Diagram of an AC0 circuit: The n input bits are on the bottom and the top gate produces the output; the circuit consists of AND- and OR-gates of polynomial fan-in each, and the alternation depth is bounded by a constant.

AC0 (alternating circuit) is a complexity class used in circuit complexity. It is the smallest class in the AC hierarchy, and consists of all families of circuits of depth O(1) and polynomial size, with unlimited-fanin AND gates and OR gates (we allow NOT gates only at the inputs).[1] It thus contains NC0, which has only bounded-fanin AND and OR gates.[1] Such circuits are called "alternating circuits", since it is only necessary for the layers to alternate between all-AND and all-OR, since one AND after another AND is equivalent to a single AND, and the same for OR.

Example problems

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Integer addition and subtraction are computable in AC0,[2] but multiplication is not (specifically, when the inputs are two integers under the usual binary[3] or base-10 representations of integers).

Since it is a circuit class, like P/poly, AC0 also contains every unary language.

Descriptive complexity

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From a descriptive complexity viewpoint, DLOGTIME-uniform AC0 is equal to the descriptive class FO+BIT of all languages describable in first-order logic with the addition of the BIT predicate, or alternatively by FO(+, ×), or by Turing machine in the logarithmic hierarchy.[4]

Separations

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In 1984 Furst, Saxe, and Sipser showed that calculating the PARITY of the input bits (unlike the aforementioned addition/subtraction problems above which had two inputs) cannot be decided by any AC0 circuits, even with non-uniformity. Similarly, computing the majority is also not in .[5][1] It follows that AC0 is strictly smaller than TC0. Note that "PARITY" is also called "XOR" in the literature.

However, PARITY is only barely out of AC0, in the sense that for any , there exists a family of alternating circuits using depth and size .[6]: 135  In particular, setting to be a large constant, then there exists a family of alternating circuits using depth , and size only slightly superlinear.

can be divided further, into a hierarchy of languages requiring up to 1 layer, 2 layers, etc. Let be the class of languages decidable by a threshold circuit family of up to depth :The following problem is -complete under a uniformity condition: given a grid graph of polynomial length and width , decide whether two given vertices are connected.[7]

The addition of two -bit integers is in but not in .[6]: 148 

References

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  1. ^ a b c Arora, Sanjeev; Barak, Boaz (2009). Computational complexity. A modern approach. Cambridge University Press. pp. 117–118, 287. ISBN 978-0-521-42426-4. Zbl 1193.68112.
  2. ^ Barrington, David Mix; Maciel, Alexis (July 18, 2000). "Lecture 2: The Complexity of Some Problems" (PDF). IAS/PCMI Summer Session 2000, Clay Mathematics Undergraduate Program: Basic Course on Computational Complexity.
  3. ^ Kayal, Neeraj; Hegde, Sumant (2015). "Lecture 5: Feb 4, 2015" (PDF). E0 309: Topics in Complexity Theory. Archived (PDF) from the original on 2021-10-16. Retrieved 2021-10-16.
  4. ^ Immerman, N. (1999). Descriptive Complexity. Springer. p. 85.
  5. ^ Furst, Merrick; Saxe, James B.; Sipser, Michael (1984). "Parity, circuits, and the polynomial-time hierarchy". Mathematical Systems Theory. 17 (1): 13–27. doi:10.1007/BF01744431. MR 0738749. Zbl 0534.94008.
  6. ^ a b Parberry, Ian; Garey, Michael R.; Meyer, Albert (1994-07-27). Circuit Complexity and Neural Networks. The MIT Press. doi:10.7551/mitpress/1836.001.0001. ISBN 978-0-262-28124-9.
  7. ^ Barrington, David A. Mix; Lu, Chi-Jen; Miltersen, Peter Bro; Skyum, Sven (1998). "Searching constant width mazes captures the AC0 hierarchy". In Morvan, Michel; Meinel, Christoph; Krob, Daniel (eds.). Stacs 98. Lecture Notes in Computer Science. Vol. 1373. Berlin, Heidelberg: Springer. pp. 73–83. doi:10.1007/BFb0028550. ISBN 978-3-540-69705-3.

📚 Artikel Terkait di Wikipedia

Circuit value problem

Boolean circuit on a given input. The problem is complete for P under uniform AC0 reductions. Note that, in terms of time complexity, it can be solved in linear

Switching lemma

Boolean circuits. It was first introduced by Johan Håstad to prove that AC0 Boolean circuits of depth k require size exp ⁡ ( Ω ( n 1 / ( k − 1 ) ) )

ACC0

theoretical computer science. The class is defined by augmenting the class AC0 of constant-depth "alternating circuits" with the ability to count; the acronym

TC (complexity)

trivially in AC0 and depends on all bits separates the two classes. (For example, consider the OR function.) The strict containment AC0 ⊊ TC0 follows

Regular language

input is even or odd and this problem is not in AC0. On the other hand, REGULAR does not contain AC0, because the nonregular language of palindromes,

Natural proof

class AC0. They give strong evidence that the techniques used in these proofs cannot be extended to show stronger lower bounds. In particular, AC0-natural

Gadget (computer science)

their equivalence between AC0 reductions and NC0 reductions to show that all sets complete for NP under AC0 reductions are AC0-isomorphic. One application

PDP-10

PDP-10 registers 00 . . . 17 18 . . . 35 (bit position) General registers   AC0 Register 0   AC1 Register 1   AC2 Register 2   AC3 Register 3   AC4 Register