4,857 research outputs found
Element Distinctness, Frequency Moments, and Sliding Windows
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. We develop a randomized algorithm for the element distinctness problem
whose time T and space S satisfy T in O (n^{3/2}/S^{1/2}), smaller than
previous lower bounds for comparison-based algorithms, showing that element
distinctness is strictly easier than sorting for randomized branching programs.
This algorithm is based on a new time and space efficient algorithm for finding
all collisions of a function f from a finite set to itself that are reachable
by iterating f from a given set of starting points. We further show that our
element distinctness algorithm can be extended at only a polylogarithmic factor
cost to solve the element distinctness problem over sliding windows, where the
task is to take an input of length 2n-1 and produce an output for each window
of length n, giving n outputs in total. In contrast, we show a time-space
tradeoff lower bound of T in Omega(n^2/S) for randomized branching programs to
compute the number of distinct elements over sliding windows. The same lower
bound holds for computing the low-order bit of F_0 and computing any frequency
moment F_k, k neq 1. This shows that those frequency moments and the decision
problem F_0 mod 2 are strictly harder than element distinctness. We complement
this lower bound with a T in O(n^2/S) comparison-based deterministic RAM
algorithm for exactly computing F_k over sliding windows, nearly matching both
our lower bound for the sliding-window version and the comparison-based lower
bounds for the single-window version. We further exhibit a quantum algorithm
for F_0 over sliding windows with T in O(n^{3/2}/S^{1/2}). Finally, we consider
the computations of order statistics over sliding windows.Comment: arXiv admin note: substantial text overlap with arXiv:1212.437
Time-Space Tradeoffs for the Memory Game
A single-player game of Memory is played with distinct pairs of cards,
with the cards in each pair bearing identical pictures. The cards are laid
face-down. A move consists of revealing two cards, chosen adaptively. If these
cards match, i.e., they bear the same picture, they are removed from play;
otherwise, they are turned back to face down. The object of the game is to
clear all cards while minimizing the number of moves. Past works have
thoroughly studied the expected number of moves required, assuming optimal play
by a player has that has perfect memory. In this work, we study the Memory game
in a space-bounded setting.
We prove two time-space tradeoff lower bounds on algorithms (strategies for
the player) that clear all cards in moves while using at most bits of
memory. First, in a simple model where the pictures on the cards may only be
compared for equality, we prove that . This is tight:
it is easy to achieve essentially everywhere on this
tradeoff curve. Second, in a more general model that allows arbitrary
computations, we prove that . We prove this latter tradeoff
by modeling strategies as branching programs and extending a classic counting
argument of Borodin and Cook with a novel probabilistic argument. We conjecture
that the stronger tradeoff in fact holds even in
this general model
Finding the Median (Obliviously) with Bounded Space
We prove that any oblivious algorithm using space to find the median of a
list of integers from requires time . This bound also applies to the problem of determining whether the median
is odd or even. It is nearly optimal since Chan, following Munro and Raman, has
shown that there is a (randomized) selection algorithm using only
registers, each of which can store an input value or -bit counter,
that makes only passes over the input. The bound also implies
a size lower bound for read-once branching programs computing the low order bit
of the median and implies the analog of for length oblivious branching programs
Memory-Adjustable Navigation Piles with Applications to Sorting and Convex Hulls
We consider space-bounded computations on a random-access machine (RAM) where
the input is given on a read-only random-access medium, the output is to be
produced to a write-only sequential-access medium, and the available workspace
allows random reads and writes but is of limited capacity. The length of the
input is elements, the length of the output is limited by the computation,
and the capacity of the workspace is bits for some predetermined
parameter . We present a state-of-the-art priority queue---called an
adjustable navigation pile---for this restricted RAM model. Under some
reasonable assumptions, our priority queue supports and
in worst-case time and in worst-case time for any . We show how to use this
data structure to sort elements and to compute the convex hull of
points in the two-dimensional Euclidean space in
worst-case time for any . Following a known lower bound for the
space-time product of any branching program for finding unique elements, both
our sorting and convex-hull algorithms are optimal. The adjustable navigation
pile has turned out to be useful when designing other space-efficient
algorithms, and we expect that it will find its way to yet other applications.Comment: 21 page
Quantum and Classical Strong Direct Product Theorems and Optimal Time-Space Tradeoffs
A strong direct product theorem says that if we want to compute k independent
instances of a function, using less than k times the resources needed for one
instance, then our overall success probability will be exponentially small in
k. We establish such theorems for the classical as well as quantum query
complexity of the OR function. This implies slightly weaker direct product
results for all total functions. We prove a similar result for quantum
communication protocols computing k instances of the Disjointness function.
Our direct product theorems imply a time-space tradeoff T^2*S=Omega(N^3) for
sorting N items on a quantum computer, which is optimal up to polylog factors.
They also give several tight time-space and communication-space tradeoffs for
the problems of Boolean matrix-vector multiplication and matrix multiplication.Comment: 22 pages LaTeX. 2nd version: some parts rewritten, results are
essentially the same. A shorter version will appear in IEEE FOCS 0
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