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Install

Install this package:

emerge -a sci-libs/dsdp

If the package is masked, you can unmask it using the autounmask tool or standard emerge options:

autounmask sci-libs/dsdp

Or alternatively:

emerge --autounmask-write -a sci-libs/dsdp

Package Information

Description:

The DSDP software is a free open source implementation of an interior-point method for semidefinite programming. It provides primal and dual solutions, exploits low-rank structure and sparsity in the data, and has relatively low memory requirements for an interior-point method. It allows feasible and infeasible starting points and provides approximate certificates of infeasibility when no feasible solution exists. The dual-scaling algorithm implemented in this package has a convergence proof and worst-case polynomial complexity under mild assumptions on the data. Furthermore, the solver offers scalable parallel performance for large problems and a well documented interface. Some of the most popular applications of semidefinite programming and linear matrix inequalities (LMI) are model control, truss topology design, and semidefinite relaxations of combinatorial and global optimization problems.

Homepage:

https://www.mcs.anl.gov/hs/software/DSDP/

License:

BSD

Versions

Metadata

Description

• The DSDP software is a free open source implementation of an interior-point method for semidefinite programming. It provides primal and dual solutions, exploits low-rank structure and sparsity in the data, and has relatively low memory requirements for an interior-point method. It allows feasible and infeasible starting points and provides approximate certificates of infeasibility when no feasible solution exists. The dual-scaling algorithm implemented in this package has a convergence proof and worst-case polynomial complexity under mild assumptions on the data. Furthermore, the solver offers scalable parallel performance for large problems and a well documented interface. Some of the most popular applications of semidefinite programming and linear matrix inequalities (LMI) are model control, truss topology design, and semidefinite relaxations of combinatorial and global optimization problems.

Maintainers

• Gentoo Science Project (sci@gentoo.org) - Type: project

<pkgmetadata>
	<maintainer type="project">
		<email>sci@gentoo.org</email>
		<name>Gentoo Science Project</name>
	</maintainer>
	<longdescription lang="en">
  The DSDP software is a free open source implementation of an
  interior-point method for semidefinite programming. It provides
  primal and dual solutions, exploits low-rank structure and sparsity
  in the data, and has relatively low memory requirements for an
  interior-point method. It allows feasible and infeasible starting
  points and provides approximate certificates of infeasibility when
  no feasible solution exists. The dual-scaling algorithm implemented
  in this package has a convergence proof and worst-case polynomial
  complexity under mild assumptions on the data. Furthermore, the
  solver offers scalable parallel performance for large problems and a
  well documented interface. Some of the most popular applications of
  semidefinite programming and linear matrix inequalities (LMI) are
  model control, truss topology design, and semidefinite relaxations
  of combinatorial and global optimization problems.
  </longdescription>
</pkgmetadata>

Lint Warnings

• [Warning] USE flag 'doc' in ebuild 5.8-r4 is not documented in metadata.xml
• [Warning] USE flag 'examples' in ebuild 5.8-r4 is not documented in metadata.xml
• [Error] Missing md5-cache for ebuild dsdp-5.8-r4

USE Flags

Manage flags for this package: euse -i <flag> -p sci-libs/dsdp | euse -E <flag> -p sci-libs/dsdp | euse -D <flag> -p sci-libs/dsdp

Files

• dsdp-5.8-gold.patch
• dsdp-5.8-malloc.patch
• dsdp-5.8-readsdpa.patch

Manifest