## Monday, March 6, 2017

### Strict Inequalities in Optimization Models

An often seen question is:
Can I write an inequality of the form:
 $\sum_i a_i x_i < b$

The obvious answer is no. LP, MIP and NLP solvers only allow $$\le$$ and $$\ge$$ inequalities.

Here are some notes:

• Here is the mathematical answer. Strict inequalities cause the feasible region to be an open set. Mathematically, a maximum or minimum over an open set does not exist.  In your theory book you should see that optimization takes place over compact sets.
• A CS answer could be that the difference between $$<$$ and $$\le$$ is not that meaningful in the presence of floating point computations and their round-off errors.
• The same thing holds for bounds: e.g. we write $$x \in [0,1]$$ and not $$x \in (0,1)$$.
• One could do something like: replace “$$x+y<1$$” by “$$x+y \le 0.999$$”.
• Be aware that solvers apply a feasibility tolerance: variables and equations are allowed to be slightly infeasible. So $$\sum a_i x_i \le b$$ really means:
 $\sum_i a_i x_i \le b +\epsilon$
where $$\epsilon>0$$ is the feasibility tolerance.
• If $$x$$ and $$y$$ are binary variables, note that they can assume values slightly smaller or larger than 0 or 1 (similar for integer variables). Solvers apply a so-called integer feasibility tolerance.
• That also means we should not write ”$$x+y\le 1-\varepsilon$$” with say $$\varepsilon=1.0 \times 10^{-15}$$. Our tolerance should be larger than the feasibility tolerance. Even more complications can arise as the result of integer tolerances and scaling. So our tolerance should be relatively large.
• If $$x$$ and $$y$$ are binary (or integer) variables, we can replace “$$x+y<2$$” by “$$x+y \le 1$$”.
• I don’t really know why so many people are asking for this: I never have the urgent need to use strict inequalities in practical models.
##### Piecewise Linear Functions

Here is an example of the last point. Say we want to implement a piecewise linear function as below, taken from (1):

 $f(x)=\begin{cases}-x-3&\text{if }x \le –3\\ x+3&\text{if }-3 In practice we would make things ambiguous at the kinks:  \[f(x)=\begin{cases}-x-3&\text{if }x \le –3\\ x+3&\text{if }-3\le x\le 0\\ -2x+3&\text{if }0 \le x\le 3\\ 0.5x-4.5&\text{if }x \ge 3 \end{cases}$

Of course here the function $$f(x)$$ is continuous and it does not make a difference. If the function $$f(x)$$ would be discontinuous (i.e. jumps instead of kinks) we can let the model pick the “best” (e.g. cheapest or most profitable) segment. In almost any case this is a good choice.

##### Floor function

An example of a discontinuous piecewise linear function is the floor function $$\lfloor x \rfloor$$. This picture is from (2): In practical model we would not care about what is precisely happening at the integer points (most likely picking the “best” value would be a good strategy: we would not want suboptimal solutions based on some artificial $$\varepsilon$$ argument). But if I want to avoid such a discussion, we could model $$y=\lfloor x \rfloor$$ as:

 \boxed{\begin{align}&y \> \text{integer}\\&y \le x\\&y \ge x-0.999\end{align}}

Note that this would create small holes in the set of allowed $$x$$ values.

##### References
1. Piecewise linear function, https://en.wikipedia.org/wiki/Piecewise_linear_function
2. Floor function, http://mathworld.wolfram.com/FloorFunction.html
3. GAMS: Piecewise linear functions with SOS2 variables, http://yetanothermathprogrammingconsultant.blogspot.com/2009/06/gams-piecewise-linear-functions-with.html