A general theory is developed for constructing the asymptotically shallowest networks and the asymptotically smallest networks (with respect to formula size) for the carry save addition of n numbers using any given basic carry save adder as a building block. Using these optimal carry save addition networks the shallowest known multiplication circuits and the shortest formulae for the majority function (and many other symmetric Boolean functions) are obtained. In this paper, simple basic carry save adders are described, using which multiplication circuits of depth 3:71 log n (the result of which is given as the sum of two numbers) and majority formulae of size O(n 3:21 ) are constructed. Using more complicated basic carry save adders, not described here, these results could be further improved. Our best bounds are currently 3:57 log n for depth and O(n 3:13 ) for formula size. 1. Introduction The question `How fast can we multiply?' is one of the fundamental questions...

Abstract. In this paper, we survey a few recent applications of Kolmogorov complexity to lower bounds in several models of computation. We consider KI complexity of Boolean functions, which gives the complexity of finding a bit where inputs differ, for pairs of inputs that map to different function values. This measure and variants thereof were shown to imply lower bounds for quantum and randomized decision tree complexity (or query complexity) [LM04]. We give a similar result for deterministic decision trees as well. It was later shown in [LLS05] that KI complexity gives lower bounds for circuit depth. We review those results here, emphasizing simple proofs using Kolmogorov complexity, instead of strongest possible lower bounds. We also present a Kolmogorov complexity alternative to Yao’s min-max principle [LL04]. As an example, this is applied to randomized one-way communication complexity.

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It is generally conjectured that there is an exponential separation between Frege and extended Frege systems. This paper reviews and introduces some candidates for families of combinatoriM tautologies for which Frege proofs might need to be superpolynomially longer than extended Frege proofs. Surprisingly, we conclude that no particularly good or convincing examples are known. The examples of combinatorial tautologies that we consider seem to give at most a quasipolynomial speed-up of extended Frege proofs over Frege proofs, with the sole possible exception of tautologies based on a theorem of Frankl.

Abstract. We consider the relationship between size and depth for layered Boolean circuits, synchronous circuits and planar circuits as well as classes of circuits with small separators. In particular, we show that every layered Boolean circuit of size s can be simulated by a layered Boolean circuit of depth O ( √ s log s). For planar circuits and synchronous circuits of size s, we obtain simulations of depth O ( √ s). The best known result so far was by Paterson and Valiant [16], and Dymond and Tompa [6], which holds for general Boolean circuits and states that D(f) = O(C(f) / log C(f)), where C(f) and D(f) are the minimum size and depth, respectively, of Boolean circuits computing f. The proof of our main result uses an adaptive strategy based on the two-person pebble game introduced by Dymond and Tompa [6]. Improving any of our results by polylog factors would immediately improve the bounds for general circuits. Key words: Boolean circuits, circuit size, circuit depth, pebble games 1

h that any g i is either one of the projections x 1 ; : : : ; xn or it equals to h(g j1 ; : : : ; g jr ), for some h 2\Omega and j 1 ; : : : ; j r ! i. Drawing directed edges from all such j 1 ; : : : ; j r to i, and labelling i by h, defines a labelled directed acyclic graph. The size of the circuit is k. The maximum length of a path from a vertex corresponding to one of the inputs x 1 ; : : : ; xn to g k is the depth of the circuit. Note that formulas are circuits whose graphs are trees. There are three basic ways to measure complexity of a Boolean function, given a basis\Omega\Gamma the minimum size L\Omega

Abstract. We show that every formula over the basis {∧, ∨, ¬} for a function f: {0, 1} n → {0, 1}, such that ∀x, y ∈ f −1 (1), d(x, y) ≥ 2d + 1, has size Ω(n d+2 |f −1 (1)| |f −1). (0)| This immediately implies a lower bound Ω(n 2) for a characteristic function of a BCH code of distance 2d + 1. The main technique used is estimating the number of monochromatic rectangles needed to cover a matrix. 1

Abstract. Spira [28] showed that any Boolean formula of size s can be simulated in depth O(log s). We generalize Spira’s theorem and show that any Boolean circuit of size s with segregators of size f(s) can be simulated in depth O(f(s) log s). If the segregator size is at least s ε for some constant ε> 0, then we can obtain a simulation of depth O(f(s)). This improves and generalizes a simulation of polynomial-size Boolean circuits of constant treewidth k in depth O(k 2 log n) by Jansen and Sarma [17]. Since the existence of small balanced separators in a directed acyclic graph implies that the graph also has small segregators, our results also apply to circuits with small separators. Our results imply that the class of languages computed by non-uniform families of polynomial-size circuits that have constant size segregators equals non-uniform NC 1. Considering space bounded Turing machines to generate the circuits, for f(s) log 2 s-space uniform families of Boolean circuits our small-depth simulations are also f(s) log 2 s-space uniform. As a corollary, we show that the Boolean Circuit Value problem for circuits with constant size segregators (or separators) is in deterministic SP ACE(log 2 n). Our results also imply that the Planar Circuit Value problem, which is known to be P-Complete [16], can be solved in deterministic SP ACE ( √ n log n). Key words: Boolean circuits, circuit size, circuit depth, Spira’s theorem, Turing machines, space complexity 1