On uncertainty versus size in branching programs

We propose an information-theoretic approach to proving lower bounds on the size of branching programs. The argument is based on Kraft type inequalities for the average amount of uncertainty about (or entropy of) a given input during the various stages of computation. The uncertainty is measured by the average depth of so-called ‘splitting trees’ for sets of inputs reaching particular nodes of the program.

We first demonstrate the approach for read-once branching programs. Then, we introduce a strictly larger class of so-called ‘balanced’ branching programs and, using the suggested approach, prove that some explicit Boolean functions cannot be computed by balanced programs of polynomial size. These lower bounds are new since some explicit functions, which are known to be hard for most previously considered restricted classes of branching programs, can be easily computed by balanced branching programs of polynomial size.     

A very simple function that requires exponential size nondeterministic graph-driven read-once branching programs

Branching programs are a well-established computation model for Boolean functions, especially read-once branching programs (BP1s) have been studied intensively. A very simple function f in n² variables is exhibited such that both the function f and its negation ¬f can be computed by Σ³_p-circuits, the function f has nondeterministic BP1s (with one nondeterministic node) of linear size and ¬f has size O(n⁴) for oblivious nondeterministic BP1s but f requires nondeterministic graph-driven BP1s of size . This answers an open question stated by Jukna, Razborov, Savický, and Wegener [Comput. Complexity 8 (1999) 357-370].     

The optimal read-once branching program complexity for the direct storage access function

Branching programs are computation models measuring the space of (Turing machine) computations. Read-once branching programs (BP1s) are the most general model where each graph-theoretical path is the computation path for some input. Exponential lower bounds on the size of read-once branching programs are known since a long time. Nevertheless, there are only few functions where the BP1 size is asymptotically known exactly. In this paper, the exact BP1 size of a fundamental function, the direct storage access function, is determined.     

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.     

Functions that have read-once branching programs of quadratic size are not necessarily testable

A property of binary strings is constructed that has a representation by a collection of read-once branching programs of quadratic size but which is not ε-testable for some fixed ε>0. This shows that Newman's result [Proc. 41st FOCS, 2000, pp. 251-258] cannot be generalized to functions representable by read-once branching programs of polynomial size.     

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].     

A hierarchy result for read-once branching programs with restricted parity nondeterminism

Restricted branching programs are considered in complexity theory in order to study the space complexity of sequential computations and in applications as a data structure for Boolean functions. In this paper (,k)-branching programs and (,k)-branching programs are considered, i.e., branching programs starting with a - (or -)node with a fan-out of k whose successors are k read-once branching programs. This model is motivated by the investigation of the power of nondeterminism in branching programs and of similar variants that have been considered as a data structure. Lower bound methods and hierarchy results for polynomial size (,k)- and (,k)-branching programs with respect to k are presented.     

A note on the decoding complexity of error-correcting codes

A time-space tradeoff lower bound for the decoding complexity of asymptotically good error-correcting codes for oblivious write-k-times branching programs is proved. Specifically, we prove that the computation time T and space S of every oblivious write-k-times branching program that decodes an asymptotically good error-correcting code with block length n satisfy S⋅Tk=Ω((n/k)^k+1).     

On P versus NP $ cap $ co-NP for decision trees and read-once branching programs

It is known that if a Boolean function f in n variables has a DNF and a CNF of size then f also has a (deterministic) decision tree of size exp(O(log n log² N)). We show that this simulation cannot be made polynomial: we exhibit explicit Boolean functions f that require deterministic trees of size exp where N is the total number of monomials in minimal DNFs for f and ?f. Moreover, we exhibit new examples of explicit Boolean functions that require deterministic read-once branching programs of exponential size whereas both the functions and their negations have small nondeterministic read-once branching programs. One example results from the Bruen—Blokhuis bound on the size of nontrivial blocking sets in projective planes: it is remarkably simple and combinatorially clear. Other examples have the additional property that f is in AC⁰. Received: June 5 1997.     

A large lower bound on the query complexity of a simple boolean function

Combinatorial property testing, initiated formally by Goldreich, Goldwasser, and Ron (1998) and inspired by Rubinfeld and Sudan (1996), deals with the relaxation of decision problems. Given a property P the aim is to decide whether a given input satisfies the property P or is far from having the property. For a family of boolean functions f=(fn) the associated property is the set of 1-inputs of f. Here, the known lower bounds on the query complexity of properties identified by boolean functions representable by (very) restricted branching programs of small size is improved up to Ω(n1/2), where n is the input length.     

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.     

On the size of (generalized) OBDDs for threshold functions

Ordered binary decision diagrams (OBDDs) are one of the most common dynamic data structures for Boolean functions. Among the many areas of application are hardware verification, model checking, and symbolic graph algorithms. Threshold functions are the basic functions for discrete neural networks and are used as building blocks in the design of some symbolic graph algorithms. In this paper the first exponential lower bound on the size of a more general model than OBDDs and the first nontrivial asymptotically optimal bound on the OBDD size for a threshold function are presented.     

On the size of randomized OBDDs and read-once branching programs for k-stable functions

In this paper, a simple technique which unifies the known approaches for proving lower bound results on the size of deterministic, nondeterministic, and randomized OBDDs and kOBDDs is described.?As an application of this technique, a generic lower bound on the size of randomized OBDDs with bounded error is established for a class of functions which has been studied in the literature on branching programs for a long time. These functions have been called “k-stable” by Jukna. It follows that several standard functions are not contained in the analog of the class BPP for OBDDs. Furthermore, exponential lower bounds on the size of randomized kOBDDs are presented.?It is well known that k-stable functions with large k are hard for deterministic read-once branching programs. This is no longer true in the randomized case. It is shown here that a certain k-stable function due to Jukna, Razborov, Savicky, and Wegener has randomized branching programs of polynomial size, even with zero error. It follows that for the analogs of these classes defined in terms of the size of read-once branching programs. Received: September 3, 1998.     

On the OBDD complexity of the most significant bit of integer multiplication

Integer multiplication as one of the basic arithmetic functions has been in the focus of several complexity theoretical investigations. Ordered binary decision diagrams (OBDDs) are one of the most common dynamic data structures for boolean functions. Among the many areas of application are verification, model checking, computer-aided design, relational algebra, and symbolic graph algorithms. In this paper it is shown that the OBDD complexity of the most significant bit of integer multiplication is exponential answering an open question posed by Wegener (2000) [18].     

Larger lower bounds on the OBDD complexity of integer multiplication

Integer multiplication as one of the basic arithmetic functions has been in the focus of several complexity theoretical investigations and ordered binary decision diagrams (OBDDs) are one of the most common dynamic data structures for Boolean functions. Recently, the question whether the OBDD complexity of the most significant bit of integer multiplication is exponential has been answered affirmatively. In this paper a larger general lower bound is presented using a simpler proof. Furthermore, we prove a larger lower bound for the variable order assumed to be one of the best ones for the most significant bit. Moreover, the best known lower bound on the OBDD complexity for the so-called graph of integer multiplication is improved.     

A note on the size of OBDDs for the graph of integer multiplication

Integer multiplication as one of the basic arithmetic functions has been in the focus of several complexity theoretical investigations and ordered binary decision diagrams (OBDDs) are the most common dynamic data structure for Boolean functions. Among the many areas of application are verification, model checking, computer-aided design, relational algebra, and symbolic graph algorithms. Analyzing the limits of symbolic graph algorithms for the all-pairs-shortest paths problem which work on OBDD-represented graph instances the so-called graph of integer multiplication has been investigated by Sawitzki [D. Sawitzki, Lower bounds on the OBDD size of graphs of some popular functions, in: Proc. of SOFSEM, LNCS, vol. 3381, 2005, pp. 298-309]. Using simple arguments his lower bound of ²n/768−1 on the size of OBDDs representing the graph of integer multiplication is improved up to ²n/24.     

A nonlinear lower bound for constant depth arithmetical circuits via the discrete uncertainty principle

We prove a superlinear lower bound on the size of a bounded depth bilinear arithmetical circuit computing cyclic convolution. Our proof uses the strengthening of the Donoho–Stark uncertainty principle [D.L. Donoho, P.B. Stark, Uncertainty principles and signal recovery, SIAM Journal of Applied Mathematics 49 (1989) 906–931] given by Tao [T. Tao, An uncertainty principle for cyclic groups of prime order, Mathematical Research Letters 12 (2005) 121–127], and a combinatorial lemma by Raz and Shpilka [R. Raz, A. Shpilka, Lower bounds for matrix product, in arbitrary circuits with bounded gates, SIAM Journal of Computing 32 (2003) 488–513]. This combination and an observation on ranks of circulant matrices, which we use to give a much shorter proof of the Donoho–Stark principle, may have other applications.     

Lower bounds for decision problems in imaginary,norm-Euclidean quadratic integer rings

We prove lower bounds for the complexity of deciding several relations in imaginary, norm-Euclidean quadratic integer rings, where computations are assumed to be relative to a basis of piecewise-linear operations. In particular, we establish lower bounds for deciding coprimality in these rings, which yield lower bounds for gcd computations. In each imaginary, norm-Euclidean quadratic integer ring, a known binary-like gcd algorithm has complexity that is quadratic in our lower bound.