lecture12

lecture12 - Advanced Computer Graphics Professor Brogan...

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Advanced Computer Graphics Professor Brogan
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Simulated Annealing Monte Carlo approach for minimizing multivariate functions Monte Carlo = Random = Stochastic Requires one ‘goodness’ metric – result of evaluation function Multivariate – selects multiple parameter values to minimize evaluation function
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Algorithm Outline Select some initial guess of evaluation function parameters: x 0 • Evaluate evaluation function, E(x 0 )=v • Compute a random displacement, x’ 0 The Monte Carlo event • Evaluate E(x’ 0 ) = v’ – If v’ < v; set new state, x 1 = x’ 0 – Else set x 1 = x’ 0 with Prob(E,T) This is the Metropolis step Repeat with updated state and temp
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What is Annealing? Used to treat work-hardened parts made out of low-carbon steels Heat to a specific temperature, then soak, and then cool slowly Thermodynamics – molecules can move around when they are at high temps. Slow cooling permits self organization into minimum energy configurations
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lecture12 - Advanced Computer Graphics Professor Brogan...

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