By Topic

Higher Lower Bounds for Near-Neighbor and Further Rich Problems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)

We convert cell-probe lower bounds for polynomial space into stronger lower bound for near-linear space. Our technique applies to any lower bound proved through the richness method. For example, it applies to partial match, and to near-neighbor problems, either for randomized exact search, or for deterministic approximate search (which are thought to exhibit the curse of dimensionality). These problems are motivated by search in large data bases, so near-linear space is the most relevant regime. Typically, richness has been used to imply Omega(d/lg n) lower bounds for polynomial-space data structures, where d is the number of bits of a query. This is the highest lower bound provable through the classic reduction to communication complexity. However, for space n lg O(1)n, we now obtain bounds of Omega(d/ lg d). This is a significant improvement for natural values of d, such as lgO(1) n. In the most important case of d = Theta (lg n), we have the first superconstant lower bound. From a complexity-theoretic perspective, our lower bounds are the highest known for any static data-structure problem, significantly improving on previous records

Published in:

Foundations of Computer Science, 2006. FOCS '06. 47th Annual IEEE Symposium on

Date of Conference:

Oct. 2006