The complexity of Boolean functions
The complexity of Boolean functions
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Polynomial-time algorithms for generation of prime implicants
Journal of Complexity
Espresso-signature: a new exact minimizer for logic functions
DAC '93 Proceedings of the 30th international Design Automation Conference
Building problem solvers
Permutation and phase independent Boolean comparison
Integration, the VLSI Journal
Two-level logic minimization: an overview
Integration, the VLSI Journal
On the Fourier spectrum of monotone functions
Journal of the ACM (JACM)
Improving the Variable Ordering of OBDDs Is NP-Complete
IEEE Transactions on Computers
The Maximum Latency and Identification of Positive Boolean Functions
SIAM Journal on Computing
A survey of Boolean matching techniques for library binding
ACM Transactions on Design Automation of Electronic Systems (TODAES)
ISLPED '98 Proceedings of the 1998 international symposium on Low power electronics and design
On the multiplicative complexity of Boolean functions over the basis ∧,⊕,1
Theoretical Computer Science - Selected papers in honor of Manuel Blum
Branching programs and binary decision diagrams: theory and applications
Branching programs and binary decision diagrams: theory and applications
Logic Minimization Algorithms for VLSI Synthesis
Logic Minimization Algorithms for VLSI Synthesis
Boolean Functions
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We present a new data structure, called a Decomposition Tree (DT), for analysing Boolean functions, and demonstrate a variety of applications. In each node of the DT, appropriate bit-string decomposition fragments are combined by a logical operator. The DT has 2k nodes in the worst case, which implies exponential complexity for problems where the whole tree has to be considered. However, it is important to note that many problems are simpler. We show that these can be handled in an efficient way using the DT. Nevertheless, many problems are of exponential complexity and cannot be made any simpler: for example, the calculation of prime implicants. Using our general DT structure, we present a new worst case algorithm to compute all prime implicants. This algorithm has a lower time complexity than the well-known Quine–McCluskey algorithm and is the fastest corresponding worst case algorithm so far.