In the context of discrete optimization, what exactly does it mean to "quadratize" a function?
The term seems to be used mainly by operations researchers, in my experience.
In the context of discrete optimization, what exactly does it mean to "quadratize" a function?
The term seems to be used mainly by operations researchers, in my experience.
One definition of quadratization (perhaps there is more) is provided in the paper by Boros, 2018.
In non-mathematical terms, quadratization is defined as
a quadratic reformulation of the nonlinear problem obtained by introducing a set of auxiliary binary variables which can be optimized using quadratic optimization techniques.
Rewriting this in functional notation,
Given a pseudo-Boolean function $f(x)$ on $\{0,1\}^n$, we say that $g(x,y)$ is a quadratization of $f$ if $g(x,y)$ is a quadratic polynomial depending on $x$ and on $m$ auxiliary variables $y_1,\cdots,y_m$, such that $$f(x)=\min\limits_{y\in\{0,1\}^m}g(x,y)\quad\forall x\in\{0,1\}^n.$$
Note that a pseudo-Boolean function is one that maps from $\{0,1\}^n$ to $\Bbb R$ which "assigns a real value to each tuple of $n$ binary variables $x_1,\cdots,x_n$".
One addition where the name comes from: in the same way as we speak of linearization (approximating a non-linear function or region by one or many linear functions) quadratization means approximating a non-linear function by quadratic ones.
Reference
[1] Boros, E., Crama, Y., Rodríguez-Heck, E. (2018). Quadratizations of symmetric pseudo-Boolean functions: sub-linear bounds on the number of auxiliary variables. ISAIM.