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

The complexity of a multiprocessor task assignment problem without deadlines

We construct a sequence of monotone Boolean functions h_n :{0, 1}ⁿ→{0, 1}ⁿ, such that the monotone complexity of h_n is of order $\text{n}{}^2\text{log n}$. This result includes the largest known lower bound of this kind. Previously there were an $\text{Ω(n}{}^{\mathrm{<mtext>3</mtext><mtext>2</mtext>}}\text{)}$ bound for the Boolean matrix product, an $\text{Ω(n}{}^{\mathrm{<mtext>5</mtext><mtext>3</mtext>}}\text{)}$ bound for Boolean sums and an $\text{Ω(}\text{n}{}^2\text{log}{}^2\text{n}\text{)}$ bound by the author for the same functions h_n. This new lower bound is proved by new methods which probably will turn out to be useful also for other problems.     

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.     

Switching functions whose monotone complexity is nearly quadratic

A sequence of monotone switching functions h_n:{0,1}ⁿ→ {0,1}ⁿ is constructed, such that the monotone complexity of h_n grows faster than Ω(n² log^?2n). Previously the best lower bounds of this nature were several Ω(n^{$\text{3}\text{2}$} bounds due to Pratt, Paterson, Mehlhorn and Galil and Savage.     

Realizing Boolean functions on disjoint sets of variables

For switching functions f let C(f) be the combinational complexity of f. We prove that for every ε>0 there are arbitrarily complex functions f:{0,1}ⁿ→{0,1}ⁿ such that C(f×f)? (1+ε)C(f) and arbitrarily complex functions f:{0,1}ⁿ→{0,1} such that C(v°(fxf)? (1+ε)C(f). These results and the techniques developed to obtain them are used to show that Ashenhurst decomposition of switching functions does not always yield optimal circuits, and to prove a new result concerning the gap between circuit size and monotone circuit size.     

Bounded-Depth Circuits Cannot Sample Good Codes

We study a variant of the classical circuit-lower-bound problems: proving lower bounds for sampling distributions given random bits. We prove a lower bound of 1 ? 1/n ^??(1) on the statistical distance between (i) the output distribution of any small constant-depth (a.k.a. AC⁰) circuit f : {0, 1}^poly(n) ?? {0, 1} ⁿ , and (ii) the uniform distribution over any code ${\mathcal{C} \subseteq \{0, 1\}^n}$ that is ??good,?? that is, has relative distance and rate both ??(1). This seems to be the first lower bound of this kind. We give two simple applications of this result: (1) any data structure for storing codewords of a good code ${\mathcal{C} \subseteq \{0, 1\}^n}$ requires redundancy ??(log n), if each bit of the codeword can be retrieved by a small AC⁰ circuit; and (2) for some choice of the underlying combinatorial designs, the output distribution of Nisan??s pseudorandom generator against AC⁰ circuits of depth d cannot be sampled by small AC⁰ circuits of depth less than d.     

The addition of bounded quantification and partial functions to a computational logic and its theorem prover

We describe an extension to our quantifier-free computational logic to provide the expressive power and convenience of bounded quantifiers and partial functions. By quantifier we mean a formal construct which introduces a bound or indicial variable whose scope is some subexpression of the quantifier expression. A familiar quantifier is the Σ operator which sums the values of an expression over some range of values on the bound variable. Our method is to represent expressions of the logic as objects in the logic, to define an interpreter for such expressions as a function in the logic, and then define quantifiers as ‘mapping functions’. The novelty of our approach lies in the formalization of the interpreter and its interaction with the underlying logic. Our method has several advantages over other formal systems that provide quantifiers and partial functions in a logical setting. The most important advantage is that proofs not involving quantification or partial recursive functions are not complicated by such notions as ‘capturing’, ‘bottom’, or ‘continuity’. Naturally enough, our formalization of the partial functions is nonconstructive. The theorem prover for the logic has been modified to support these new features. We describe the modifications. The system has proved many theorems that could not previously be stated in our logic. Among them are:

? classic quantifier manipulation theorems, such as $$\sum\limits_{{\text{l}} = 0}^{\text{n}} {{\text{g}}({\text{l}}) + {\text{h(l) = }}} \sum\limits_{{\text{l = }}0}^{\text{n}} {{\text{g}}({\text{l}})} + \sum\limits_{{\text{l = }}0}^{\text{n}} {{\text{h(l)}};} $$

? elementary theorems involving quantifiers, such as the Binomial Theorem: $$(a + b)^{\text{n}} = \sum\limits_{{\text{l = }}0}^{\text{n}} {\left( {_{\text{i}}^{\text{n}} } \right)} \user2{ }{\text{a}}^{\text{l}} {\text{b}}^{{\text{n - l}}} ;$$

? elementary theorems about ‘mapping functions’ such as: $$(FOLDR\user2{ }'PLUS\user2{ O L) = }\sum\limits_{{\text{i}} \in {\text{L}}}^{} {{\text{i}};} $$

? termination properties of many partial recursive functions such as the fact that an application of the partial function described by $$\begin{gathered} (LEN X) \hfill \\ \Leftarrow \hfill \\ ({\rm I}F ({\rm E}QUAL X NIL) \hfill \\ {\rm O} \hfill \\ (ADD1 (LEN (CDR X)))) \hfill \\ \end{gathered} $$ terminates if and only if the argument ends in NIL;

? theorems about functions satisfying unusual recurrence equations such as the 91-function and the following list reverse function: $$\begin{gathered} (RV X) \hfill \\ \Leftarrow \hfill \\ ({\rm I}F (AND (LISTP X) (LISTP (CDR X))) \hfill \\ (CONS (CAR (RV (CDR X))) \hfill \\ (RV (CONS (CAR X) \hfill \\ (RV (CDR (RV (CDR X))))))) \hfill \\ X). \hfill \\ \end{gathered} $$

     

A nondeterministic space-time tradeoff for linear codes

We are interested in proving exponential lower bounds on the size of nondeterministic D-way branching programs computing functions in linear time, that is, in time at most kn for a constant k. Ajtai has proved such lower bounds for explicit functions over domains D of size about n, and Beame, Saks and Thathachar for functions over domains of size about k²². We prove an exponential lower bound ²Ω(n/ck) for an explicit function over substantially smaller domain D of size about k². Our function is a universal function of linear codes.     

Soluzione delle equazioni della cinetica di un reattore nucleare per mezzo di approssimazioni successive di tipo monotono

The solution of the one-energy group space-independent reactor kinetics equations is obtained in the form of the limit of two monotone bounded sequences of functions {N _j ^?} and {N _j ⁺}, non decreasing and non increasing respectively, defined as $$\begin{gathered} N_{j + 1}^ - = T_1 N_j^ + + T_2 N_j^ - + f \hfill \\ N_{j + 1}^ + = T_1 N_j^ - + T_2 N_j^ + + f \hfill \\ \end{gathered} $$ whereT ₁ andT ₂ are monotone-type operators, precisely antitone and isotone. In this work a procedure for obtaining the two initial elements,N ₀ ^? andN ₀ ⁺, satisfying the required properties to assure the convergence of the two sequences {N _j ^?} and {N _j ⁺}, is described; moreover, it is proved that the two sequences converge uniformely to the same limit. In addition, some numerical results are presented.     

Lower Bounds for Agnostic Learning via Approximate Rank

We prove that the concept class of disjunctions cannot be pointwise approximated by linear combinations of any small set of arbitrary real-valued functions. That is, suppose that there exist functions f₁, ?, f_r\phi_{1}, \ldots , \phi_{r} : {− 1, 1}ⁿ → \mathbbR{\mathbb{R}} with the property that every disjunction f on n variables has $\|f - \sum\nolimits_{i=1}^{r} \alpha_{i}\phi _{i}\|_{\infty}\leq 1/3$\|f - \sum\nolimits_{i=1}^{r} \alpha_{i}\phi _{i}\|_{\infty}\leq 1/3 for some reals a₁, ?, a_r\alpha_{1}, \ldots , \alpha_{r}. We prove that then $r \geq exp \{\Omega(\sqrt{n})\}$r \geq exp \{\Omega(\sqrt{n})\}, which is tight. We prove an incomparable lower bound for the concept class of decision lists. For the concept class of majority functions, we obtain a lower bound of W(2ⁿ/n)\Omega(2^{n}/n) , which almost meets the trivial upper bound of 2ⁿ for any concept class. These lower bounds substantially strengthen and generalize the polynomial approximation lower bounds of Paturi (1992) and show that the regression-based agnostic learning algorithm of Kalai et al. (2005) is optimal.     

On the Gap Between Trivial and Nontrivial Initial Segment Prefix-Free Complexity

An infinite sequence X is said to have trivial (prefix-free) initial segment complexity if the prefix-free Kolmogorov complexity of each initial segment of X is the same as the complexity of the sequence of 0s of the same length, up to a constant. We study the gap between the minimum complexity K(0 ⁿ ) and the initial segment complexity of a nontrivial sequence, and in particular the nondecreasing unbounded functions f such that ? for a nontrivial sequence X, where K denotes the prefix-free complexity. Our first result is that there exists a $\varDelta^{0}_{3}$ unbounded nondecreasing function f which does not have this property. It is known that such functions cannot be $\varDelta^{0}_{2}$ hence this is an optimal bound on their arithmetical complexity. Moreover it improves the bound $\varDelta^{0}_{4}$ that was known from Csima and Montalbán (Proc. Amer. Math. Soc. 134(5):1499?C1502, 2006). Our second result is that if f is $\varDelta^{0}_{2}$ then there exists a non-empty $\varPi^{0}_{1}$ class of reals X with nontrivial prefix-free complexity which satisfy (?). This implies that in this case there uncountably many nontrivial reals X satisfying (?) in various well known classes from computability theory and algorithmic randomness; for example low for ??, non-low for ?? and computably dominated reals. A special case of this result was independently obtained by Bienvenu, Merkle and Nies (STACS, pp. 452?C463, 2011).     

A Sigma-Pi-Sigma Neural Network (SPSNN)

This letter presents a sigma-pi-sigma neural network (SPSNN) structure. The SPSNN can learn to implement static mapping that multilayer neural networks and radial basis function networks usually do. The output of the SPSNN has the sum of product-of-sum form , where x _j's are inputs, N _v is the number of inputs, f _nij() is a function to be generated through the network training, and K is the number of pi-sigma network (PSN) which is the basic building block for SPSNN. A linear memory array can be used to implement f _nij (). The function f _nij (x _j ) can be expressed as , where B _ijk() is a single-variable basis function, w _nijk's are weight values stored in memory, N _q is the quantized element number for x _j , and N _e is the number of basis functions in the neighborhood used for storing information for x _j. If all B _ijk()'s are Gaussian functions, the new neural network degenerates to a Gaussian function network. This paper focuses on the use of overlapped rectangular pulses as the basis functions. With such basis functions, will equal either zero or w _nijk, and the computation of f _nij (x _j) becomes a simple addition of retrieved w _nijk's. The new neural network structure demonstrates excellent learning convergence characteristics and requires small memory space. It has merits over multilayer neural networks, radial basis function networks and CMAC. This revised version was published online in June 2006 with corrections to the Cover Date.     

On the Incompressibility of Monotone DNFs

We prove optimal lower bounds for multilinear circuits and for monotone circuits with bounded depth. These lower bounds state that, in order to compute certain functions, these circuits need exactly as many OR gates as the respective DNFs. The proofs exploit a property of the functions that is based solely on prime implicant structure. Due to this feature, the lower bounds proved also hold for approximations of the considered functions that are similar to slice functions. Known lower bound arguments cannot handle these kinds of approximations. In order to show limitations of our approach, we prove that cliques of size n - 1 can be detected in a graph with n vertices by monotone formulas with O(log n) OR gates. Our lower bound for multilinear circuits improves a lower bound due to Borodin, Razborov and Smolensky for nondeterministic read-once branching programs computing the clique function.     

Unconditional lower bounds for learning intersections of halfspaces

We prove new lower bounds for learning intersections of halfspaces, one of the most important concept classes in computational learning theory. Our main result is that any statistical-query algorithm for learning the intersection of $\sqrt{n}$ halfspaces in n dimensions must make $2^{\varOmega (\sqrt{n})}$ queries. This is the first non-trivial lower bound on the statistical query dimension for this concept class (the previous best lower bound was n ^Ω(log?n)). Our lower bound holds even for intersections of low-weight halfspaces. In the latter case, it is nearly tight. We also show that the intersection of two majorities (low-weight halfspaces) cannot be computed by a polynomial threshold function (PTF) with fewer than n ^{Ω(log?n/log?log?n)} monomials. This is the first super-polynomial lower bound on the PTF length of this concept class, and is nearly optimal. For intersections of k=ω(log?n) low-weight halfspaces, we improve our lower bound to $\min\{2^{\varOmega (\sqrt{n})},n^{\varOmega (k/\log k)}\},$ which too is nearly optimal. As a consequence, intersections of even two halfspaces are not computable by polynomial-weight PTFs, the most expressive class of functions known to be efficiently learnable via Jackson’s Harmonic Sieve algorithm. Finally, we report our progress on the weak learnability of intersections of halfspaces under the uniform distribution.     

An extension theorem for arcs and linear codes

We prove the following generalization to the extension theorem of Hill and Lizak: For every nonextendable linear [n, k, d] _q code, q = p ^s , (d,q) = 1, we have $\sum\limits_{i\not \equiv 0,d(\bmod q)} {A_i > q^{k - 3} r(q),} $ where q + r(q) + 1 is the smallest size of a nontrivial blocking set in PG(2, q). This result is applied further to rule out the existence of some linear codes over $\mathbb{F}_4 $ meeting the Griesmer bound.     

Runtime analysis of a binary particle swarm optimizer

We investigate the runtime of a binary Particle Swarm Optimizer (PSO) for optimizing pseudo-Boolean functions f:{0,1}ⁿ→R. The binary PSO maintains a swarm of particles searching for good solutions. Each particle consists of a current position from {0,1}ⁿ, its own best position and a velocity vector used in a probabilistic process to update its current position. The velocities for a particle are then updated in the direction of its own best position and the position of the best particle in the swarm.We present a lower bound for the time needed to optimize any pseudo-Boolean function with a unique optimum. To prove upper bounds we transfer a fitness-level argument that is well-established for evolutionary algorithms (EAs) to PSO. This method is applied to estimate the expected runtime for the class of unimodal functions. A simple variant of the binary PSO is considered in more detail for the test function OneMax, showing that there the binary PSO is competitive to EAs. An additional experimental comparison reveals further insights.     

On the performance of Dijkstra’s third self-stabilizing algorithm for mutual exclusion and related algorithms

In Dijkstra (Commun ACM 17(11):643–644, 1974) introduced the notion of self-stabilizing algorithms and presented three such algorithms for the problem of mutual exclusion on a ring of n processors. The third algorithm is the most interesting of these three but is rather non intuitive. In Dijkstra (Distrib Comput 1:5–6, 1986) a proof of its correctness was presented, but the question of determining its worst case complexity—that is, providing an upper bound on the number of moves of this algorithm until it stabilizes—remained open. In this paper we solve this question and prove an upper bound of 3\frac1318 n² + O(n){3\frac{13}{18} n^2 + O(n)} for the complexity of this algorithm. We also show a lower bound of 1\frac56 n² - O(n){1\frac{5}{6} n^2 - O(n)} for the worst case complexity. For computing the upper bound, we use two techniques: potential functions and amortized analysis. We also present a new-three state self-stabilizing algorithm for mutual exclusion and show a tight bound of \frac56 n² + O(n){\frac{5}{6} n^2 + O(n)} for the worst case complexity of this algorithm. In Beauquier and Debas (Proceedings of the second workshop on self-stabilizing systems, pp 17.1–17.13, 1995) presented a similar three-state algorithm, with an upper bound of 5\frac34n²+O(n){5\frac{3}{4}n^2+O(n)} and a lower bound of \frac18n²-O(n){\frac{1}{8}n^2-O(n)} for its stabilization time. For this algorithm we prove an upper bound of 1\frac12n² + O(n){1\frac{1}{2}n^2 + O(n)} and show a lower bound of n ²−O(n). As far as the worst case performance is considered, the algorithm in Beauquier and Debas (Proceedings of the second workshop on self-stabilizing systems, pp 17.1–17.13, 1995) is better than the one in Dijkstra (Commun ACM 17(11):643–644, 1974) and our algorithm is better than both.     

Size-depth tradeoff in monotone Boolean formulae

Summary Formula size and depth are two important complexity measures of Boolean functions. We study the tradeoff between those two measures: We give an infinite set of Boolean functions and show for nearly each of them: There is no monotone formula computing it optimal with respect to both measures. We give a lower and upper bound on the product of size and depth of monotone formulae computing our functions. That implies, moreover, a logarithmic lower bound on circuit depth.Denotations the set of natural numbers {1,2,...} - for x>0, x =max{y{0}¦y¦<=x} - log logarithm to the base 2     

Pseudorandom functions in \textit{TC}^{0} and cryptographic limitations to proving lower bounds

This paper investigates which complexity classes inside NC can contain pseudorandom function generators (PRFGs). Under the Decisional Diffie-Hellman assumption (a common cryptographic assumption) TC⁰ \textit{TC}^{0} 4 contains PRFGs. No lower complexity classes with this property are currently known. On the other hand, we use effective lower bound arguments to show that some complexity classes cannot contain PRFGs. This provides evidence for the following conjecture: Any effective lower bound argument for a complexity class can be turned into an efficient distinguishing algorithm which proves that this class cannot