229 research outputs found
Improved Bounds for 3SUM, -SUM, and Linear Degeneracy
Given a set of real numbers, the 3SUM problem is to decide whether there
are three of them that sum to zero. Until a recent breakthrough by Gr{\o}nlund
and Pettie [FOCS'14], a simple -time deterministic algorithm for
this problem was conjectured to be optimal. Over the years many algorithmic
problems have been shown to be reducible from the 3SUM problem or its variants,
including the more generalized forms of the problem, such as -SUM and
-variate linear degeneracy testing (-LDT). The conjectured hardness of
these problems have become extremely popular for basing conditional lower
bounds for numerous algorithmic problems in P.
In this paper, we show that the randomized -linear decision tree
complexity of 3SUM is , and that the randomized -linear
decision tree complexity of -SUM and -LDT is , for any odd
. These bounds improve (albeit randomized) the corresponding
and decision tree bounds
obtained by Gr{\o}nlund and Pettie. Our technique includes a specialized
randomized variant of fractional cascading data structure. Additionally, we
give another deterministic algorithm for 3SUM that runs in time. The latter bound matches a recent independent bound by Freund
[Algorithmica 2017], but our algorithm is somewhat simpler, due to a better use
of word-RAM model
Clustered Integer 3SUM via Additive Combinatorics
We present a collection of new results on problems related to 3SUM,
including:
1. The first truly subquadratic algorithm for
1a. computing the (min,+) convolution for monotone increasing
sequences with integer values bounded by ,
1b. solving 3SUM for monotone sets in 2D with integer coordinates
bounded by , and
1c. preprocessing a binary string for histogram indexing (also
called jumbled indexing).
The running time is:
with
randomization, or deterministically. This greatly improves the
previous time bound obtained from Williams'
recent result on all-pairs shortest paths [STOC'14], and answers an open
question raised by several researchers studying the histogram indexing problem.
2. The first algorithm for histogram indexing for any constant alphabet size
that achieves truly subquadratic preprocessing time and truly sublinear query
time.
3. A truly subquadratic algorithm for integer 3SUM in the case when the given
set can be partitioned into clusters each covered by an interval
of length , for any constant .
4. An algorithm to preprocess any set of integers so that subsequently
3SUM on any given subset can be solved in
time.
All these results are obtained by a surprising new technique, based on the
Balog--Szemer\'edi--Gowers Theorem from additive combinatorics
Threesomes, Degenerates, and Love Triangles
The 3SUM problem is to decide, given a set of real numbers, whether any
three sum to zero. It is widely conjectured that a trivial -time
algorithm is optimal and over the years the consequences of this conjecture
have been revealed. This 3SUM conjecture implies lower bounds on
numerous problems in computational geometry and a variant of the conjecture
implies strong lower bounds on triangle enumeration, dynamic graph algorithms,
and string matching data structures.
In this paper we refute the 3SUM conjecture. We prove that the decision tree
complexity of 3SUM is and give two subquadratic 3SUM
algorithms, a deterministic one running in
time and a randomized one running in time with
high probability. Our results lead directly to improved bounds for -variate
linear degeneracy testing for all odd . The problem is to decide, given
a linear function and a set , whether . We show the
decision tree complexity of this problem is .
Finally, we give a subcubic algorithm for a generalization of the
-product over real-valued matrices and apply it to the problem of
finding zero-weight triangles in weighted graphs. We give a
depth- decision tree for this problem, as well as an
algorithm running in time
Solving -SUM using few linear queries
The -SUM problem is given input real numbers to determine whether any
of them sum to zero. The problem is of tremendous importance in the
emerging field of complexity theory within , and it is in particular open
whether it admits an algorithm of complexity with . Inspired by an algorithm due to Meiser (1993), we show
that there exist linear decision trees and algebraic computation trees of depth
solving -SUM. Furthermore, we show that there exists a
randomized algorithm that runs in
time, and performs linear queries on the input. Thus, we show
that it is possible to have an algorithm with a runtime almost identical (up to
the ) to the best known algorithm but for the first time also with the
number of queries on the input a polynomial that is independent of . The
bound on the number of linear queries is also a tighter bound
than any known algorithm solving -SUM, even allowing unlimited total time
outside of the queries. By simultaneously achieving few queries to the input
without significantly sacrificing runtime vis-\`{a}-vis known algorithms, we
deepen the understanding of this canonical problem which is a cornerstone of
complexity-within-.
We also consider a range of tradeoffs between the number of terms involved in
the queries and the depth of the decision tree. In particular, we prove that
there exist -linear decision trees of depth
Why walking the dog takes time: Frechet distance has no strongly subquadratic algorithms unless SETH fails
The Frechet distance is a well-studied and very popular measure of similarity
of two curves. Many variants and extensions have been studied since Alt and
Godau introduced this measure to computational geometry in 1991. Their original
algorithm to compute the Frechet distance of two polygonal curves with n
vertices has a runtime of O(n^2 log n). More than 20 years later, the state of
the art algorithms for most variants still take time more than O(n^2 / log n),
but no matching lower bounds are known, not even under reasonable complexity
theoretic assumptions.
To obtain a conditional lower bound, in this paper we assume the Strong
Exponential Time Hypothesis or, more precisely, that there is no
O*((2-delta)^N) algorithm for CNF-SAT for any delta > 0. Under this assumption
we show that the Frechet distance cannot be computed in strongly subquadratic
time, i.e., in time O(n^{2-delta}) for any delta > 0. This means that finding
faster algorithms for the Frechet distance is as hard as finding faster CNF-SAT
algorithms, and the existence of a strongly subquadratic algorithm can be
considered unlikely.
Our result holds for both the continuous and the discrete Frechet distance.
We extend the main result in various directions. Based on the same assumption
we (1) show non-existence of a strongly subquadratic 1.001-approximation, (2)
present tight lower bounds in case the numbers of vertices of the two curves
are imbalanced, and (3) examine realistic input assumptions (c-packed curves)
Conditional Lower Bounds for Space/Time Tradeoffs
In recent years much effort has been concentrated towards achieving
polynomial time lower bounds on algorithms for solving various well-known
problems. A useful technique for showing such lower bounds is to prove them
conditionally based on well-studied hardness assumptions such as 3SUM, APSP,
SETH, etc. This line of research helps to obtain a better understanding of the
complexity inside P.
A related question asks to prove conditional space lower bounds on data
structures that are constructed to solve certain algorithmic tasks after an
initial preprocessing stage. This question received little attention in
previous research even though it has potential strong impact.
In this paper we address this question and show that surprisingly many of the
well-studied hard problems that are known to have conditional polynomial time
lower bounds are also hard when concerning space. This hardness is shown as a
tradeoff between the space consumed by the data structure and the time needed
to answer queries. The tradeoff may be either smooth or admit one or more
singularity points.
We reveal interesting connections between different space hardness
conjectures and present matching upper bounds. We also apply these hardness
conjectures to both static and dynamic problems and prove their conditional
space hardness.
We believe that this novel framework of polynomial space conjectures can play
an important role in expressing polynomial space lower bounds of many important
algorithmic problems. Moreover, it seems that it can also help in achieving a
better understanding of the hardness of their corresponding problems in terms
of time
Subquadratic Algorithms for Algebraic Generalizations of 3SUM
info:eu-repo/semantics/publishe
- …