dsdp 5.8-10.1 source package in Ubuntu

Changelog

dsdp (5.8-10.1) unstable; urgency=medium

  * Non-maintainer upload.
  * Rename libraries for 64-bit time_t transition.  Closes: #1062389

 -- Michael Hudson-Doyle <email address hidden>  Wed, 28 Feb 2024 02:28:49 +0000

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Uploaded by:
Debian QA Group
Uploaded to:
Sid
Original maintainer:
Debian QA Group
Architectures:
any all
Section:
science
Urgency:
Medium Urgency

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dsdp_5.8-10.1.dsc 2.0 KiB 4ccbb58d4c36cc33e5732d7a9cc800a0c64a193f8747eb74e744c2a8ad0d658d
dsdp_5.8.orig.tar.gz 360.0 KiB de82af5e2daec70c8bf653ea4872108850bebea25238a799e78289ff88f88e06
dsdp_5.8-10.1.debian.tar.xz 7.3 KiB a57f2f10ec2dae16a7a2e3bfc972bdd670fa3a41e2a1d3117d149cefeb1254e6

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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.