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Title: Large-Scale Quasi-Newton Trust-Region Methods With Low-Dimensional Linear Equality Constraints

Abstract

We propose two limited-memory BFGS (L-BFGS) trust-region methods for large-scale optimization with linear equality constraints. Here, the methods are intended for problems where the number of equality constraints is small. By exploiting the structure of the quasi-Newton compact representation, both proposed methods solve the trust-region subproblems nearly exactly, even for large problems. We derive theoretical global convergence results of the proposed algorithms, and compare their numerical effectiveness and performance on a variety of large-scale problems.

Authors:
ORCiD logo [1];  [2];  [3]
  1. Argonne National Lab. (ANL), Argonne, IL (United States)
  2. Univ. of California, Merced, CA (United States)
  3. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Argonne National Laboratory (ANL), Argonne, IL (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program; National Science Foundation (NSF); USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1596681
Alternate Identifier(s):
OSTI ID: 1575872
Report Number(s):
LLNL-JRNL-755231
Journal ID: ISSN 0926-6003; 154974
Grant/Contract Number:  
AC02-06CH11357; AC52-07NA27344
Resource Type:
Accepted Manuscript
Journal Name:
Computational Optimization and Applications
Additional Journal Information:
Journal Volume: 74; Journal Issue: 3; Journal ID: ISSN 0926-6003
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; L-BFGS; Compact Representation; Eigendecomposition; Linear Equality Constraints; Quasi-Newton; Shape-Changing Norm; Trust-Region Algorithm; Linear equality constraints; Trust-region algorithm; Compact representation; Shape-changing norm

Citation Formats

Brust, Johannes J., Marcia, Roummel F., and Petra, Cosmin G. Large-Scale Quasi-Newton Trust-Region Methods With Low-Dimensional Linear Equality Constraints. United States: N. p., 2019. Web. doi:10.1007/s10589-019-00127-4.
Brust, Johannes J., Marcia, Roummel F., & Petra, Cosmin G. Large-Scale Quasi-Newton Trust-Region Methods With Low-Dimensional Linear Equality Constraints. United States. https://doi.org/10.1007/s10589-019-00127-4
Brust, Johannes J., Marcia, Roummel F., and Petra, Cosmin G. Thu . "Large-Scale Quasi-Newton Trust-Region Methods With Low-Dimensional Linear Equality Constraints". United States. https://doi.org/10.1007/s10589-019-00127-4. https://www.osti.gov/servlets/purl/1596681.
@article{osti_1596681,
title = {Large-Scale Quasi-Newton Trust-Region Methods With Low-Dimensional Linear Equality Constraints},
author = {Brust, Johannes J. and Marcia, Roummel F. and Petra, Cosmin G.},
abstractNote = {We propose two limited-memory BFGS (L-BFGS) trust-region methods for large-scale optimization with linear equality constraints. Here, the methods are intended for problems where the number of equality constraints is small. By exploiting the structure of the quasi-Newton compact representation, both proposed methods solve the trust-region subproblems nearly exactly, even for large problems. We derive theoretical global convergence results of the proposed algorithms, and compare their numerical effectiveness and performance on a variety of large-scale problems.},
doi = {10.1007/s10589-019-00127-4},
journal = {Computational Optimization and Applications},
number = 3,
volume = 74,
place = {United States},
year = {Thu Sep 05 00:00:00 EDT 2019},
month = {Thu Sep 05 00:00:00 EDT 2019}
}

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Works referenced in this record:

Computing a Trust Region Step
journal, September 1983

  • Moré, Jorge J.; Sorensen, D. C.
  • SIAM Journal on Scientific and Statistical Computing, Vol. 4, Issue 3
  • DOI: 10.1137/0904038

A trust region method based on interior point techniques for nonlinear programming
journal, November 2000

  • Byrd, Richard H.; Gilbert, Jean Charles; Nocedal, Jorge
  • Mathematical Programming, Vol. 89, Issue 1
  • DOI: 10.1007/PL00011391

A trust region algorithm for equality constrained optimization
journal, November 1990

  • Powell, M. J. D.; Yuan, Y.
  • Mathematical Programming, Vol. 49, Issue 1-3
  • DOI: 10.1007/BF01588787

Compact representation of the full Broyden class of quasi-Newton updates: Compact Representation of the Full Broyden Class of Quasi-Newton Updates
journal, May 2018

  • DeGuchy, Omar; Erway, Jennifer B.; Marcia, Roummel F.
  • Numerical Linear Algebra with Applications, Vol. 25, Issue 5
  • DOI: 10.1002/nla.2186

On the Implementation of an Algorithm for Large-Scale Equality Constrained Optimization
journal, August 1998

  • Lalee, Marucha; Nocedal, Jorge; Plantenga, Todd
  • SIAM Journal on Optimization, Vol. 8, Issue 3
  • DOI: 10.1137/S1052623493262993

On efficiently combining limited-memory and trust-region techniques
journal, June 2016

  • Burdakov, Oleg; Gong, Lujin; Zikrin, Spartak
  • Mathematical Programming Computation, Vol. 9, Issue 1
  • DOI: 10.1007/s12532-016-0109-7

A Trust Region Algorithm for Equality Constrained Minimization: Convergence Properties and Implementation
journal, June 1985

  • Vardi, Avi
  • SIAM Journal on Numerical Analysis, Vol. 22, Issue 3
  • DOI: 10.1137/0722035

On solving L-SR1 trust-region subproblems
journal, September 2016

  • Brust, Johannes; Erway, Jennifer B.; Marcia, Roummel F.
  • Computational Optimization and Applications, Vol. 66, Issue 2
  • DOI: 10.1007/s10589-016-9868-3

The Conjugate Gradient Method and Trust Regions in Large Scale Optimization
journal, June 1983

  • Steihaug, Trond
  • SIAM Journal on Numerical Analysis, Vol. 20, Issue 3
  • DOI: 10.1137/0720042

A dense initialization for limited-memory quasi-Newton methods
journal, May 2019

  • Brust, Johannes; Burdakov, Oleg; Erway, Jennifer B.
  • Computational Optimization and Applications, Vol. 74, Issue 1
  • DOI: 10.1007/s10589-019-00112-x

Benchmarking optimization software with performance profiles
journal, January 2002

  • Dolan, Elizabeth D.; Moré, Jorge J.
  • Mathematical Programming, Vol. 91, Issue 2
  • DOI: 10.1007/s101070100263

Algorithm 943: MSS: MATLAB Software for L-BFGS Trust-Region Subproblems for Large-Scale Optimization
journal, June 2014

  • Erway, Jennifer B.; Marcia, Roummel F.
  • ACM Transactions on Mathematical Software, Vol. 40, Issue 4
  • DOI: 10.1145/2616588

Updating the Inverse of a Matrix
journal, June 1989

  • Hager, William W.
  • SIAM Review, Vol. 31, Issue 2, p. 221-239
  • DOI: 10.1137/1031049

On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
journal, April 2005


An Interior Point Algorithm for Large-Scale Nonlinear Programming
journal, January 1999

  • Byrd, Richard H.; Hribar, Mary E.; Nocedal, Jorge
  • SIAM Journal on Optimization, Vol. 9, Issue 4
  • DOI: 10.1137/S1052623497325107

Compact representation of the full Broyden class of quasi-Newton updates
preprint, January 2017


Benchmarking Optimization Software with Performance Profiles
text, January 2001


Representations of quasi-Newton matrices and their use in limited memory methods
journal, January 1994

  • Byrd, Richard H.; Nocedal, Jorge; Schnabel, Robert B.
  • Mathematical Programming, Vol. 63, Issue 1-3
  • DOI: 10.1007/bf01582063

The Conjugate Gradient Method and Trust Regions in Large Scale Optimization
journal, June 1983

  • Steihaug, Trond
  • SIAM Journal on Numerical Analysis, Vol. 20, Issue 3
  • DOI: 10.1137/0720042