling and Change Point Problem
 STATISTICA SINICA
, 2000
"... Motivated by the success of genetic algorithms and simulated annealing in hard optimization problems, the authors propose a new Markov chain Monte Carlo (MCMC) algorithm so called an evolutionary Monte Carlo algorithm. This algorithm has incorporated several attractive features of genetic algorithms ..."
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Cited by 25 (5 self)
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Motivated by the success of genetic algorithms and simulated annealing in hard optimization problems, the authors propose a new Markov chain Monte Carlo (MCMC) algorithm so called an evolutionary Monte Carlo algorithm. This algorithm has incorporated several attractive features of genetic algorithms and simulated annealing into the framework of MCMC. It works by simulating a population of Markov chains in parallel, where each chain is attached to a different temperature. The population is updated by mutation (Metropolis update), crossover (partial state swapping) and exchange operators (full state swapping). The algorithm is illustrated through examples of the Cpbased model selection and changepoint identification. The numerical results and the extensive comparisons show that evolutionary Monte Carlo is a promising approach for simulation and optimization.
Optimizing an Empirical Scoring Function for Transmembrane Protein Structure Determination
, 2004
"... We examine the problem of transmembrane protein structure determination. Like many questions that arise in biological research, this problemcannot be addressed generally by traditional laboratory experimentation alone. Instead, an approach that integrates experiment and computation is required. We f ..."
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Cited by 5 (4 self)
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We examine the problem of transmembrane protein structure determination. Like many questions that arise in biological research, this problemcannot be addressed generally by traditional laboratory experimentation alone. Instead, an approach that integrates experiment and computation is required. We formulate the transmembrane protein structure determination problem as a boundconstrained optimization problem using a special empirical scoring function, called Bundler, as the objective function. In this paper, we describe the optimization problem and its mathematical properties, and we examine results obtained using two different derivativefree optimization algorithms.
MO Mathematical Optimization
"... this paper. For example, output from testing Rosenbrock's function for 12 variables consists of the following: 20 X0 VECTOR: 1.20 1.001.20 1.00 1.20 1.001.20 1.00 1.20 1.001.20 1.00 Y VECTOR: 1.09 0.770.88 0.64 0.71 0.58 0.940.90 0.62 0.770.900.98 ENTERING TESTGH ROUTINE: THE FUNCTION V ..."
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this paper. For example, output from testing Rosenbrock's function for 12 variables consists of the following: 20 X0 VECTOR: 1.20 1.001.20 1.00 1.20 1.001.20 1.00 1.20 1.001.20 1.00 Y VECTOR: 1.09 0.770.88 0.64 0.71 0.58 0.940.90 0.62 0.770.900.98 ENTERING TESTGH ROUTINE: THE FUNCTION VALUE AT X = 1.45200000E+02 THE FIRSTORDER TAYLOR TERM, (G, Y) = 3.19353760E+02 THE SECONDORDER TAYLOR TERM, (Y,HY) = 5.39772665E+03 EPSMIN = 1.42108547E14 EPS F TAYLOR DIFF. RATIO 5.0000E01 1.09854129E+03 9.79592712E+02 1.18948574E+02 2.5000E01 4.07080835E+02 3.93717398E+02 1.33634374E+01 8.90104621E+00 1.2500E01 2.28865318E+02 2.27288959E+02 1.57635878E+00 8.47740855E+00 6.2500E02 1.75893210E+02 1.75702045E+02 1.91165417E01 8.24604580E+00 3.1250E02 1.57838942E+02 1.57815414E+02 2.35282126E02 8.12494428E+00 1.5625E02 1.50851723E+02 1.50848805E+02 2.91806005E03 8.06296382E+00 7.8125E03 1.47860040E+02 1.47859677E+02 3.63322099E04 8.03160629E+00 3.9063E03 1.46488702E+02 1.46488657E+02 4.53255493E05 8.01583443E+00 1.9531E03 1.45834039E+02 1.45834033E+02 5.66008660E06 8.00792506E+00 9.7656E04 1.45514443E+02 1.45514443E+02 7.07160371E07 8.00396463E+00 4.8828E04 1.45356578E+02 1.45356578E+02 8.83731524E08 8.00198196E+00 DIFF IS SMALL (LESS THAN 2.97291798E08 IN ABSOLUTE VALUE) Note that the RATIO is larger than eight when EPS is larger and then decreases steadily. A small error in the code would produce much different values. We encourage the student to try this testing routine on several subroutines that compute objective functions and their derivatives; errors should be introduced into the derivative codes systematically to examine the ability of TESTGH to detect them and provide the right diagnosis, as outlined above. Methods for Unconstrained Continuous...
COINOR METSlib a Metaheuristics Framework in Modern C++.
, 2011
"... (this document refers to version 0.5.3 of the library) Contents ..."