Lower bounds for monotone span programs

Span programs provide a linear algebraic model of computation. Lower bounds for span programs imply lower bounds for formula size, symmetric branching programs, and contact schemes. Monotone span programs correspond also to linear secret-sharing schemes. We present a new technique for proving lower bounds for monotone span programs. We prove a lower bound of (m ^2.5) for the 6-clique function. Our results improve on the previously known bounds for explicit functions.     

Circuit and Decision Tree Complexity of Some Number Theoretic Problems

We extend the area of applications of the Abstract Harmonic Analysis to lower bounds on the circuit and decision tree complexity of Boolean functions related to some number theoretic problems. In particular, we prove that deciding if a given integer is square-free and testing co-primality of two integers by unbounded fan-in circuits of bounded depth requires superpolynomial size.     

Graph Complexity and Slice Functions

Abstract. A graph-theoretic approach to study the complexity of Boolean functions was initiated by Pudlák, Rödl, and Savický [PRS] by defining models of computation on graphs. These models generalize well-known models of Boolean complexity such as circuits, branching programs, and two-party communication complexity. A Boolean function f is called a 2-slice function if it evaluates to zero on inputs with less than two 1's and evaluates to one on inputs with more than two 1's. On inputs with exactly two 1's f may be nontrivially defined. There is a natural correspondence between 2-slice functions and graphs. Using the framework of graph complexity, we show that sufficiently strong superlinear monotone lower bounds for the very special class of {2-slice functions} would imply superpolynomial lower bounds over a complete basis for certain functions derived from them. We prove, for instance, that a lower bound of n ^1+Ω(1) on the (monotone) formula size of an explicit 2-slice function f on n variables would imply a 2 ^Ω(?) lower bound on the formula size over a complete basis of another explicit function g on l variables, where l=Θ( log n) . We also consider lower bound questions for depth-3 bipartite graph complexity. We prove a weak lower bound on this measure using algebraic methods. For instance, our result gives a lower bound of Ω(( log n) ³ / ( log log n) ⁵ ) for bipartite graphs arising from Hadamard matrices, such as the Paley-type bipartite graphs. Lower bounds for depth-3 bipartite graph complexity are motivated by two significant applications: (i) a lower bound of n ^Ω(1) on the depth-3 complexity of an explicit n -vertex bipartite graph would yield superlinear size lower bounds on log-depth Boolean circuits for an explicit function, and (ii) a lower bound of $\exp((\log \log n)^{\omega(1)})$ would give an explicit language outside the class Σ ₂ ^cc of the two-party communication complexity as defined by Babai, Frankl, and Simon [BFS]. Our lower bound proof is based on sign-representing polynomials for DNFs and lower bounds on ranks of ±1 matrices even after being subjected to sign-preserving changes to their entries. For the former, we use a result of Nisan and Szegedy [NS] and an idea from a recent result of Klivans and Servedio [KS]. For the latter, we use a recent remarkable lower bound due to Forster [F1].     

Graph Complexity and Slice Functions

   Abstract. A graph-theoretic approach to study the complexity of Boolean functions was initiated by Pudlák, R?dl, and Savicky [PRS] by defining models of computation on graphs. These models generalize well-known models of Boolean complexity such as circuits, branching programs, and two-party communication complexity. A Boolean function f is called a 2-slice function if it evaluates to zero on inputs with less than two 1's and evaluates to one on inputs with more than two 1's. On inputs with exactly two 1's f may be nontrivially defined. There is a natural correspondence between 2-slice functions and graphs. Using the framework of graph complexity, we show that sufficiently strong superlinear monotone lower bounds for the very special class of {2-slice functions} would imply superpolynomial lower bounds over a complete basis for certain functions derived from them. We prove, for instance, that a lower bound of n ^1+Ω(1) on the (monotone) formula size of an explicit 2-slice function f on n variables would imply a 2 ^Ω(ℓ) lower bound on the formula size over a complete basis of another explicit function g on l variables, where l=Θ( log n) . We also consider lower bound questions for depth-3 bipartite graph complexity. We prove a weak lower bound on this measure using algebraic methods. For instance, our result gives a lower bound of Ω(( log n) ³ / ( log log n) ⁵ ) for bipartite graphs arising from Hadamard matrices, such as the Paley-type bipartite graphs. Lower bounds for depth-3 bipartite graph complexity are motivated by two significant applications: (i) a lower bound of n ^Ω(1) on the depth-3 complexity of an explicit n -vertex bipartite graph would yield superlinear size lower bounds on log-depth Boolean circuits for an explicit function, and (ii) a lower bound of

Which bases admit non-trivial shrinkage of formulae?

We show that the shrinkage exponent, under random restrictions, of formulae over a finite complete basis B of Boolean functions, is strictly greater than 1 if and only if all the functions in B are unate, i.e., monotone increasing or decreasing in each of their variables. As a consequence, we get non-linear lower bounds on the formula complexity of the parity function over any basis composed only of unate functions. Received: June 15, 2000.     

The Monotone Circuit Complexity of Quadratic Boolean Functions

Several results on the monotone circuit complexity and the conjunctive complexity, i.e., the minimal number of AND gates in monotone circuits, of quadratic Boolean functions are proved. We focus on the comparison between single level circuits, which have only one level of AND gates, and arbitrary monotone circuits, and show that there is an exponential gap between the conjunctive complexity of single level circuits and that of general monotone circuits for some explicit quadratic function. Nearly tight upper bounds on the largest gap between the single level conjunctive complexity and the general conjunctive complexity over all quadratic functions are also proved. Moreover, we describe the way of lower bounding the single level circuit complexity and give a set of quadratic functions whose monotone complexity is strictly smaller than its single level complexity.     

Property Testing Lower Bounds via Communication Complexity

We develop a new technique for proving lower bounds in property testing, by showing a strong connection between testing and communication complexity. We give a simple scheme for reducing communication problems to testing problems, thus allowing us to use known lower bounds in communication complexity to prove lower bounds in testing. This scheme is general and implies a number of new testing bounds, as well as simpler proofs of several known bounds. For the problem of testing whether a Boolean function is k-linear (a parity function on k variables), we achieve a lower bound of ??(k) queries, even for adaptive algorithms with two-sided error, thus confirming a conjecture of Goldreich (2010a). The same argument behind this lower bound also implies a new proof of known lower bounds for testing related classes such as k-juntas. For some classes, such as the class of monotone functions and the class of s-sparse GF(2) polynomials, we significantly strengthen the best known bounds.     

A note on monotone complexity and the rank of matrices

We shall give simpler proofs of some lower bounds on monotone computations. We describe a simple condition on combinatorial structures, such that the rank of the matrix associated with these structures gives lower bounds on monotone span program size and monotone formula size. We also prove an upper bound on the rank of the corresponding matrices, and show that such structures can be constructed from self-avoiding families. As a corollary, we obtain an upper bound on the size of self-avoiding families, which solves a problem posed by Babai and Gál [Combinatorica 19 (3) (1999) 301-319].     

Malign Distributions for Average Case Circuit Complexity

In contrast to machine models like Turing machines or random access machines, circuits are a static computational model. The internal information flow of a computation is fixed in advance, independent of the actual input. Therefore, size and depth are natural and simple measures for circuits and provide a worst-case analysis. We consider a new model in which an internal gate is evaluated as soon as its result has been determined by a partial assignment of its inputs. This way, a dynamic notion of delay is obtained which gives rise to an average case measure for the time complexity of circuits. In a previous paper we have obtained tight upper and lower bounds for the average case complexity of several basic Boolean functions. This paper examines the asymptotic average case complexity for the set of alln-ary Boolean functions. In contrast to worst case analysis a simple counting argument does not work. We prove that with respect to the uniform probability distribution almost all Boolean functions require at leastn−log n−log log nexpected time. On the other hand, there is a significantly large subset of functions that can be computed with a constant average delay. Finally, for an arbitrary Boolean function we compare its worst case and average case complexity. It is shown that for each function that requires circuit depthd, i.e. of worst-case complexityd, the expected time complexity will be at leastd−log n−log dwith respect to an explicitly defined probability distribution. In addition, a nontrivial upper bound on the complexity of such a distribution will be obtained.     

A new lower bound on the monotone network complexity of Boolean sums

Summary Neciporuk [3], Lamagna/Savage [1] and Tarjan [6] determined the monotone network complexity of a set of Boolean sums if each two sums have at most one variable in common. By this result they could define explicitely a set of n Boolean sums which depend on n variables and whose monotone complexity is of order n ^3/2. In the main theorem of this paper we prove a more general lower bound on the monotone network complexity of Boolean sums. Our lower bound is for many Boolean sums the first nontrivial lower bound. On the other side we can prove that the best lower bound which the main theorem yields is the n ^3/2-bound cited above. For the proof we use the technical trick of assuming that certain functions are given for free.     

Symmetric approximation arguments for monotone lower bounds without sunflowers

We propose a symmetric version of Razborov's method of approximation to prove lower bounds for monotone circuit complexity. Traditionally, only DNF formulas have been used as approximators, whereas we use both CNF and DNF formulas. As a consequence we no longer need the Sun ower Lemma that has been essential for the method of approximation. The new approximation argument corresponds to Haken's recent method for proving lower bounds for monotone circuit complexity (counting bottlenecks) in a natural way.?We provide lower bounds for the BMS problem introduced by Haken, Andreev's polynomial problem, and for Clique. The exponential bounds obtained are the same as those previously best known for the respective problems. Received: July 16, 1996.     

Exponential lower bounds for the pigeonhole principle

In this paper we prove an exponential lower bound on the size of bounded-depth Frege proofs for the pigeonhole principle (PHP). We also obtain an (loglogn)-depth lower bound for any polynomial-sized Frege proof of the pigeonhole principle. Our theorem nearly completes the search for the exact complexity of the PHP, as S. Buss has constructed polynomial-size, logn-depth Frege proofs for the PHP. The main lemma in our proof can be viewed as a general Håstad-style Switching Lemma for restrictions that are partial matchings. Our lower bounds for the pigeonhole principle improve on previous superpolynomial lower bounds.     

Communication complexity towards lower bounds on circuit depth

Karchmer, Raz, and Wigderson (1995) discuss the circuit depth complexity of n-bit Boolean functions constructed by composing up to d = log n/log log n levels of k = log n-bit Boolean functions. Any such function is in AC¹ . They conjecture that circuit depth is additive under composition, which would imply that any (bounded fan-in) circuit for this problem requires depth. This would separate AC¹ from NC¹. They recommend using the communication game characterization of circuit depth. In order to develop techniques for using communication complexity to prove circuit depth lower bounds, they suggest an intermediate communication complexity problem which they call the Universal Composition Relation. We give an almost optimal lower bound of dk—O(d ²(k log k)^1/2) for this problem. In addition, we present a proof, directly in terms of communication complexity, that there is a function on k bits requiring circuit depth. Although this fact can be easily established using a counting argument, we hope that the ideas in our proof will be incorporated more easily into subsequent arguments which use communication complexity to prove circuit depth bounds. Received: July 30, 1999.     

The complexity of monotone boolean functions

We study the realization of monotone Boolean functions by networks. Our main result is a precise version of the following statement: the complexity of realizing a monotone Boolean function ofn arguments is less by the factor (2/n)^1/2, where is the circular ratio, than the complexity of realizing an arbitrary Boolean function ofn arguments. The proof combines known results concerning monotone Boolean functions with new methods relating the computing abilities of networks and machines.     

A 4n-lower bound on the monotone network complexity of a one-output boolean function

The main purpose of Boolean network theory is to find functions f:{0, 1}ⁿ → {0, 1} with large network complexity. The best known lower bound over the complete basis of all binary functions is of size 3n for a non-monotone function (Blum (1982)). Bloniarz (1979) has proved a 3n-lower bound for the majority-function over the monotone basis. In this paper a special function is presented for which a lower bound of size 4n over the monotone basis can be proved.     

Harmonic analysis and Boolean function complexity

Any attempt to find connections between mathematical properties of functions and their computational complexity has strong relevance to theory of computation. Indeed, there is the hope that developing new mathematical techniques could lead to discovering properties that might be responsible for lower bounds. The current situation is that none of the known techniques has yet led to lower bounds in general models of computation. The subject of this paper is related to the above general arguments. More precisely, we study the Fourier Transform of Boolean functions, and analyze the extent to which mathematical techniques from the area of abstract harmonic analysis can provide some insight in our current understanding of Boolean circuit complexity. In addition to presenting new applications of Fourier analysis to circuit complexity, we give the necessary background on abstract harmonic analysis and review some work on the subject. Received: March 1998 / Accepted: May 1998     

Width versus size in resolution proofs

A general theme in the proof of lower bounds on the size of resolution refutations in propositional logic has been that of basing size lower bounds on lower bounds on the width of refutations. Ben-Sasson and Wigderson have proved a general width-size tradeoff result that can be used to prove many of the lower bounds on resolution complexity in a uniform manner. However, it does not apply directly to the well known pigeonhole clauses. The present paper generalizes their width-size tradeoff so that it applies directly to (a monotone transformation of) the pigeonhole clauses.     

The equivalence of Horn and network complexity for Boolean functions

Summary We propose in this paper a logical complexity measure — Horn complexity — for Boolean functions which measures the minimal length of quasi-Horn definitions of such functions by propositional formulae. The interest for this complexity measure comes on the one hand from the observation that the satisfiability problem for Horn formulae is in P, on the other hand from a strong connection to Cook's problem. We show the proposed Horn complexity to be polynomially equivalent to network complexity and therefore to Turing complexity for Boolean functions.A preliminary version of this work has appeared in the Proceedings of the 5^th Scandinavian Logic Symposium, edited by B. Mayoh and F. Jensen, Aalborg University Press, Aalborg 1979     

Lower bounds on monotone arithmetic circuits with restricted depths

We consider monotone arithmetic circuits with restricted depths to compute monotone multivariate polynomials such as the elementary symmetric functions, convolution of several vectors and raising a matrix to a power. We develop general lower- and upper-bound techniques that seem to generate almost-matching bounds for all the functions considered. These results imply exponential lower bounds for circuits of bounded depths which compute any of these functions. We also obtain several examples for which negation can reduce the size exponentially.