SVD

The program is based on the powerful model-free method of time series analysis Caterpillar-SSA (Singular Spectrum Analysis). The result of the Caterpillar-SSA processing is identification, analysis and forecast of additive components of time series (trends, periodicities, noise). The program can be applied to multivariate analysis/forecasting and change-point detection.

The Matrix ActiveX Component simplifies the use of matrix operations in application development. It provides for matrix operations such as addition, subtraction, multiplication, inversion, transpose, 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 real or complex matrices.

The Bluebit .NET Matrix Library provides classes for object-oriented linear algebra in the .NET platform. It can be used to solve systems of simultaneous linear equations, least-squares solutions of linear systems of equations, eigenvalues and eigenvectors problems, and singular value problems. Also provided are the associated matrix factorizations such as Eigen, LQ, LU, Cholesky, QR, SVD. Both real and complex matrices are supported.

GoogleIzer is a meta-search tool that gathers Google results, and then clusters, indexes, and sorts the results. Clusters are groups of related search results. The index is automatically built from the search results. Marking interesting results or index words automatically finds related results and words. If you use Google a lot, then try GoogleIzer. It speeds up your Google searching and allows you to sort through more results in less time.

SearchAlyzer2 simultaneously queries the common search engines and then clusters, indexes, and sorts those search results. Clusters are groups of related search results. The index is automatically built from the search results. In addition, marking interesting search results or index words finds related results or words, as measured by updated relevance bars.

The first meta search tool based on singular value decomposition or SVD. SVD is a powerful method for finding patterns in data used in, for example, Latent Semantic Indexing or LSI. iMetaSearch uses SVD for clustering results and building a relevance network that shows how related index words and results are to each other. By clicking an index word or result, you can instantly see other related index words and results.