• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations
Advanced Search Include Citations | Disambiguate

DMCA

SNOPT: An SQP Algorithm For Large-Scale Constrained Optimization (2002)

Cached

  • Download as a PDF

Download Links

  • [www.scicomp.ucsd.edu]
  • [sdna3.ucsd.edu]
  • [www.stanford.edu]
  • [www-leland.stanford.edu]
  • [web.stanford.edu]
  • [www.ccom.ucsd.edu]
  • [www.stanford.edu]
  • [www-leland.stanford.edu]

  • Save to List
  • Add to Collection
  • Correct Errors
  • Monitor Changes
by Philip E. Gill , Walter Murray , Michael A. Saunders
Citations:571 - 23 self
  • Summary
  • Citations
  • Active Bibliography
  • Co-citation
  • Clustered Documents
  • Version History

BibTeX

@MISC{Gill02snopt:an,
    author = {Philip E. Gill and Walter Murray and Michael A. Saunders},
    title = {SNOPT: An SQP Algorithm For Large-Scale Constrained Optimization},
    year = {2002}
}

Share

Facebook Twitter Reddit Bibsonomy

OpenURL

 

Abstract

Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first derivatives are available, and that the constraint gradients are sparse. We discuss

Keyphrases

large-scale constrained optimization    sqp algorithm    constraint gradient    general inequality constraint    smooth nonlinear function    sequential quadratic programming    first derivative    constrained optimization problem   

Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University