Boolean Literals Research Articles

Sketching Approximability of All Finite CSPs

A constraint satisfaction problem (CSP), \(\textsf {Max-CSP}(\mathcal {F})\) , is specified by a finite set of constraints \(\mathcal {F}\subseteq \lbrace [q]^k \rightarrow \lbrace 0,1\rbrace \rbrace\) for positive integers q and k . An instance of the problem on n variables is given by m applications of constraints from \(\mathcal {F}\) to subsequences of the n variables, and the goal is to find an assignment to the variables that satisfies the maximum number of constraints. In the (γ ,β)-approximation version of the problem for parameters 0 ≤ β ≤ γ ≤ 1, the goal is to distinguish instances where at least γ fraction of the constraints can be satisfied from instances where at most β fraction of the constraints can be satisfied. In this work, we consider the approximability of this problem in the context of sketching algorithms and give a dichotomy result. Specifically, for every family \(\mathcal {F}\) and every β < γ, we show that either a linear sketching algorithm solves the problem in polylogarithmic space or the problem is not solvable by any sketching algorithm in \(o(\sqrt {n})\) space. In particular, we give non-trivial approximation algorithms using polylogarithmic space for infinitely many constraint satisfaction problems. We also extend previously known lower bounds for general streaming algorithms to a wide variety of problems, and in particular the case of q = k =2, where we get a dichotomy, and the case when the satisfying assignments of the constraints of \(\mathcal {F}\) support a distribution on \([q]^k\) with uniform marginals. Prior to this work, other than sporadic examples, the only systematic classes of CSPs that were analyzed considered the setting of Boolean variables q = 2, binary constraints k =2, and singleton families \(|\mathcal {F}|=1\) and only considered the setting where constraints are placed on literals rather than variables. Our positive results show wide applicability of bias-based algorithms used previously by [ 47 ] and [ 41 ], which we extend to include richer norm estimation algorithms, by giving a systematic way to discover biases. Our negative results combine the Fourier analytic methods of [ 56 ], which we extend to a wider class of CSPs, with a rich collection of reductions among communication complexity problems that lie at the heart of the negative results. In particular, previous works used Fourier analysis over the Boolean cube to initiate their results and the results seemed particularly tailored to functions on Boolean literals (i.e., with negations). Our techniques surprisingly allow us to get to general q -ary CSPs without negations by appealing to the same Fourier analytic starting point over Boolean hypercubes.

CTFTP: A Test Case Generation Strategy for General Boolean Expressions Based on Ordered Binary Label-Driven Petri Nets

Boolean expression testing requires certain types of tests for each Boolean expression in program specification or implementation. Fault-based testing essentially uses a subset of the exhaustive test set to detect certain special types of faults. A fault-based Boolean expression testing strategy called constraint true and false test point (CTFTP) is proposed. The test consists of two test case generation strategies, namely a unique constraint true point (UCTP) strategy and a near constraint false point (NCFP) strategy. An ordered binary label-driven Petri net model is presented to analyze the interaction between Boolean transitions and Boolean literals and yield the test paths of a singular term for the irredundant disjunctive normal forms (IDNFs). On the basis of the test paths, we develop a configuration-based IDNF test generation algorithm, which is employed to obtain the UCTP, NCFP, and CTFTP test sets for the IDNFs. The proposed test generation algorithm based on literal substitution is applied to extend the CTFTP strategy and generate a test suite for general Boolean expressions, which are evaluated using TCAS II specifications. Experimental results show that the CTFTP strategy can detect the same seven types of faults similar to the MUMCUT strategy when testing IDNFs, but only a subset of the MUMCUT test set is required. Five types of faults of general Boolean expressions can also be detected using CTFTP strategies.

Optimal and heuristic algorithms to synthesize lattices of four-terminal switches

In this work, we study implementation of Boolean functions with nano-crossbar arrays where each crosspoint behaves as a four-terminal switch controlled by a Boolean literal. These types of arrays are commonly called as switching lattices. We propose optimal and heuristic algorithms that minimize lattice sizes to implement a given Boolean function. The algorithms are mainly constructed on a technique that finds Boolean functions of lattices having independent inputs. This technique works recursively by using transition matrices representing columns and rows of the lattice. It performs symbolic manipulation of Boolean literals as opposed to using truth tables that allows us to successfully find Boolean functions having up to 81 variables corresponding to a 9 × 9-lattice. With a Boolean function of a certain sized lattice, we check if a given function can be implemented with this lattice size by defining the problem as a satisfiability problem. This process is repeated until a desired solution is found. Additionally, we fix the previously proposed algorithm that is claimed to be optimal. The fixed version guarantees optimal sizes. Finally, we perform synthesis trials on standard benchmark circuits to evaluate the proposed algorithms by considering lattice sizes and runtimes in comparison with the recently proposed three algorithms.

Boolean Equi-propagation for Concise and Efficient SAT Encodings of Combinatorial Problems

We present an approach to propagation-based SAT encoding of combinatorial problems, Boolean equi-propagation, where constraints are modeled as Boolean functions which propagate information about equalities between Boolean literals. This information is then applied to simplify the CNF encoding of the constraints. A key factor is that considering only a small fragment of a constraint model at one time enables us to apply stronger, and even complete, reasoning to detect equivalent literals in that fragment. Once detected, equivalences apply to simplify the entire constraint model and facilitate further reasoning on other fragments. Equi-propagation in combination with partial evaluation and constraint simplification provide the foundation for a powerful approach to SAT-based finite domain constraint solving. We introduce a tool called BEE (Ben-Gurion Equi-propagation Encoder) based on these ideas and demonstrate for a variety of benchmarks that our approach leads to a considerable reduction in the size of CNF encodings and subsequent speed-ups in SAT solving times.

A logical model of HCP

For an arbitrary undirected graph G, we are designing a logical model for the Hamiltonian Cycle Problem (HCP), using tools of Boolean algebra only. The obtained model is a logic formulation of the conditions for the existence of the Hamiltonian cycle, and uses m Boolean variables, where m is the number of the edges of a graph. This Boolean expression is true if and only if an initial graph is Hamiltonian. In general, the obtained Boolean expression may have an exponential length (the number of Boolean literals) and may be used for construction of the solution algorithm.