Download .NET Matrix Library 32-bit Developer 5.0

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Free-To-Try.com Windows Development Components & Libraries .NET Matrix Library 32-bit Developer

.NET Matrix Library 32-bit Developer 5.0
.NET Matrix Library 32-bit Developer 5.0 screenshot
Provides classes for object-oriented linear algebra in the .NET platform.
Price: 399 USD (Buy Now) Hits: 200 visitors Publisher: Bluebit Software OS Support: Win2000, Win7 x32, Win7 x64, WinOther, WinServer, WinVista, WinXP License: Commercial Date added: 11 Oct 2012 Last Update: 30 Nov 2009 Downloads: 5

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.NET Matrix Library 32-bit Developer Publisher's description

Provides classes for object-oriented linear algebra in the .NET platform.

The Bluebit .NET Matrix Library provides classes for object-oriented linear algebra in the .NET platform. It can be used to solve systems of simultaneous linear equations, least-squares solutions of linear systems of equations, eigenvalues and eigenvectors problems, and singular value problems. Also provided are the associated matrix factorizations such as Eigen, LQ, LU, Cholesky, QR, SVD. The above functionality is present for both real and complex matrices. Two analogous sets of classes are provided for real and complex matrices, vectors and factorizations. While exposing an easy to use and powerful interface, the Bluebit .NET Matrix Library does not sacrifice any performance. Highly optimized BLAS and the standard LAPACK routines are used within the library and provide fast execution and accurate calculations. The Bluebit .NET Matrix Library has been developed as a mixed mode C++ project, combining together managed and unmanaged code and delivering the best of both worlds; the speed of native C++ code and the feature-rich and easy to use environment of the .NET Framework.

Version 5.0 adds support for sparse matrices.
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Changes on the new version:
Added support for sparse Matrices

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Version History
Version 5.0 added on: 11 Oct 2012


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Related Tags
    matrix          vector          math          eigenvalues          eigenvalue          eigenvectors          eigenvector          qr          cholesky          lu          decomposition          svd          net      

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