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Extreme Optimization Numerical Libraries for .NET
Vector and Matrix Library Features
Below is a list of features for the vector and matrix library portion of the
Extreme Optimization Numerical Libraries for .NET.
Also see the detailed Mathematics and Statistics feature lists.
Download the trial version today!
General
- Double or (coming soon) single precision real or complex components.
- Based on standard BLAS and LAPACK routines.
- 100% managed implementation for security, portability and small sizes.
- Native, processor-optimized implementation for speed with large
sizes based on the Intel® Math Kernel Library.
- Native 64bit support.
Vectors
- General vectors.
- Band vectors.
- Constant vectors.
- Row, column and diagonal vectors.
- Vector views.
Vector Operations
- Basic arithmetic operations.
- Element-wise operations.
- Overloaded arithmetic operators.
- Norms, dot products.
- Largest and smallest values.
- Functions of vectors (sine, cosine, etc.)
Matrices
- General matrices.
- Triangular matrices.
- Real symmetric matrices and complex Hermitian matrices.
- Band matrices.
- Diagonal matrices.
- Matrix views.
Matrix Operations
- Basic arithmetic operations.
- Matrix-vector products.
- Overloaded arithmetic operations.
- Element-wise operations.
- Row and column scaling.
- Norms, rank, condition numbers.
- Singular values, eigenvalues and eigenvectors.
Matrix Decompositions
- LU decomposition.
- QR decomposition.
- Cholesky decomposition.
- Singular value decomposition.
- Symmetric eigenvalue decomposition.
- Non-symmetric eigenvalue decomposition.
- Banded LU and Cholesky decomposition.
- Non-negative matrix factorization (NMF) - Coming soon!
Sparse Matrices
- Sparse vectors
- Sparse matrices
- Matrices in Compressed Sparse Column format
- Sparse LU Decomposition
- Read matrices in Matrix Market format
Linear equations and least squares
- Shared API for matrices and decompositions.
- Determinants, inverses, numerical rank, condition numbers.
- Solve equations with 1 or multiple right-hand sides.
- Least squares solutions using QR or Singular
Value Decomposition.
- Moore-Penrose Pseudo-inverse.
- Non-negative least squares (NNLS) - Coming soon!
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