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Register a new userDetermining the Multiplicative Complexity of Boolean Functions using SAT
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Mathias Soeken
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Authors
Mathias Soeken
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We present a constructive SAT-based algorithm to determine the multiplicative complexity of a Boolean function, i.e., the smallest number of AND gates in any logic network that consists of 2-input AND gates, 2-input XOR gates, and inverters. In order to speed-up solving time, we make use of several symmetry breaking constraints; these exploit properties of XAGs that may be useful beyond the proposed SAT-based algorithm. We further propose a heuristic post-optimization algorithm to reduce the number of XOR gates once the optimum number of AND gates has been obtained, which also makes use of SAT solvers. Our algorithm is capable to find all optimum XAGs for representatives of all 5-input affine-equivalent classes, and for a set of frequently occurring 6-input functions.
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The {em Total Influence} ({em Average Sensitivity) of a discrete function is one of its fundamental measures. We study the problem of approximating the total influence of a monotone Boolean function ifnumplusminus=1 $f: {pm1}^n longrightarrow {pm1}$, else $f: bitset^n to bitset$, fi which we denote by $I[f]$. We present a randomized algorithm that approximates the influence of such functions to within a multiplicative factor of $(1pm eps)$ by performing $O(frac{sqrt{n}log n}{I[f]} poly(1/eps)) $ queries. % mnote{D: say something about technique?} We also prove a lower bound of % $Omega(frac{sqrt{n/log n}}{I[f]})$ $Omega(frac{sqrt{n}}{log n cdot I[f]})$ on the query complexity of any constant-factor approximation algorithm for this problem (which holds for $I[f] = Omega(1)$), % and $I[f] = O(sqrt{n}/log n)$), hence showing that our algorithm is almost optimal in terms of its dependence on $n$. For general functions we give a lower bound of $Omega(frac{n}{I[f]})$, which matches the complexity of a simple sampling algorithm.
We provide two sufficient and necessary conditions to characterize any $n$-bit partial Boolean function with exact quantum 1-query complexity. Using the first characterization, we present all $n$-bit partial Boolean functions that depend on $n$ bits and have exact quantum 1-query complexity. Due to the second characterization, we construct a function $F$ that maps any $n$-bit partial Boolean function to some integer, and if an $n$-bit partial Boolean function $f$ depends on $k$ bits and has exact quantum 1-query complexity, then $F(f)$ is non-positive. In addition, we show that the number of all $n$-bit partial Boolean functions that depend on $k$ bits and have exact quantum 1-query complexity is not bigger than $n^{2}2^{2^{n-1}(1+2^{2-k})+2n^{2}}$ for all $ngeq 3$ and $kgeq 2$.
The k-regret query aims to return a size-k subset S of a database D such that, for any query user that selects a data object from this size-k subset S rather than from database D, her regret ratio is minimized. The regret ratio here is modeled by the relative difference in the optimality between the locally optimal object in S and the globally optimal object in D. The optimality of a data object in turn is modeled by a utility function of the query user. Unlike traditional top-k queries, the k-regret query does not minimize the regret ratio for a specific utility function. Instead, it considers a family of infinite utility functions F, and aims to find a size-k subset that minimizes the maximum regret ratio of any utility function in F. Studies on k-regret queries have focused on the family of additive utility functions, which have limitations in modeling individuals preferences and decision making processes, especially for a common observation called the diminishing marginal rate of substitution (DMRS). We introduce k-regret queries with multiplicative utility functions, which are more expressive in modeling the DMRS, to overcome those limitations. We propose a query algorithm with bounded regret ratios. To showcase the applicability of the algorithm, we apply it to a special family of multiplicative utility functions, the Cobb-Douglas family of utility functions, and a closely related family of utility functions, the Constant Elasticity of Substitution family of utility functions, both of which are frequently used utility functions in microeconomics. After a further study of the query properties, we propose a heuristic algorithm that produces even smaller regret ratios in practice. Extensive experiments on the proposed algorithms confirm that they consistently achieve small maximum regret ratios.
Limits on the number of satisfying assignments for CNS instances with n variables and m clauses are derived from various inequalities. Some bounds can be calculated in polynomial time, sharper bounds demand information about the distribution of the number of unsatisfied clauses. Quite generally, the number of satisfying assignments involve variance and mean of this distribution. For large formulae, m>>1, bounds vary with 2**n/n, so they may be of use only for instances with a large number of satisfying assignments.
For Boolean satisfiability problems, the structure of the solution space is characterized by the solution graph, where the vertices are the solutions, and two solutions are connected iff they differ in exactly one variable. For this implicitly defined graph, we here study the st-connectivity and connectivity problems. Building on the work of Gopalan et al. (The Connectivity of Boolean Satisfiability: Computational and Structural Dichotomies, 2006/2009), we first investigate satisfiability problems given by CSPs, more exactly CNF(S)-formulas with constants (as considered in Schaefers famous 1978 dichotomy theorem); we prove a computational dichotomy for the st-connectivity problem, asserting that it is either solvable in polynomial time or PSPACE-complete, and an aligned structural dichotomy, asserting that the maximal diameter of connected components is either linear in the number of variables, or can be exponential; further, we show a trichotomy for the connectivity problem, asserting that it is either in P, coNP-complete, or PSPACE-complete. Next we investigate two important variants: CNF(S)-formulas without constants, and partially quantified formulas; in both cases, we prove analogous dichotomies for st-connectivity and the diameter; for for the connectivity problem, we show a trichotomy in the case of quantified formulas, while in the case of formulas without constants, we identify fragments of a possible trichotomy. Finally, we consider the connectivity issues for B-formulas, which are arbitrarily nested formulas built from some fixed set B of connectives, and for B-circuits, which are Boolean circuits where the gates are from some finite set B; we prove a common dichotomy for both connectivity problems and the diameter; for partially quantified B-formulas, we show an analogous dichotomy.
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