close
Skip to main content
Springer Nature Link
Log in
Menu
Find a journal Publish with us Track your research
Search
Saved research
Cart
  1. Home
  2. Discrete & Computational Geometry
  3. Article

Bounded-Independence Derandomization of Geometric Partitioning with Applications to Parallel Fixed-Dimensional Linear Programming

  • Published: December 1997
  • Volume 18, pages 397–420 (1997)
  • Cite this article
Download PDF
Save article
View saved research
Discrete & Computational Geometry Aims and scope Submit manuscript
Bounded-Independence Derandomization of Geometric Partitioning with Applications to Parallel Fixed-Dimensional Linear Programming
Download PDF
  • M. T. Goodrich1 &
  • E. A. Ramos2 
  • 389 Accesses

  • 10 Citations

  • Explore all metrics

Abstract.

We give fast and efficient methods for constructing ε-nets and ε-approximations for range spaces with bounded VC-exponent. These combinatorial structures have wide applicability to geometric partitioning problems, which are often used in divide-and-conquer constructions in computational geometry algorithms. In addition, we introduce a new deterministic set approximation for range spaces with bounded VC-exponent, which we call the δ-relative ε-approximation, and we show how such approximations can be efficiently constructed in parallel. To demonstrate the utility of these constructions we show how they can be used to solve the linear programming problem in \({\Bbb R}^d\) deterministically in \(O((\log\log n)^d)\) time using linear work in the PRAM model of computation, for any fixed constant d. Our method is developed for the CRCW variant of the PRAM parallel computation model, and can be easily implemented to run in \(O(\log n(\log\log n)^{d-1})\) time using linear work on an EREW PRAM.

Article PDF

Download to read the full article text

Similar content being viewed by others

Sparse Harmonic Transforms: A New Class of Sublinear-Time Algorithms for Learning Functions of Many Variables

Article 24 June 2020

Efficient Second-Order Shape-Constrained Function Fitting

Chapter © 2019

Low-CP-Rank Tensor Completion via Practical Regularization

Article Open access 01 March 2022

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.
  • Algorithms
  • Combinatorial Geometry
  • Computational Geometry
  • Computational Complexity
  • Data Structures and Information Theory
  • Mathematics and Computing

Author information

Authors and Affiliations

  1. Center for Geometric Computing, Johns Hopkins University, Baltimore, MD 21218, USA goodrich@cs.jhu.edu , , , , , , US

    M. T. Goodrich

  2. DIMACS, Rutgers University, Piscataway, NJ 08855-1179, USA ramose@dimacs.rutgers.edu, , , , , , US

    E. A. Ramos

Authors
  1. M. T. Goodrich
    View author publications

    Search author on:PubMed Google Scholar

  2. E. A. Ramos
    View author publications

    Search author on:PubMed Google Scholar

Additional information

Received August 7, 1995, and in revised form November 11, 1996.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Goodrich, M., Ramos, E. Bounded-Independence Derandomization of Geometric Partitioning with Applications to Parallel Fixed-Dimensional Linear Programming . Discrete Comput Geom 18, 397–420 (1997). https://doi.org/10.1007/PL00009325

Download citation

  • Issue date: December 1997

  • DOI: https://doi.org/10.1007/PL00009325

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Computation Model
  • Programming Problem
  • Efficient Method
  • Parallel Computation
  • Linear Programming Problem

Advertisement

Search

Navigation

  • Find a journal
  • Publish with us
  • Track your research

Footer Navigation

Discover content

  • Journals A-Z
  • Books A-Z
  • Subjects A-Z

Publish with us

  • Journal finder
  • Publish your research
  • Language editing
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our brands

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Discover

Corporate Navigation

  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support
  • Legal notice
  • Cancel contracts here

104.23.197.170

Not affiliated

Springer Nature

© 2026 Springer Nature