Pavel Pudlak, Jiri Sgall

We prove an unexpected upper bound on a communication game proposed

by Jeff Edmonds and Russell Impagliazzo as an approach for

proving lower bounds for time-space tradeoffs for branching programs.

Our result is based on a generalization of a construction of Erdos,

Frankl and Rodl of a large 3-hypergraph ...
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Paul Beame, Michael Saks, Jayram S. Thathachar

We obtain the first non-trivial time-space tradeoff lower bound for

functions f:{0,1}^n ->{0,1} on general branching programs by exhibiting a

Boolean function f that requires exponential size to be computed by any

branching program of length cn, for some constant c>1. We also give the first

separation result between the ...
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Paul Beame, Michael Saks, Xiaodong Sun, Erik Vee

We prove the first time-space lower bound tradeoffs for randomized

computation of decision problems. The bounds hold even in the

case that the computation is allowed to have arbitrary probability

of error on a small fraction of inputs. Our techniques are an

extension of those used by Ajtai in his ...
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Lance Fortnow, Dieter van Melkebeek

We show new tradeoffs for satisfiability and nondeterministic

linear time. Satisfiability cannot be solved on general purpose

random-access Turing machines in time $n^{1.618}$ and space

$n^{o(1)}$. This improves recent results of Lipton and Viglas and

Fortnow.

Eric Allender, Michal Koucky, Detlef Ronneburger, Sambuddha Roy, V. Vinay

We extend the lower bound techniques of [Fortnow], to the

unbounded-error probabilistic model. A key step in the argument

is a generalization of Nepomnjascii's theorem from the Boolean

setting to the arithmetic setting. This generalization is made

possible, due to the recent discovery of logspace-uniform TC^0

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Rahul Santhanam

We consider uniform assumptions for derandomization. We provide

intuitive evidence that BPP can be simulated non-trivially in

deterministic time by showing that (1) P \not \subseteq i.o.i.PLOYLOGSPACE

implies BPP \subseteq SUBEXP (2) P \not \subseteq SUBPSPACE implies BPP

= P. These results extend and complement earlier work of ...
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Ryan Williams

We prove the first time-space tradeoffs for counting the number of solutions to an NP problem modulo small integers, and also improve upon the known time-space tradeoffs for Sat. Let m be a positive integer, and define MODm-Sat to be the problem of determining if a given Boolean formula has ... more >>>

Dieter van Melkebeek

Ever since the fundamental work of Cook from 1971, satisfiability has been recognized as a central problem in computational complexity. It is widely believed to be intractable, and yet till recently even a linear-time, logarithmic-space algorithm for satisfiability was not ruled out. In 1997 Fortnow, building on earlier work by ... more >>>

Eli Ben-Sasson, Jakob NordstrÃ¶m

A number of works have looked at the relationship between length and space of resolution proofs. A notorious question has been whether the existence of a short proof implies the existence of a proof that can be verified using limited space.

In this paper we resolve the question by answering ... more >>>

Paul Beame, Widad Machmouchi

We prove a time-space tradeoff lower bound of $T =

\Omega\left(n\log(\frac{n}{S}) \log \log(\frac{n}{S})\right) $ for

randomized oblivious branching programs to compute $1GAP$, also

known as the pointer jumping problem, a problem for which there is a

simple deterministic time $n$ and space $O(\log n)$ RAM (random

access machine) algorithm.

In ... more >>>

Paul Beame, Raphael Clifford, Widad Machmouchi

We derive new time-space tradeoff lower bounds and algorithms for exactly computing statistics of input data, including frequency moments, element distinctness, and order statistics, that are simple to calculate for sorted data. In particular, we develop a randomized algorithm for the element distinctness problem whose time $T$ and space $S$ ... more >>>

Paul Beame, Shayan Oveis Gharan, Xin Yang

We develop an extension of recently developed methods for obtaining time-space tradeoff lower bounds for problems of learning from random test samples to handle the situation where the space of tests is signficantly smaller than the space of inputs, a class of learning problems that is not handled by prior ... more >>>