Lower bounds on communication complexity
Information and Computation
The power of randomness for communication complexity
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Multiparty protocols and logspace-hard pseudorandom sequences
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Communication complexity
STOC '82 Proceedings of the fourteenth annual ACM symposium on Theory of computing
Las Vegas is better than determinism in VLSI and distributed computing (Extended Abstract)
STOC '82 Proceedings of the fourteenth annual ACM symposium on Theory of computing
Some complexity questions related to distributive computing(Preliminary Report)
STOC '79 Proceedings of the eleventh annual ACM symposium on Theory of computing
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
On notions of information transfer in VLSI circuits
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
Multiparty Communication Complexity: Very Hard Functions
MFCS '99 Proceedings of the 24th International Symposium on Mathematical Foundations of Computer Science
Partition Arguments in Multiparty Communication Complexity
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
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A boolean function f(x1,...,xn) with xi ∈ {0,1}m for each i is hard if its nondeterministic multiparty communication complexity (introduced in [in: Proceedings of the 30th IEEE FOCS, 1989, p. 428-433]), C(f), is at least nm. Note that C(f) ≤ nm for each f(x1,...,xn) with xi ∈ {0, 1}m for each i. A boolean function is very hard if it is hard and its complementary function is also hard. In this paper, we show that randomly chosen boolean function f(x1,...,xn) with xi ∈ {0,1}m for each i is very hard with very high probability (for n ≥ 3 and m large enough). In [in: Proceedings of the 12th Symposium on Theoretical Aspects of Computer Science, LNCS 900, 1995, p. 350-360], it has been shown that if f(x1,...,xk,...,xn) = f1(x1,..., xk) ċ f2(xk+1,...,xn), where C(f1) 0 and C(f2) 0, then C(f) = C(f1) + C(f2). We prove here an analogical result: If f(x1,..., xk,..., xn) = f1(x1,...,xk) ⊕ f2(xk+1,..., xn) then DC(f) = DC(f1) + DC(f2), where DC(g) denotes the deterministic multiparty communication complexity of the function g and "⊕" denotes the parity function.