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

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 Cp-based model selection and change-point identification. The numerical results and the extensive comparisons show that evolutionary Monte Carlo is a promising approach for simulation and optimization.

this paper. For example, output from testing Rosenbrock's function for 12 variables consists of the following: 20 X0 VECTOR: -1.20 1.00-1.20 1.00 -1.20 1.00-1.20 1.00 -1.20 1.00-1.20 1.00 Y VECTOR: -1.09 0.77-0.88 0.64 0.71 0.58 0.94-0.90 -0.62 0.77-0.90-0.98 ENTERING TESTGH ROUTINE: THE FUNCTION VALUE AT X = 1.45200000E+02 THE FIRST-ORDER TAYLOR TERM, (G, Y) = 3.19353760E+02 THE SECOND-ORDER TAYLOR TERM, (Y,HY) = 5.39772665E+03 EPSMIN = 1.42108547E-14 EPS F TAYLOR DIFF. RATIO 5.0000E-01 1.09854129E+03 9.79592712E+02 1.18948574E+02 2.5000E-01 4.07080835E+02 3.93717398E+02 1.33634374E+01 8.90104621E+00 1.2500E-01 2.28865318E+02 2.27288959E+02 1.57635878E+00 8.47740855E+00 6.2500E-02 1.75893210E+02 1.75702045E+02 1.91165417E-01 8.24604580E+00 3.1250E-02 1.57838942E+02 1.57815414E+02 2.35282126E-02 8.12494428E+00 1.5625E-02 1.50851723E+02 1.50848805E+02 2.91806005E-03 8.06296382E+00 7.8125E-03 1.47860040E+02 1.47859677E+02 3.63322099E-04 8.03160629E+00 3.9063E-03 1.46488702E+02 1.46488657E+02 4.53255493E-05 8.01583443E+00 1.9531E-03 1.45834039E+02 1.45834033E+02 5.66008660E-06 8.00792506E+00 9.7656E-04 1.45514443E+02 1.45514443E+02 7.07160371E-07 8.00396463E+00 4.8828E-04 1.45356578E+02 1.45356578E+02 8.83731524E-08 8.00198196E+00 DIFF IS SMALL (LESS THAN 2.97291798E-08 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...

this paper.