15,308 research outputs found
Cell-probe Lower Bounds for Dynamic Problems via a New Communication Model
In this paper, we develop a new communication model to prove a data structure
lower bound for the dynamic interval union problem. The problem is to maintain
a multiset of intervals over with integer coordinates,
supporting the following operations:
- insert(a, b): add an interval to , provided that
and are integers in ;
- delete(a, b): delete a (previously inserted) interval from
;
- query(): return the total length of the union of all intervals in
.
It is related to the two-dimensional case of Klee's measure problem. We prove
that there is a distribution over sequences of operations with
insertions and deletions, and queries, for which any data
structure with any constant error probability requires time
in expectation. Interestingly, we use the sparse set disjointness protocol of
H\aa{}stad and Wigderson [ToC'07] to speed up a reduction from a new kind of
nondeterministic communication games, for which we prove lower bounds.
For applications, we prove lower bounds for several dynamic graph problems by
reducing them from dynamic interval union
Amortized Dynamic Cell-Probe Lower Bounds from Four-Party Communication
This paper develops a new technique for proving amortized, randomized
cell-probe lower bounds on dynamic data structure problems. We introduce a new
randomized nondeterministic four-party communication model that enables
"accelerated", error-preserving simulations of dynamic data structures.
We use this technique to prove an cell-probe
lower bound for the dynamic 2D weighted orthogonal range counting problem
(2D-ORC) with updates and queries, that holds even
for data structures with success probability. This
result not only proves the highest amortized lower bound to date, but is also
tight in the strongest possible sense, as a matching upper bound can be
obtained by a deterministic data structure with worst-case operational time.
This is the first demonstration of a "sharp threshold" phenomenon for dynamic
data structures.
Our broader motivation is that cell-probe lower bounds for exponentially
small success facilitate reductions from dynamic to static data structures. As
a proof-of-concept, we show that a slightly strengthened version of our lower
bound would imply an lower bound for the
static 3D-ORC problem with space. Such result would give a
near quadratic improvement over the highest known static cell-probe lower
bound, and break the long standing barrier for static data
structures
Equivalence of Systematic Linear Data Structures and Matrix Rigidity
Recently, Dvir, Golovnev, and Weinstein have shown that sufficiently strong
lower bounds for linear data structures would imply new bounds for rigid
matrices. However, their result utilizes an algorithm that requires an
oracle, and hence, the rigid matrices are not explicit. In this work, we derive
an equivalence between rigidity and the systematic linear model of data
structures. For the -dimensional inner product problem with queries, we
prove that lower bounds on the query time imply rigidity lower bounds for the
query set itself. In particular, an explicit lower bound of
for redundant storage bits would
yield better rigidity parameters than the best bounds due to Alon, Panigrahy,
and Yekhanin. We also prove a converse result, showing that rigid matrices
directly correspond to hard query sets for the systematic linear model. As an
application, we prove that the set of vectors obtained from rank one binary
matrices is rigid with parameters matching the known results for explicit sets.
This implies that the vector-matrix-vector problem requires query time
for redundancy in the systematic linear
model, improving a result of Chakraborty, Kamma, and Larsen. Finally, we prove
a cell probe lower bound for the vector-matrix-vector problem in the high error
regime, improving a result of Chattopadhyay, Kouck\'{y}, Loff, and
Mukhopadhyay.Comment: 23 pages, 1 tabl
Crossing the Logarithmic Barrier for Dynamic Boolean Data Structure Lower Bounds
This paper proves the first super-logarithmic lower bounds on the cell probe
complexity of dynamic boolean (a.k.a. decision) data structure problems, a
long-standing milestone in data structure lower bounds.
We introduce a new method for proving dynamic cell probe lower bounds and use
it to prove a lower bound on the operational
time of a wide range of boolean data structure problems, most notably, on the
query time of dynamic range counting over ([Pat07]). Proving an
lower bound for this problem was explicitly posed as one of
five important open problems in the late Mihai P\v{a}tra\c{s}cu's obituary
[Tho13]. This result also implies the first lower bound for the
classical 2D range counting problem, one of the most fundamental data structure
problems in computational geometry and spatial databases. We derive similar
lower bounds for boolean versions of dynamic polynomial evaluation and 2D
rectangle stabbing, and for the (non-boolean) problems of range selection and
range median.
Our technical centerpiece is a new way of "weakly" simulating dynamic data
structures using efficient one-way communication protocols with small advantage
over random guessing. This simulation involves a surprising excursion to
low-degree (Chebychev) polynomials which may be of independent interest, and
offers an entirely new algorithmic angle on the "cell sampling" method of
Panigrahy et al. [PTW10]
New Unconditional Hardness Results for Dynamic and Online Problems
There has been a resurgence of interest in lower bounds whose truth rests on
the conjectured hardness of well known computational problems. These
conditional lower bounds have become important and popular due to the painfully
slow progress on proving strong unconditional lower bounds. Nevertheless, the
long term goal is to replace these conditional bounds with unconditional ones.
In this paper we make progress in this direction by studying the cell probe
complexity of two conjectured to be hard problems of particular importance:
matrix-vector multiplication and a version of dynamic set disjointness known as
Patrascu's Multiphase Problem. We give improved unconditional lower bounds for
these problems as well as introducing new proof techniques of independent
interest. These include a technique capable of proving strong threshold lower
bounds of the following form: If we insist on having a very fast query time,
then the update time has to be slow enough to compute a lookup table with the
answer to every possible query. This is the first time a lower bound of this
type has been proven
Cell-Probe Lower Bounds from Online Communication Complexity
In this work, we introduce an online model for communication complexity.
Analogous to how online algorithms receive their input piece-by-piece, our
model presents one of the players, Bob, his input piece-by-piece, and has the
players Alice and Bob cooperate to compute a result each time before the next
piece is revealed to Bob. This model has a closer and more natural
correspondence to dynamic data structures than classic communication models do,
and hence presents a new perspective on data structures.
We first present a tight lower bound for the online set intersection problem
in the online communication model, demonstrating a general approach for proving
online communication lower bounds. The online communication model prevents a
batching trick that classic communication complexity allows, and yields a
stronger lower bound. We then apply the online communication model to prove
data structure lower bounds for two dynamic data structure problems: the Group
Range problem and the Dynamic Connectivity problem for forests. Both of the
problems admit a worst case -time data structure. Using online
communication complexity, we prove a tight cell-probe lower bound for each:
spending (even amortized) time per operation results in at best an
probability of correctly answering a
-fraction of the queries
Lower Bounds for Oblivious Near-Neighbor Search
We prove an lower bound on the dynamic
cell-probe complexity of statistically
approximate-near-neighbor search () over the -dimensional
Hamming cube. For the natural setting of , our result
implies an lower bound, which is a quadratic
improvement over the highest (non-oblivious) cell-probe lower bound for
. This is the first super-logarithmic
lower bound for against general (non black-box) data structures.
We also show that any oblivious data structure for
decomposable search problems (like ) can be obliviously dynamized
with overhead in update and query time, strengthening a classic
result of Bentley and Saxe (Algorithmica, 1980).Comment: 28 page
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