Pseudo-Boolean function

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In mathematics and optimization, a pseudo-Boolean function is a function of the form


where B = {0, 1} is a Boolean domain and n is a nonnegative integer called the arity of the function. A Boolean function is then a special case, where the values are also restricted to 0,1.


Any pseudo-Boolean function can be written uniquely as a multi-linear polynomial:[1][2]

The degree of the pseudo-Boolean function is simply the degree of the polynomial in this representation.

In many settings (e.g., in Fourier analysis of pseudo-Boolean functions), a pseudo-Boolean function is viewed as a function that maps to . Again in this case we can uniquely write as a multi-linear polynomial: where are Fourier coefficients of and .


Minimizing (or, equivalently, maximizing) a pseudo-Boolean function is NP-hard. This can easily be seen by formulating, for example, the maximum cut problem as maximizing a pseudo-Boolean function.[3]


The submodular set functions can be viewed as a special class of pseudo-Boolean functions, which is equivalent to the condition

This is an important class of pseudo-boolean functions, because they can be minimized in polynomial time.

Roof Duality[edit]

If f is a quadratic polynomial, a concept called roof duality can be used to obtain a lower bound for its minimum value.[3] Roof duality may also provide a partial assignment of the variables, indicating some of the values of a minimizer to the polynomial. Several different methods of obtaining lower bounds were developed only to later be shown to be equivalent to what is now called roof duality.[3]


If the degree of f is greater than 2, one can always employ reductions to obtain an equivalent quadratic problem with additional variables.[4] One possible reduction is

There are other possibilities, for example,

Different reductions lead to different results. Take for example the following cubic polynomial:[5]

Using the first reduction followed by roof duality, we obtain a lower bound of -3 and no indication on how to assign the three variables. Using the second reduction, we obtain the (tight) lower bound of -2 and the optimal assignment of every variable (which is ).

Polynomial Compression Algorithms[edit]

Consider a pseudo-Boolean function as a mapping from to . Then Assume that each coefficient is integral. Then for an integer the problem P of deciding whether is more or equal to is NP-complete. It is proved in [6] that in polynomial time we can either solve P or reduce the number of variables to . Let be the degree of the above multi-linear polynomial for . Then [6] proved that in polynomial time we can either solve P or reduce the number of variables to .

See also[edit]


  • Boros; Hammer (2002). "Pseudo-Boolean Optimization". Discrete Applied Mathematics. 123. doi:10.1016/S0166-218X(01)00341-9.
  • Crowston; Fellows, Gutin; Jones, Rosamond; Thomasse, Yeo (2011). "Simultaneously Satisfying Linear Equations Over GF(2): MaxLin2 and Max-r-Lin2 Parameterized Above Average". Proc. of FSTTCS 2011. arXiv:1104.1135. Bibcode:2011arXiv1104.1135C.
  • Ishikawa (2011). "Transformation of general binary MRF minimization to the first order case". IEEE Transactions on Pattern Analysis and Machine Intelligence. 33 (6): 1234–1249. doi:10.1109/tpami.2010.91.
  • Rother; Kolmogorov; Lempitsky; Szummer (2007). "Optimizing Binary MRFs via Extended Roof Duality" (PDF). International Conference on Computer Vision and Pattern Recognition.
  • Kahl; Strandmark (2011). "Generalized Roof Duality for Pseudo-Boolean Optimization" (PDF). International Conference on Computer Vision.
  • O'Donnell, Ryan (2008). "Some topics in analysis of Boolean functions". ECCC TR08-055. External link in |journal= (help)


  1. ^ Hammer, P.L.; Rosenberg, I.; Rudeanu, S. (1963). "On the determination of the minima of pseudo-Boolean functions". Studii ¸si cercetari matematice (in Romanian) (14): 359–364. ISSN 0039-4068.
  2. ^ Hammer, Peter L.; Rudeanu, Sergiu (1968). Boolean Methods in Operations Research and Related Areas. Springer. ISBN 978-3-642-85825-3.
  3. ^ a b c Boros and Hammer, 2002
  4. ^ Ishikawa, 2011
  5. ^ Kahl and Strandmark, 2011
  6. ^ a b Crowston et al., 2011