CONSTANT-DEPTH PERIODIC CIRCUITS

1991 ◽  
Vol 01 (01) ◽  
pp. 49-87 ◽  
Author(s):  
HOWARD STRAUBING
Keyword(s):  
Solvable Groups ◽  
Computational Power ◽  
Constant Depth ◽  
Branching Programs ◽  
Dihedral Groups ◽  
Circuit Element ◽  
Independent Evidence ◽  
Polynomial Size ◽  
And Function ◽  
Fixed Period

This paper is devoted to the languages accepted by constant-depth, polynomial-size families of circuits in which every circuit element computes the sum of its input bits modulo a fixed period q. It has been conjectured that such a circuit family cannot compute the AND function of n inputs. Here it is shown that such circuit families are equivalent in power to polynomial-length programs over finite solvable groups; in particular, the conjecture implies that Barrington's result on the computational power of branching programs over nonsolvable groups cannot be extended to solvable groups. It is also shown that polynomial-length programs over dihedral groups cannot compute the AND function. Furthermore, it is shown that the conjecture is equivalent to a characterization, in terms of finite semigroups and formal logic, of the regular languages accepted by such circuit families. There is, moreover, considerable independent evidence for this characterization. This last result is established using new theorems, of independent interest, concerning the algebraic structure of finite categories.

ON SERIALIZABLE LANGUAGES

1994 ◽  
Vol 05 (03n04) ◽  
pp. 303-318 ◽  
Author(s):  
MITSUNORI OGIHARA
Keyword(s):  
Lower Bounds ◽  
Upper Bounds ◽  
Turing Machines ◽  
Computational Power ◽  
Branching Programs ◽  
Polynomial Size

Cai and Furst introduced the notion of bottleneck Turing machines. Based on Barrington’s innovating technique, which is used to showed that polynomial-size branching programs have exactly the same power as NC1, Cai and Furst showed that the languages recognized by width-5 bottleneck Turing machines are exactly the same as those in PSPACE. In this paper, computational power of bottleneck Turing machines with widths fewer than 5 is investigated. It is shown that width-2 bottleneck Turing machines capture ⊕P// OptP , the class of sets recognized by ⊕P-machines with pre-computation in OptP. For languages recognized by bottleneck Turing machines with width-3 and width-4, some lower-bounds and upper-bounds are shown.

scholarly journals Circuits on Cylinders

2002 ◽  
Vol 9 (50) ◽  
Author(s):  
Kristoffer Arnsfelt Hansen ◽  
Peter Bro Miltersen ◽  
V. Vinay
Keyword(s):  
Constant Width ◽  
Computational Power ◽  
Branching Programs ◽  
Polynomial Size

We consider the computational power of constant width polynomial size cylindrical circuits and nondeterministic branching programs. We show that every function computed by a Pi_2 o MOD o AC^0 circuit can also be computed by a constant width polynomial size cylindrical nondeterministic branching program (or cylindrical circuit) and that every function computed by a constant width polynomial size cylindrical circuit belongs to ACC^0.

scholarly journals Uniform derandomization from pathetic lower bounds

2012 ◽  
Vol 370 (1971) ◽  
pp. 3512-3535 ◽  
Author(s):  
Eric Allender ◽  
V. Arvind ◽  
Rahul Santhanam ◽  
Fengming Wang
Keyword(s):  
Word Problem ◽  
Lower Bounds ◽  
Black Box ◽  
Arithmetic Circuits ◽  
List Type ◽  
Probabilistic Algorithms ◽  
Constant Depth ◽  
Subexponential Time ◽  
Polynomial Size ◽  
Threshold Circuits

The notion of probabilistic computation dates back at least to Turing, who also wrestled with the practical problems of how to implement probabilistic algorithms on machines with, at best, very limited access to randomness. A more recent line of research, known as derandomization, studies the extent to which randomness is superfluous. A recurring theme in the literature on derandomization is that probabilistic algorithms can be simulated quickly by deterministic algorithms, if one can obtain impressive (i.e. superpolynomial, or even nearly exponential) circuit size lower bounds for certain problems. In contrast to what is needed for derandomization, existing lower bounds seem rather pathetic. Here, we present two instances where ‘pathetic’ lower bounds of the form n 1+ ϵ would suffice to derandomize interesting classes of probabilistic algorithms. We show the following: — If the word problem over S 5 requires constant-depth threshold circuits of size n 1+ ϵ for some ϵ >0, then any language accepted by uniform polynomial size probabilistic threshold circuits can be solved in subexponential time (and, more strongly, can be accepted by a uniform family of deterministic constant-depth threshold circuits of subexponential size). — If there are no constant-depth arithmetic circuits of size n 1+ ϵ for the problem of multiplying a sequence of n  3×3 matrices, then, for every constant d , black-box identity testing for depth- d arithmetic circuits with bounded individual degree can be performed in subexponential time (and even by a uniform family of deterministic constant-depth AC 0 circuits of subexponential size).

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