Quadratizations of symmetric pseudo-Boolean functions: sub-linear bounds on the number of auxiliary variables
Endre Boros, Yves Crama, Elisabeth Rodríguez-Heck
The problem of minimizing a pseudo-Boolean function with no additional constraints arises in a variety of applications.A quadratization is a quadratic reformulation of the nonlinear problem obtained by introducing a set of auxiliary binary variables which can be optimized using quadratic optimization techniques.Using the well-known result that a pseudo-Boolean function can be uniquely expressed as a multilinear polynomial, any pseudo-Boolean function can be quadratized by providing a quadratization for negative monomials and one for positive monomials.A desirable property for a quadratization is to introduce small number of auxiliary variables.For the case of negative monomials there exists a quadratization using a single auxiliary variable which is, in addition, submodular.However, much less is known for positive monomials.The best lower bound on the number of variables required by a quadratization in the literature is n-1 2 .We present here new quadratizations of the positive monomial that significantly improve this lower bound decreasing it to a logarithmic bound.This lower bound is derived from a more general result, stating that a quadratization with a logarithmic number of variables can be defined for exact kout-of-n functions, which is a more general class of symmetric functions.Moreover, for exact k-out-of-n functions we also prove that a logarithmic number of variables is necessary when requiring certain symmetry conditions to the quadratization.Finally, we provide quadratizations for general symmetric functions using O( √ n) auxiliary variables.This upper bound nicely matches a lower bound of Ω( √ n) variables that was recently introduced.
ISAIM•Jan 4, 2018•7 citations