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There is promise of efficiency gains in simulator-in-the-loop analog circuit optimization if one uses numerical performance modeling on simulation data to relate design parameters to performance values. However, the choice of modeling approach can impact performance. We analyze and compare these approaches: polynomials, posynomials, genetic programming, feedforward neural networks, boosted feedforward neural networks, multivariate adaptive regression splines, support vector machines, and kriging. Experiments are conducted on a dataset used previously for posynomial modeling, showing the strengths and weaknesses of the different methods in the context of circuit optimization.