# How to formulate case distinctions in AMPLs objective function?

This is my first real optimisation problem I formulated and now trying to solve by using AMPL.

The following objective function is from a linear 0-1 LP means all variables $$x_i^b\in\{0,1\}$$, with $$i\in[1,n]$$ and $$b$$ referring to the type of the node, which means $$0$$ and $$1$$ say a node is or is not of a certain type.

The objective function is as follows with $$A$$ a set of nodes, $$N[a]$$ a set of all neighbours of $$a$$ including $$a$$, and $$n$$ a given number of types:

$$\min - \sum_{a\in A}f\left(\frac1n\sum\limits_{i=1}^nf\left(\sum\limits_{b\in N[a]}x_i^b\right)\right)$$

with

$$f:\mathbb{R}\mapsto\{0,1\}\\ f(x):=\begin{cases}0,&x<1\\1,&x\geq 1\end{cases}$$

I already heard that AMPL doesn't support the definition of functions/methods and that I have to use the Big M method to create this objective function. I couldn't really figure out yet how to use the method to replace my case distinctions...

Let $$\rho$$ be some small value. \begin{align}M \times f(x) &\geq x - 1 + \rho\\M \times (1 - f(x)) &\geq 1 - x - \rho\end{align}

Here is the small working code in Python pulp

import pulp as pl

prob = pl.LpProblem("Problem", pl.LpMinimize)
x = 1
f = pl.LpVariable("f_{0}", 0, 1, pl.LpBinary)
prob += f
M = 100
prob += M * f >= x - 1 + 0.001
prob += M * (1 - f) >= 1 - x - 0.0001

print(prob)
prob.solve()

for v in prob.variables():
print(v.name, "=", v.varValue)

• Can I ask for another detail - I am very interested in the "WHY" so why is it not possible to solve objective functions with case distinctions directly? – baxbear Feb 14 '20 at 12:31