**39.1****
****Supported Features** (.NET, C#, CSharp, VB, Visual Basic, F#)

Only selected **NMath**
classes are able to route their computations to the graphics processor.
The directly supported features for GPU acceleration of linear algebra
(dense systems) include:

● Singular value decomposition (SVD)

● QR decomposition

● Eigenvalue routines

● Solve Ax = B

GPU acceleration for signal processing includes:

● 1D Fast
Fourier Transforms (Complex data input)^{1}

● 2D Fast Fourier Transforms (Complex data input)

Of course, many higher-level **NMath**
and **NMath Stats** classes make
use of these functions internally, and so also benefit from GPU acceleration
indirectly.

**NMath**

● Least squares, including weighted least squares

● Filtering, such as moving window filters and Savitsky-Golay

● Nonlinear programming (NLP)

● Ordinary differential equations (ODE)

**NMath Stats**

● Two-Way ANOVA, with or without repeated measures

● Factor Analysis

● Linear regression and logistic regression

● Principal component analysis (PCA)

● Partial least squares (PLS)

● Nonnegative matrix factorization (NMF)