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. Any pseudo-Boolean function can be written uniquely as a multi-linear polynomial:
An important class of pseudo-Boolean functions are the submodular functions, because polynomimal-time algorithms exists for minimizing them. The degree of the pseudo-Boolean function is simply the degree of the polynomial.
In Fourier analysis of pseudo-Boolean functions, a pseudo-Boolean function is defined as a function that maps to . Again in this case we can write as a multi-linear polynomial: where are Fourier coefficients of and . For a nice and simple introduction to Fourier analysis of pseudo-Boolean functions, see [1].
Optimization
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.
Submodularity
A pseudo-Boolean function f is said to be submodular if
for every x and y. This is a very importand concept, because a submodular pseudo-boolean function can be minimized in polynomial time.[citation needed]
Roof Duality
If f is a quadratic polynomial, a concept called roof duality can be used to obtain a lower bound for its minimum value.[2] 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.[2]
Reductions
If the degree of f is greater than 2, one can always employ reductions to obtain an equivalent quadratic problem with additional variables.[3] One possible reduction is
There are other possibilities, for example,
Different reductions lead to different results. Take for example the following cubic polynomial:
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 ).
See also
References
- Boros (2002). "Pseudo-Boolean Optimization". Discrete Applied Mathematics. 123. doi:10.1016/S0166-218X(01)00341-9.
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- Rother (2007). "Optimizing Binary MRFs via Extended Roof Duality" (PDF). International Conference on Computer Vision and Pattern Recognition.
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