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Scott Kirkpatrick is a computer scientist whose work connects statistical physics with computational complexity theory and optimization. With Charles Daniel Gelatt Jr. and Mario P. Vecchi, he developed simulated annealing as a method for combinatorial optimization.^[ref]

The method borrows a rule from the Metropolis algorithm: a proposed increase $\Delta C>0$ in cost can be accepted with probability $\exp (-\Delta C/T)$, where $T$ is an adjustable control parameter in the same units as the cost. Allowing occasional uphill changes can escape a local minimum; reducing $T$ progressively makes them less likely. The original work applied this physical analogy to electronic component placement and wiring.^[ref]