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I have a problem where I have to minimize a certain quantity $Z$ subject to the following constraints:-

  1. $w_1 + w_2 + w_3 = 1$
  2. $\frac{f_1(w_1*Z) + f_2(w_2 * Z) + f_3(w_3 * Z)}{Z} >= k$

where $k$ is a known constant. $f_1$, $f_2$ and $f_3$ are non-linear functions that we have empirical curves for. (These curves are approximately logarithmic if that helps with the solution). $w_1$, $w_2$ and $w_3$ are weights whose optimal values are to be arrived at. I am aware of basic linear programming techniques. However, I was unable to reduce constraint #2 into a linear constraint.

Any help is welcome. Please let me know if any further details are required.

PS:- Solutions that use Python would be ideal. However, I'm more interested in the approach rather than language/package used.

Update (in response to comments):-

  1. $Z$ does not depend on the values of $w_1, w_2, w_3$
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This is just an NLP (Non-Linear Programming) model. You can rewrite it as:

$$\begin{align}\min \>& Z \\ & \sum_i w_i = 1 \\ & \sum_i f_i(w_i\cdot Z)\ge k\cdot Z \\ & w_i \in [0,1] \end{align}$$

Getting rid of a division is always a good idea. If we can assume $Z\gt0$, then a slightly different formulation can look like:

$$\begin{align}\min \>& Z \\ & \sum_i w'_i = Z \\ & \sum_i f_i(w'_i)\ge k\cdot Z \\ & w'_i \ge 0 \end{align}$$

You can recover $w_i$ by $w_i := w'_i/Z$ (using the optimal values for $w'_i$ and $Z$).

Choosing appropriate solvers depends much on the details (e.g. are you looking for global or local solutions). I often try to solve using a few different solvers as predicting the best solver is not that easy. If you can express the model in AMPL or GAMS, you can try different solvers at NEOS.

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