dsdp 5.8-9.1ubuntu1 source package in Ubuntu
Changelog
dsdp (5.8-9.1ubuntu1) xenial; urgency=medium * Build using -O2 on s390x. LP: #1543982. -- Matthias Klose <email address hidden> Wed, 10 Feb 2016 11:28:13 +0100
Upload details
- Uploaded by:
- Matthias Klose
- Uploaded to:
- Xenial
- Original maintainer:
- Soeren Sonnenburg
- Architectures:
- any all
- Section:
- science
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| dsdp_5.8.orig.tar.gz | 360.0 KiB | de82af5e2daec70c8bf653ea4872108850bebea25238a799e78289ff88f88e06 |
| dsdp_5.8-9.1ubuntu1.debian.tar.xz | 6.2 KiB | fdb02e86f936cb2acc67e1782fe252c304b22eb252a284ad98380a8fbbe835ce |
| dsdp_5.8-9.1ubuntu1.dsc | 2.0 KiB | c982d16f73ccfd27f57538763249326be711da9d0d064c1632e720382b5f0469 |
Available diffs
- diff from 5.8-9.1build1 to 5.8-9.1ubuntu1 (767 bytes)
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-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-5.8gf: 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-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.
