Stochastic Tunneling
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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Monte Carlo method-based optimization techniques sample the objective function by randomly "hopping" from the current solution vector to another with a difference in the function value of E. The acceptance probability of such a trial jump is in most cases chosen to be minleft(1;expleft(-betacdotDelta Eright)right) (Metropolis criterion) with an appropriate parameter . The general idea of STUN is to circumvent the slow dynamics of ill-shaped energy functions that one e...
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Monte Carlo method-based optimization techniques sample the objective function by randomly "hopping" from the current solution vector to another with a difference in the function value of E. The acceptance probability of such a trial jump is in most cases chosen to be minleft(1;expleft(-betacdotDelta Eright)right) (Metropolis criterion) with an appropriate parameter . The general idea of STUN is to circumvent the slow dynamics of ill-shaped energy functions that one encounters for example in spin glasses by tunneling through such barriers. This goal is achieved by Monte Carlo sampling of a transformed function that lacks this slow dynamics. In the "standard-form" the transformation reads.
