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Non-Linear Least Squares Problems (NLLSP)
\mathbf {\sum_{k=0}^{K} f_k(x)^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_k, c_i, h_j :R^n \to R \} \subset C^1}}
(smooth differentiable functions)
\mathbf{x \in R^n}

NLLSP solvers connected to OpenOpt:

Solver License Made by Info
scipy_leastsq BSD Argonne national laboratory, Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. more Unconstrained problems only! "leastsq" is a scipy wrapper around MINPACK's lmdif algorithm
converter to nlp Dmitrey Example: r = p.solve('nlp:ralg'). See NLP page for list of available NLP solvers
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