Results 1  10
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21
Monotone Complexity
, 1990
"... We give a general complexity classification scheme for monotone computation, including monotone spacebounded and Turing machine models not previously considered. We propose monotone complexity classes including mAC i , mNC i , mLOGCFL, mBWBP , mL, mNL, mP , mBPP and mNP . We define a simple ..."
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Cited by 2359 (12 self)
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We give a general complexity classification scheme for monotone computation, including monotone spacebounded and Turing machine models not previously considered. We propose monotone complexity classes including mAC i , mNC i , mLOGCFL, mBWBP , mL, mNL, mP , mBPP and mNP . We define a simple notion of monotone reducibility and exhibit complete problems. This provides a framework for stating existing results and asking new questions. We show that mNL (monotone nondeterministic logspace) is not closed under complementation, in contrast to Immerman's and Szelepcs 'enyi's nonmonotone result [Imm88, Sze87] that NL = coNL; this is a simple extension of the monotone circuit depth lower bound of Karchmer and Wigderson [KW90] for stconnectivity. We also consider mBWBP (monotone bounded width branching programs) and study the question of whether mBWBP is properly contained in mNC 1 , motivated by Barrington's result [Bar89] that BWBP = NC 1 . Although we cannot answer t...
Circuit Complexity before the Dawn of the New Millennium
, 1997
"... The 1980's saw rapid and exciting development of techniques for proving lower bounds in circuit complexity. This pace has slowed recently, and there has even been work indicating that quite different proof techniques must be employed to advance beyond the current frontier of circuit lower bound ..."
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Cited by 30 (3 self)
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The 1980's saw rapid and exciting development of techniques for proving lower bounds in circuit complexity. This pace has slowed recently, and there has even been work indicating that quite different proof techniques must be employed to advance beyond the current frontier of circuit lower bounds. Although this has engendered pessimism in some quarters, there have in fact been many positive developments in the past few years showing that significant progress is possible on many fronts. This paper is a (necessarily incomplete) survey of the state of circuit complexity as we await the dawn of the new millennium.
The Permanent Requires Large Uniform Threshold Circuits
, 1999
"... We show that the permanent cannot be computed by uniform constantdepth threshold circuits of size T (n) for any function T such that for all k, T (k) (n) = o(2 n ). More generally, we show that any problem that is hard for the complexity class C=P requires circuits of this size (on the unif ..."
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Cited by 28 (8 self)
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We show that the permanent cannot be computed by uniform constantdepth threshold circuits of size T (n) for any function T such that for all k, T (k) (n) = o(2 n ). More generally, we show that any problem that is hard for the complexity class C=P requires circuits of this size (on the uniform constantdepth threshold circuit model). In particular, this lower bound applies to any problem that is hard for the complexity classes PP or #P.
On the Relation Between BDDs and FDDs
 INFORMATION AND COMPUTATION
, 1995
"... Data structures for Boolean functions build an essential component of design automation tools, especially in the area of logic synthesis. The state of the art data structure is the ordered binary decision diagram (OBDD), which results from general binary decision diagrams (BDDs), also called bran ..."
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Cited by 27 (12 self)
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Data structures for Boolean functions build an essential component of design automation tools, especially in the area of logic synthesis. The state of the art data structure is the ordered binary decision diagram (OBDD), which results from general binary decision diagrams (BDDs), also called branching programs, by ordering restrictions. In the context of EXORbased logic synthesis another type of decision diagram (DD), called (ordered) functional decision diagram ((O)FDD) becomes increasingly important. We study the relation between (ordered, free) BDDs and FDDs. Both, BDDs and FDDs, result from DDs by defining the represented function in different ways. If the underlying DD is complete, the relation between both types of interpretation can be described by a Boolean transformation . This allows us to relate the FDDsize of f and the BDDsize of (f) also in the case that the corresponding DDs are free or ordered, but not (necessarily) complete. We use this property to derive...
Analog versus Discrete Neural Networks
 Neural Computation
, 1996
"... We show that neural networks with threetimes continuously differentiable activation functions are capable of computing a certain family of nbit Boolean functions with two gates, whereas networks composed of binary threshold functions require at least \Omega\Gammaast n) gates. Thus, for a large cla ..."
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Cited by 17 (2 self)
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We show that neural networks with threetimes continuously differentiable activation functions are capable of computing a certain family of nbit Boolean functions with two gates, whereas networks composed of binary threshold functions require at least \Omega\Gammaast n) gates. Thus, for a large class of activation functions, analog neural networks can be more powerful than discrete neural networks, even when computing Boolean functions. 1 Introduction. Artificial neural networks have become a popular model for machine learning and many results have been obtained regarding their application to practical problems. Typically, the network is trained to encode complex associations between inputs and outputs during supervised training cycles, where the associations are encoded by the weights of the network. Once trained, the network will compute an input/output mapping which (hopefully) is a good approximation of the original mapping. 1 Partially supported by NSF Grant CCR9114545 In thi...
On TC⁰, AC⁰, and Arithmetic Circuits
 JOURNAL OF COMPUTER AND SYSTEM SCIENCES
, 2000
"... Continuing a line of investigation that has studied the function classes #P [Val79b], #SAC¹ [Val79a, Vin91, AJMV], #L [AJ93b, Vin91, AO94], and #NC¹ [CMTV96], we study the class of functions #AC⁰. One way to define #AC⁰ is as the class of functions computed by constantdepth polynomialsize arith ..."
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Cited by 13 (3 self)
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Continuing a line of investigation that has studied the function classes #P [Val79b], #SAC¹ [Val79a, Vin91, AJMV], #L [AJ93b, Vin91, AO94], and #NC¹ [CMTV96], we study the class of functions #AC⁰. One way to define #AC⁰ is as the class of functions computed by constantdepth polynomialsize arithmetic circuits of unbounded fanin addition and multiplication gates. In contrast to the preceding
On Methods for Proving Lower Bounds in Propositional Logic
"... This paper is based on my lecture [26]. It examines the problem of proving nontrivial lower bounds for the length of proofs in propositional logic from the perspective of methods available rather than surveying known partial results (i.e., lower bounds for weaker proof systems). We discuss neither ..."
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Cited by 8 (2 self)
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This paper is based on my lecture [26]. It examines the problem of proving nontrivial lower bounds for the length of proofs in propositional logic from the perspective of methods available rather than surveying known partial results (i.e., lower bounds for weaker proof systems). We discuss neither motivations for proving lower bounds for propositional logic nor relations to other problems in logic or complexity theory. The reader is referred to [20] for the background information (as well as for all details missing in this paper). The paper is aimed at curious nonspecialists. The style of our exposition is accordingly informal at places and we do not burden the text (especially in the introduction) with exhausting references not directly related to our main objective. The reader starving for details can find them, together with all original references, in [20] (see also expository articles [25, 32]). Introduction
Cracks in the Defenses: Scouting Out Approaches on Circuit Lower Bounds
"... Razborov and Rudich identified an imposing barrier that stands in the way of progress toward the goal of proving superpolynomial lower bounds on circuit size. Their work on “natural proofs” applies to a large class of arguments that have been used in complexity theory, and shows that no such argum ..."
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Cited by 3 (0 self)
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Razborov and Rudich identified an imposing barrier that stands in the way of progress toward the goal of proving superpolynomial lower bounds on circuit size. Their work on “natural proofs” applies to a large class of arguments that have been used in complexity theory, and shows that no such argument can prove that a problem requires circuits of superpolynomial size, even for some very restricted classes of circuits (under reasonable cryptographic assumptions). This barrier is so daunting, that some researchers have decided to focus their attentions elsewhere. Yet the goal of proving circuit lower bounds is of such importance, that some in the community have proposed concrete strategies for surmounting the obstacle. This lecture will discuss some of these strategies, and will dwell at length on a recent approach proposed by Michal Koucky and the author.
A Note on a Theorem of Barrington, Straubing and Therien
 Information Processing Letters
, 1996
"... ..."
Polynomial Programs and the RazborovSmolensky Method
 Electronic Colloquium on Computational Complexity
, 2001
"... Representations of boolean functions as polynomials (over rings) have been used to establish lower bounds in complexity theory. Such representations were used to great effect by Smolensky, who established that MOD q / # AC 0 [MOD p] (for distinct primes p, q) by representing AC 0 [MOD p] fun ..."
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Cited by 1 (1 self)
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Representations of boolean functions as polynomials (over rings) have been used to establish lower bounds in complexity theory. Such representations were used to great effect by Smolensky, who established that MOD q / # AC 0 [MOD p] (for distinct primes p, q) by representing AC 0 [MOD p] functions as lowdegree multilinear polynomials over fields of characteristic p. Another tool which has yielded insight into smalldepth circuit complexity classes is the programovermonoids model of computation, which has provided characterizations of circuit complexity classes such as AC 0 and NC 1 .