Matrix ActiveX Component 3.1
Matrix ActiveX Component provides for all basic matrix operations plus LU, Cholesky, QR and Singular Value (SVD) decompositions, Eigenvalue and Eigenvector computation for symmetric and non symmetric matrices. Version 3.1 supports complex matrices.
Product Specifications
| User Rating: | item has less then 1 vote |
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| FileCart Rating | ||
| Submitted by: | trifont | |
| Homepage | Visit Homepage | |
| License | Demo | |
| Price | $49 | |
| Downloads | 650 | |
| OS Support | Windows Install and Uninstall | |
| File Name | maxc31.zip | |
| File Size | 0.46 MB ( 467 KB ) | |
| Published | Jan 10, 2005 (21 years ago) | |
| Updated | Dec 23, 2013 (12 years ago) | |
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Product Details
The Matrix ActiveX Component is a useful tool that can simplify the use of matrix operations for mathematical computations in application development. It provides for matrix operations such as addition, subtraction, multiplication, inversion, transpose, and computation of determinant, LU and Cholesky decompositions. Advanced edition additionally supports QR and Singular Value (SVD) decompositions, Eigenvalue and Eigenvector computation for symmetric and non symmetric matrices. Advanced II edition now supports operations with complex matrices. The Matrix ActiveX Component 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, it uses an object-oriented approach to matrix math computation, it provides you with the Matrix and CMatrix Classes containing all the Properties, Methods that you'll need when deploying your numerical exploration.You may be developing a statistical package, or you might want fast 3D graphics transformations, or you may need to solve simultaneous equations, multivariate linear regressions, or to solve EigenValues and EigenVectors problems, Matrix ActiveX Component can do all of this and more.
| Release Info: | Major Update on Mar 01, 2004 |
| Related Tags: | matrix math eigenvalues eigenvalue eigenvectors |
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