spectra 1.0.1-3 source package in Ubuntu
Changelog
spectra (1.0.1-3) unstable; urgency=medium * Marking the binary package as Multi-Arch: foreign (Closes: #1034414) * Raising Standards version to 4.6.2 (no change) -- Pierre Gruet <email address hidden> Wed, 14 Jun 2023 06:51:25 +0200
Upload details
- Uploaded by:
- Debian Math Team
- Uploaded to:
- Sid
- Original maintainer:
- Debian Math Team
- Architectures:
- all
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section | |
|---|---|---|---|---|
| Oracular | release | universe | misc | |
| Noble | release | universe | misc | |
| Mantic | release | universe | misc |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| spectra_1.0.1-3.dsc | 1.9 KiB | 39251fb4de4b745d91f3b267441b4b9a5c6a83bbf96c4cd0dad6f07e43ea8fdb |
| spectra_1.0.1.orig.tar.gz | 253.9 KiB | 919e3fbc8c539a321fd5a0766966922b7637cc52eb50a969241a997c733789f3 |
| spectra_1.0.1-3.debian.tar.xz | 4.5 KiB | 24461167ff0bdf3c2e9b94395a1871821f3e003f2ce43f6d352900dea615c610 |
Available diffs
- diff from 1.0.1-2 to 1.0.1-3 (637 bytes)
No changes file available.
Binary packages built by this source
- libspectra-dev: library for large scale eigenvalue problems (development files)
Spectra stands for Sparse Eigenvalue Computation Toolkit as a Redesigned
ARPACK. It is a C++ library for large scale eigenvalue problems, built on top
of Eigen, an open source linear algebra library.
.
Spectra is implemented as a header-only C++ library, whose only dependency,
Eigen, is also header-only. Hence Spectra can be easily embedded in C++
projects that require calculating eigenvalues of large matrices.
.
Spectra is designed to calculate a specified number of eigenvalues of a large
square matrix. Usually this number of eigenvalues is much smaller than the
size of the matrix, so that only a few eigenvalues and eigenvectors are
computed, which in general is more efficient than calculating the whole
spectral decomposition. Users can choose eigenvalue selection rules to pick
the eigenvalues of interest, such as the largest k eigenvalues, or eigenvalues
with largest real parts, etc.
