Suppose $0\le x \le 1$ is a decision variable and $\gamma(x)$ is defined as follows: $$ \gamma(x)= \begin{cases} \theta & x>0\\ 0 & x=0 \end{cases} $$ where $0\le \theta\le 1$.
In my model, I have both $\gamma(x)$ and $x \gamma(x)$ and I want to convert them to linear programs. I used the following constraints: \begin{align} \Gamma&\ge \theta x - (1- y ) \\ \Gamma&\le y \\ \gamma&=\theta y \\ y &\ge x \end{align}
Here, the problem is that $x$ can take value 0 and then, $\gamma = \theta$ at the same time. I can add the constraint $y \le 10000 x$, but it is will exclude some parts of the solution space.