NDLI: Algorithm 896: LSA: Algorithms for large-scale optimization

Content Provider	ACM Digital Library
Author	Matonoha, Ctirad Lukšan, Ladislav Vlček, Jan
Copyright Year	2009
Abstract	We present 14 basic Fortran subroutines for large-scale unconstrained and box constrained optimization and large-scale systems of nonlinear equations. Subroutines PLIS and PLIP, intended for dense general optimization problems, are based on limited-memory variable metric methods. Subroutine PNET, also intended for dense general optimization problems, is based on an inexact truncated Newton method. Subroutines PNED and PNEC, intended for sparse general optimization problems, are based on modifications of the discrete Newton method. Subroutines PSED and PSEC, intended for partially separable optimization problems, are based on partitioned variable metric updates. Subroutine PSEN, intended for nonsmooth partially separable optimization problems, is based on partitioned variable metric updates and on an aggregation of subgradients. Subroutines PGAD and PGAC, intended for sparse nonlinear least-squares problems, are based on modifications and corrections of the Gauss-Newton method. Subroutine PMAX, intended for minimization of a maximum value (minimax), is based on the primal line-search interior-point method. Subroutine PSUM, intended for minimization of a sum of absolute values, is based on the primal trust-region interior-point method. Subroutines PEQN and PEQL, intended for sparse systems of nonlinear equations, are based on the discrete Newton method and the inverse column-update quasi-Newton method, respectively. Besides the description of methods and codes, we propose computational experiments which demonstrate the efficiency of the proposed algorithms.
Starting Page	1
Ending Page	29
Page Count	29
File Format	PDF
ISSN	00983500
e-ISSN	15577295
DOI	10.1145/1527286.1527290
Volume Number	36
Issue Number	3
Journal	ACM Transactions on Mathematical Software (TOMS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-07-23
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Large-scale optimization Discrete Newton methods Large-scale nonlinear least squares Large-scale nonlinear minimax Large-scale nonsmooth optimization Large-scale systems of nonlinear equations Limited-memory methods Partially separable problems Primal interior-point methods Quasi-Newton methods Sparse problems
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Software

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Algorithm 896: LSA: Algorithms for large-scale optimization

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Algorithm 896: LSA: Algorithms for large-scale optimization