Jblas: Linear Algebra for Java
 | The MadmanBot has performed a web search with the contents of this page, and it appears to include material copied directly from:
It will soon be reviewed to determine if there are any copyright issues. The content should not be mirrored or otherwise reused until the issue has been resolved. If substantial content is duplicated, unless evidence is provided to the contrary (e.g. evidence of permission to use this content under terms consistent with the Wikimedia Terms of Use or public domain status; see Wikipedia:Donating copyrighted materials), editors will assume that this text is a copyright violation, and will soon delete the copy.
Before removing this notice, you should:
|
| Jblas: Linear Algebra for Java | |
|---|---|
| Original author(s) | Mikio L. Braun |
| Stable release | 1.2.3
/ February 13, 2013 |
| Operating system | Cross-platform |
| Type | Library |
| License | BSD Revised |
| Website | jblas |
jblas is a linear algebra library for the Java programming language. jblas is a fast library due to its use of native implementations of BLAS and LAPACK, which are are the de-facto industry standard for matrix computations. jblas improved upon the archaic API found in BLAS and LAPACK (an artifact of them originating from the Fortran community) by providing an easy to use high level interface for Java users. jblas aims to make this functionality available to Java programmers such that they do not have to worry about writing JNI interfaces and calling conventions of Fortran code[1].
Capabilities
The following is an overview of jblas's capabilities, as listed on the project's website:[1]
- Eigen – eigendecomposition
- Solve – solving linear equations
- Singular – singular value decomposition
- Decompose – decompositions like LU, Cholesky, ...
- Geometry – centering, normalizing, ...
Usage Example
Example of Eigenvalue Decomposition:
DoubleMatrix[] evd = Eigen.symmetricEigenvectors(matA);
DoubleMatrix V = evd[0];
DoubleMatrix D = evd[1];
Example of matrix multiplication:
DoubleMatrix result = matA.mmul(matB);