Boolean functions minimization by the method of figurative transformations

The object of research is the method of figurative transformations for Boolean functions minimization. One of the most problematic places to minimize Boolean functions is the complexity of the minimization algorithm and the guarantee of obtaining a minimal function. During the study, the method of equivalent figurative transformations was used, which is based on the laws and axioms of the algebra of logic; minimization protocols for Boolean functions that are used when the truth table of a given function has a complete binary combinatorial system with repetition or an incomplete binary combinatorial system with repetition. A reduction in the complexity of the minimization process for Boolean functions is obtained, new criteria for finding minimal functions are established. This is due to the fact that the proposed method of Boolean functions minimization has a number of peculiarities of solving the problem of finding minimal logical functions, in particular: – mathematical apparatus of the block diagram with repetition makes it possible to obtain more information about the orthogonality, adjacency, uniqueness of the truth table blocks; – equivalent figurative transformations due to the greater information capacity are capable of replacing verbal procedures of algebraic transformations; – result of minimization is estimated based on the sign of the minimum function; – minimum DNF or CNF functions are obtained regardless of the given normal form of the logical function, which means that it is necessary to minimize the given function for two normal forms — DNF and CNF using the full truth table; This ensures that it is possible to obtain an optimal reduction in the number of variables of a given function without losing its functionality. The effectiveness of the use of equivalent figurative transformations for Boolean functions minimization is demonstrated by examples of minimization of functions borrowed from other methods for the purpose of comparison. Compared with similar well-known methods of Boolean functions minimization, this provides: – less complexity of the minimization procedure for Boolean functions; – guaranteed Boolean functions minimization; – self-sufficiency of the specified method of Boolean functions minimization due to the introduction of features of the minimal function and minimization of two normal forms – DNF and CNF on the complete truth table of a given Boolean functio

BOOM - A Heuristic Boolean Minimizer

This paper presents an algorithm for two-level Boolean minimization (BOOM) based on a new implicant generation paradigm. In contrast to all previous minimization methods, where the implicants are generated bottom-up, the proposed method uses a top-down approach. Thus, instead of increasing the dimensionality of implicants by omitting literals from their terms, the dimension of a term is gradually decreased by adding new literals. The method is advantageous especially for functions with many input variables (up to thousands) and with only few care terms defined, where other minimization tools are not applicable because of the long runtime. The method has been tested on several different kinds of problems and the results were compared with ESPRESSO

A recursive paradigm to solve Boolean relations

A Boolean relation can specify some types of flexibility of a combinational circuit that cannot be expressed with don't cares. Several problems in logic synthesis, such as Boolean decomposition or multilevel minimization, can be modeled with Boolean relations. However, solving Boolean relations is a computationally expensive task. This paper presents a novel recursive algorithm for solving Boolean relations. The algorithm has several features: efficiency, wide exploration of solutions, and customizable cost function. The experimental results show the applicability of the method in logic minimization problems and tangible improvements with regard to previous heuristic approaches

Minimization for Generalized Boolean Formulas

The minimization problem for propositional formulas is an important optimization problem in the second level of the polynomial hierarchy. In general, the problem is Sigma-2-complete under Turing reductions, but restricted versions are tractable. We study the complexity of minimization for formulas in two established frameworks for restricted propositional logic: The Post framework allowing arbitrarily nested formulas over a set of Boolean connectors, and the constraint setting, allowing generalizations of CNF formulas. In the Post case, we obtain a dichotomy result: Minimization is solvable in polynomial time or coNP-hard. This result also applies to Boolean circuits. For CNF formulas, we obtain new minimization algorithms for a large class of formulas, and give strong evidence that we have covered all polynomial-time cases

NP-hardness of circuit minimization for multi-output functions

Can we design efficient algorithms for finding fast algorithms? This question is captured by various circuit minimization problems, and algorithms for the corresponding tasks have significant practical applications. Following the work of Cook and Levin in the early 1970s, a central question is whether minimizing the circuit size of an explicitly given function is NP-complete. While this is known to hold in restricted models such as DNFs, making progress with respect to more expressive classes of circuits has been elusive. In this work, we establish the first NP-hardness result for circuit minimization of total functions in the setting of general (unrestricted) Boolean circuits. More precisely, we show that computing the minimum circuit size of a given multi-output Boolean function f : {0,1}^n ? {0,1}^m is NP-hard under many-one polynomial-time randomized reductions. Our argument builds on a simpler NP-hardness proof for the circuit minimization problem for (single-output) Boolean functions under an extended set of generators. Complementing these results, we investigate the computational hardness of minimizing communication. We establish that several variants of this problem are NP-hard under deterministic reductions. In particular, unless ? = ??, no polynomial-time computable function can approximate the deterministic two-party communication complexity of a partial Boolean function up to a polynomial. This has consequences for the class of structural results that one might hope to show about the communication complexity of partial functions