Eigenvalues eigenvectors calculation
Eigenvalues Eigenvectors

Eigenvalues and Eigenvectors calculation in just one line
of your source code

Eigenvalue and Eigenvector calculation is just one aspect of matrix algebra that is featured in the new Advanced edition of Matrix ActiveX Component (MaXC).

Supported operations include:

  • Eigen Problems
    • Eigenvalue Eigenvector of symmetric and non- symmetric matrices *
  • Matrix Decompositions
    • LU Decomposition
    • Cholesky Decomposition
    • QR Decomposition *
    • Singular Value Decomposition (SVD) *
  • Basic Matrix Operations
    • Addition
    • Subtraction
    • Scalar Multiplication
    • Normalize
    • Transpose
  • Common matrix operations
    • Multiplication
    • Inversion
    • Determinant
  • Simultaneous Equation Solving
    • Using matrix inversion
    • Using Gauss elimination

(*) Available in the Advanced editions

The Matrix ActiveX Component (MaXC) delivers all the speed, numerical stability, robustness and scalability you could ask for, significantly reducing development time and allowing you to focus on the actual goals of your application. Without compromising speed, MaXC uses an object-oriented approach to matrix math computation, providing you with the Matrix and CMatrix objects containing all the Properties and Methods that you'll need when deploying your numerical exploration.

It can be used from within any programming language that supports COM, such as Visual Basic, .NET Basic, Vbscript, ASP, JavaScript, C++, C#, Delphi, Excel's VBA.

Download a Free Trial Now!

Use it in your source code and see how the Matrix ActiveX Component simplifies your coding tasks.

Whether you are developing a statistical package, seeking fast 3D graphics transformations, looking to solve simultaneous equations, multivariate linear regressions, eigenvalue and eigenvector calculation problems, the Matrix ActiveX Component can do all of this and more!

Finally, you can discover for yourself just how useful and versatile the Matrix ActiveX Component is in all types of matrix math calculations, as well as in its ability to calculate eigenvalue and eigenvector.