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Fitting a data set with a parametrized model can be seen geometrically asfinding the global minimum of the chi^2 hypersurface, depending on a set ofparameters {P_i}. This is usually done using the Levenberg-Marquardt algorithm.The main drawback of this algorithm is that despite of its fast convergence, itcan get stuck if the parameters are not initialized close to the finalsolution. We propose a modification of the Metropolis algorithm introducing aparameter step tuning that optimizes the sampling of parameter space. Theability of the parameter tuning algorithm together with simulated annealing tofind the global chi^2 hypersurface minimum, jumping across chi^2{P_i} barrierswhen necessary, is demonstrated with synthetic functions and with real data.

Fitting in a complex chi^2 landscape using an optimized hypersurface sampling