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Our customers

This is a partial list of companies who are using our libraries:

ABB Robotics
Allstate
Arcam
Astra Schedule
Babson College
Canadian Council on Learning
Canyon Associates
Caxton Associates
CECity
Constellation Energy
CreditSights
DeepOcean
Duke University
Dynamotive
Elecsoft
Engelhard Corporation
Epcor
Equipoise Software
Galileo International
GAM UK
Gammex
GlaxoSmithKline
Global Matrix
The Hartford
Infinera Corporation
Intel
JDS Uniphase
LaBranche & Co.
Learning & Skills Council
Jacobs Consultancy
Litman Gregory
Lucas Systems
Malvern Instruments
Medrio
Merck & Co.
Mintera.
Monitor Software
MorningStar
NanoString Technologies
Paletta Invent
Parametric Portfolio Associates
Prosanos
RATA Associates
RiskShield
Ramboll
Standard & Poor's
Strategic Analysis Corporation
Univ. of Alicante
Univ. of South Carolina
vielife
Xerox
US Army

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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.

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General

  • Single, double, or quad 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.

GPU computing

  • GPU computing: offload computations to the GPU.
  • Data is kept on the GPU as long as possible for optimal performance.

Vectors

  • Dense 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)