FFTW is a C subroutine library for computing the discrete Fourier transform (DFT) in one or more dimensions, of arbitrary input size, and of both real and complex data (as well as of even/odd data, i.e. the discrete cosine/sine transforms or DCT/DST). We believe that FFTW, which is free software, should become the FFT library of choice for most applications.
# R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To download R, please choose your preferred CRAN mirror. # If you have questions about R like how to do
The goal of the JEM thematic network is to pool together the required expertise and to contribute to the coordination of content enrichment activities in the area of mathematics, to the maintenance of agreed standards and to the delivery of powerful synoptic high-quality user information and support pages, invoked in e-learning platforms operated by the partners.
This is a tutorial on vector algebra and matrix algebra from the viewpoint of computer graphics. It covers most vector and matrix topics needed to read college-level computer graphics text books. Most graphics texts cover these subjects in an appendix, but it is often too short. This tutorial covers the same material at greater length, and with many examples.
If you studied math, science, or engineering at a four-year college in the US, much of what you learned is useless, forgotten, or obsolete. All that money, all that time, all that wasted talent. If all we lost were a few years, no big deal. But the really
J. Berner, P. Grohs, G. Kutyniok, and P. Petersen. (2021)cite arxiv:2105.04026Comment: This review paper will appear as a book chapter in the book "Theory of Deep Learning" by Cambridge University Press.
J. Berner, P. Grohs, G. Kutyniok, and P. Petersen. (2021)cite arxiv:2105.04026Comment: This review paper will appear as a book chapter in the book "Theory of Deep Learning" by Cambridge University Press.
J. Berner, P. Grohs, G. Kutyniok, and P. Petersen. (2021)cite arxiv:2105.04026Comment: This review paper will appear as a book chapter in the book "Theory of Deep Learning" by Cambridge University Press.