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 ... more >>>

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 ... more >>>

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 ... more >>>

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.

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
more >>>

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 ... more >>>

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 >>>

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 >>>

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 >>>

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 >>>

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 >>>

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 >>>

We develop an extension of recent analytic methods for obtaining time-space tradeoff lower bounds for problems of learning from uniformly random labelled examples. With our methods we can obtain bounds for learning concept classes of finite functions from random evaluations even when the sample space of random inputs can be ... more >>>

In function inversion, we are given a function $f: [N] \mapsto [N]$, and want to prepare some advice of size $S$, such that we can efficiently invert any image in time $T$. This is a well studied problem with profound connections to cryptography, data structures, communication complexity, and circuit lower ... more >>>