How to write conditional constraints and sum the result in Linear Programming in Python?

Question

I want to use the sum of a series of linear expressions as objective and constraints. These linear expressions are chosen to be included or not based on some conditions. I can achieve it in Excel Solver but don't know how to code it in python.

I've seen a solution of adding conditional constraints here but it couldn't take the sum of all the needed expressions before binding a limit.

Here is the model to be solved.

Variables:

$x_0\ge0,\ x_1\ge0,\ x_2\ge0,\ x_3\ge0 $

There are $n$ linear expressions:

$f_i(x_0,x_1,x_2,x_3) = a_{i0}x_0 + a_{i1}x_1 + a_{i2}x_2 + a_{i3}x_3 + b_i\quad (i=0,1,\dots,n)$

Objective: to minimize the objective function below

$ objective = 0\\ for\ i\ in\ range(n): \\ \quad if\ f_i(x_0,x_1,x_2,x_3)\ge0:\\ \quad\quad objective\ = objective\ + f_i(x_0,x_1,x_2,x_3)\\ $

Constraints: sum $\ge$ -5000

$ sum=0\\ for\ i\ in\ range(n): \\ \quad if\ f_i(x_0,x_1,x_2,x_3)\lt0:\\ \quad\quad sum\ = sum\ + f_i(x_0,x_1,x_2,x_3)\\ $

Answer 1

To minimize $\sum_i \max(f_i,0)$, introduce a nonnegative variable $y_i$ and minimize $\sum_i y_i$ subject to $y_i\ge f_i$.

To enforce $\sum_i \min(f_i,0)\ge -5000$, introduce a nonpositive variable $z_i$ and impose $\sum_i z_i\ge -5000$ subject to $z_i\le f_i$.

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SAS (disclaimer: I work at SAS) can automatically linearize your problem as follows:

   var X {0..3} >= 0;
   impvar F {i in 0..n} = sum {j in 0..3} a[i,j]*X[j] + b[i];
   min MyObj = sum {i in 0..n} max(F[i],0);
   con MyCon: sum {i in 0..n} min(F[i],0) >= -5000;
   solve linearize;