Software for distributed-memory dense and sparse-direct linear algebra and optimization which supports a wide range of functionality not available elsewhere. The current recommended release of Elemental is version 0.87.7. The documentation will soon be updated to reflect the large amount of functionality added in this release.
http://www.libelemental.org/
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Elemental: distributed-memory dense and sparse-direct linear algebra and optimization — Elemental | libelemental.org Reviews
https://libelemental.org
Software for distributed-memory dense and sparse-direct linear algebra and optimization which supports a wide range of functionality not available elsewhere. The current recommended release of Elemental is version 0.87.7. The documentation will soon be updated to reflect the large amount of functionality added in this release.
libelemental.org
Supporting grants — Elemental
http://libelemental.org/support
AFRL Contract FA8750-12-2-0306 (2015). AFRL Contract FA8750-13-C-0002 (2014). Award ACI-1148125/1340293 (supplement): Collaborative Research: SI2-SSI: A Linear Algebra Software Infrastructure for Sustained Innovation in Computational Chemistry and other Sciences. A number of research institutions have either directly or indirectly helped support the development of Elemental since its development began in 2009. Several advanced computing facilities have been gracious enough to provide compute time for Ele...
Elemental’s development documentation — Elemental
http://libelemental.org/documentation/dev/index.html
Handling mandatory external dependencies. Getting Elemental’s source. Testing the C 11 installation. Testing the Python installation. Elemental as a CMake subproject. Reduction to condensed form. Elemental’s development documentation. Elemental’s development documentation. Handling mandatory external dependencies. Getting Elemental’s source. Testing the C 11 installation. Testing the Python installation. Elemental as a CMake subproject. Reduction to condensed form. Provided by Read the Docs.
Contributing — Elemental
http://libelemental.org/contributing
Elemental is an open source project and contributions are always welcome. This page contains information for prospective Elemental developers, including an overview of the organization of the current code base, how to write code and documentation, and how to use the tools like Git. This role is traditionally referred to as the Benevolent Dictator. As more developers begin to more frequently contribute high-quality changesets to the library, Elemental will move towards a more distributed decision-maki...
Getting help — Elemental
http://libelemental.org/help
This page describes how to get in contact to solve problems you might encounter when using Elemental. All of the following lists will eventually be archived at news.gmane.org. Where to ask questions. General usage questions should be directed to users @. Which will soon be archived here. Several questions have also been posted on SciComp. And an interface similar to fenics-qa. Yes, I want to become a member of the mailing list users@libelemental.org . Enter your e-mail address:. Enter your e-mail address:.
About Elemental — Elemental
http://libelemental.org/about
Elemental is an open-source library for distributed-memory dense and sparse-direct linear algebra and optimization which builds on top of BLAS, LAPACK, and MPI using modern C and additionally exposes interfaces to C and Python (with a Julia interface beginning development). The development of Elemental has led to a number of. And a number of related projects, such as the parallel sweeping preconditioner, PSP. And a parallel algorithm for Low-rank Plus Sparse MRI, RT-LPS-MRI. Least Squares / Minimum Length.
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Install Guide — CVXPY 0.4.3 documentation
http://www.cvxpy.org/en/latest/install/index.html
To update CVXPY, first update NumPy and SciPy separately. Then run. Can cause errors, especially if you’re using Anaconda. The following instructions assume you already have Python installed. CVXPY supports both Python 2 and Python 3. We recommend using Anaconda. Rather than the Python that comes with the Mac and installing pip, nose, NumPy, SciPy, and CVXOPT through Anaconda. But it is not necessary to have Anaconda. To install CVXPY, and the instructions below assume you do not have Anaconda. There are...
[LMM] literature overview: performance
http://lomereiter.github.io/2015/03/29/performance.html
LMM] literature overview: performance. March 29, 2015. LMM] literature overview: performance. March 27, 2015. LMM] literature overview: approximate methods. March 15, 2015. March 13, 2015. March 11, 2015. FaST-LMM] comparison with PyLMM (continued). March 10, 2015. FaST-LMM] comparison with PyLMM (practice). March 9, 2015. FaST-LMM] comparison with PyLMM (theory). March 3, 2015. FaST-LMM] fastlmm/inference/lmm cov.py, part 2. February 27, 2015. FaST-LMM] high-level overview, part 2. February 25, 2015.
Documentation
http://smallk.github.io/documentation
High-dimensional data sets are ubiquitous in data science, and they often present serious problems for researchers. Our work in dimensionality reduction focuses on, but is not limited to, low rank approximations via nonnegative matrix factorization (NMF) [see Publications [1,2]. NMF is a non-convex optimization problem with important applications in data and interactive visual analytics of high-dimensional data. Will provide the information required to quickly begin real work. A ≅ W H. 1 Hierarchical Clu...
Google Project Hosting
http://code.google.com/p/elemental
Project "elemental" has moved to another location on the Internet. View the project at: http:/ libelemental.org. For pages about "elemental". If you are the project's administrator, you can update the new project URL here. Powered by Google Project Hosting.
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Elemental: distributed-memory dense and sparse-direct linear algebra and optimization — Elemental
Software for distributed-memory dense and sparse-direct linear algebra and optimization which supports a wide range of functionality not available elsewhere. The current recommended release of Elemental is version 0.87.7. The documentation will soon be updated to reflect the large amount of functionality added in this release.
LibELesS
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