Abstract
Solving a large subset of multidimensional nonlinear optimization problems can be significantly improved by decoupling their intrinsically linear and nonlinear parts. This effectively decreases the dimensionality of the problem, reduces the search space and improves the efficiency of the optimization. This decoupled approach is generalized with mathematical formalism and its superiority over standard methods empirically verified and quantified on a couple of examples involving $$\chi ^2$$ź2 curve fitting to data.
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