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I have this problem:

\begin{align} \min_{\mathbf{w}} & \sum_{i=1}^N c_i P_i(w_i)\\ s.t & \notag\\ & \sum_{i =1 }^N w_i = w \\ & 0 \leq w_i \leq w_{max},~~\forall i \in 1, ..., N \end{align}

where $P_i(w)$ is a piecewise linear convex function. Is there a closed formula to solve this? If not, what is the most relevant algorithm?

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If $P_i(w)$ is a piecewise linear, convex function, then it can be written as the maximum of a number of linear functions, $P_i(w)=\max \{L_{i1}(w),\ldots,L_{iJ_i}(w)\}$. Then, the optimization problem allows for the reformulation $$ \min_{\mathbf w,\mathbf x} \sum_{i=1}^N c_ix_i = \mathbf c^T \mathbf x, \\ \sum_{i=1}^N w_i = w \\ 0\le w_i \le w_{max}, \qquad i=1,\ldots,N \\ x_i \ge L_{ij}(w) \qquad j=1,\ldots,J_i. $$ This is a linear program with linear constraints that can be solved using, for example, the simplex algorithm. There is, in general, no closed-form solution, however.

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  • $\begingroup$ Thank you for your answer! I have come to the same conclusion myself. $\endgroup$ – burnedWood Jan 22 '15 at 20:14

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