Numerical-Analysis

This repository mirrors Computational-Statistics on resources related to numerical analysis.

Textbooks

• Anderson E, et al. (1999). LAPACK Users’ Guide, 3e, https://www.netlib.org/lapack/lug/.
• Forsythe GE, Malcolm MA, Moler CB (1977). Computer Methods for Mathematical Computations. Prentice-Hall. Source code.
• Golub GH, Van Loan CF (2013). Matrix Computations, 4e. The Johns Hopkins University Press, https://jhupbooks.press.jhu.edu/title/matrix-computations. Source code.
• Press WH, et al. (2007). Numerical Recipes: The Art of Scientific Computing, 3e, Cambridge University Press.
• Stewart GW (1998). Matrix Algorithms I-Basic Decompositions. SIAM. https://epubs.siam.org/doi/book/10.1137/1.9781611971408.
• Stewart GW (2001). Matrix Algorithms II-Eigensystems. SIAM. https://epubs.siam.org/doi/book/10.1137/1.9780898718058.
• Trefethen LN (2013). Approximation Theory and Approximation Practice. SIAM. Source code.
• Wilkinson JH (1965). The algebraic eigenvalue problem, Oxford University Press.

Websites

• Guide to Available Mathematical Software.
• Netlib Repository.
• NIST Digital Library of Mathematical Functions.
• Wolfram MathWorld.

Software

• AMPL.
• ILOG (Wikipedia).
• Maplesoft.
• Mathematica.
• SageMath.

Miscellaneous resources

• Adam-optimizer for stochastic function x^2 -4*x + 4, https://github.com/sagarvegad/Adam-optimizer
• An illustrated introduction to the t-SNE algorithm
• Computational Linear Algebra for Coders (post)
• PCA vs Autoencoders for Dimensionality Reduction

AI-related

• Applied Generative AI for Beginners
• Building AI Applications with OpenAI APIs
• How to Conduct Time Series Analysis in R
• Lasso and Elastic Net Regressions, Explained: A Visual Guide with Code Examples
• OpenAI API Cookbook
• Python Data Cleaning and Preparation Best Practices