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Data Fit Problems (DFP)
\mathbf {\sum_{k=0}^{K} \|f(x, X_k)-Y_k\| ^2 \to min}
subjected to
\mathbf{lb} \le \mathbf{x} \le \mathbf{ub}
\mathbf{A x} \le \mathbf{b}
\mathbf{A}_\mathbf{eq} \mathbf{x} = \mathbf{b}_\mathbf{eq}
 \mathbf{\forall i=0,...,I: c_i(x) \le 0}
 \mathbf{\forall j=0,...,J: h_j(x) = 0}
 \mathbf{f, c_i, h_j :R^n \to R}
\mathbf{x \in R^n,\ X_k \in R^m, Y_k \in R^s}

OpenOpt DFP examples:

DFP solvers connected to OpenOpt

Solver License Made by Info
converter to nlp BSD Dmitrey Example: r = p.solve('nlp:ralg'). Can handle only smooth functions. See NLP page for list of available NLP solvers
Retrieved from "http://openopt.org/DFP"
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