dsdp 5.8-10.1build1 source package in Ubuntu

Changelog

dsdp (5.8-10.1build1) noble; urgency=medium

  * No-change rebuild for CVE-2024-3094

 -- Steve Langasek <email address hidden>  Sun, 31 Mar 2024 18:04:44 +0000

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Uploaded by:
Steve Langasek
Uploaded to:
Noble
Original maintainer:
Ubuntu Developers
Architectures:
any all
Section:
science
Urgency:
Medium Urgency

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Oracular release universe science
Noble release universe science

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File Size SHA-256 Checksum
dsdp_5.8.orig.tar.gz 360.0 KiB de82af5e2daec70c8bf653ea4872108850bebea25238a799e78289ff88f88e06
dsdp_5.8-10.1build1.debian.tar.xz 7.4 KiB 599dde7a636a0394c49ea22e8d3d94a4a4463501cf007701659ce9b9f448b3d0
dsdp_5.8-10.1build1.dsc 2.1 KiB ebcc0b06feadc0c778445b951107c3d4b4f9750c0ea25428a7e37155402115cd

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Binary packages built by this source

dsdp: Software for Semidefinite Programming

 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.
 .
 This package contains the binaries.

dsdp-dbgsym: debug symbols for dsdp
dsdp-doc: Software for Semidefinite Programming

 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.
 .
 This package contains the documentation and examples.

libdsdp-5.8t64: Software for Semidefinite Programming

 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.
 .
 This package contains the library files.

libdsdp-5.8t64-dbgsym: debug symbols for libdsdp-5.8t64
libdsdp-dev: Software for Semidefinite Programming

 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.
 .
 This package contains the header files for developers.