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f_{j}(x)\lambda_{j}.
</math>
Where <math> f_{j} </math> is one of <math> M </math> functions that are used for the approximation. The fitting function is linear in respect to <math>\lambda_j </math>, the fitting parameters, but is not necessarily linear in respect to <math> x</math>. The algorithm searches for the optimal fit parameters, by minimizing the error denoted by:
:<math>
E=\sum_{i=1}^N
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