The code calculates the value of Pi to arbitrary number of digits such as 100, 1000 etc. The article describes the theory behind the code.

Further your understanding of recursion.

Discover a more efficient way to round decimals to integers.

Learn how to combine four image alignment algorithms (Lucas-Kanade, forwards-compositional, Baker-Dellaert-Matthews, and Hager-Belhumeur) into a 2D object tracker with dynamic templates and template pixel weights.

There are many different styles of recursion that can be used in a variety of instances. Learn about the styles and find the one that is best for your current project.

Learn how to implement some basic number theory functions with the help of C++ template meta programming.

With Blitz++, you can turbo charge C++ so that you get advanced language features but lose its poor performance.

Learn about various Check Digit Schemes and how to implement the scheme.

Learn how to create a Signing of Product Keys to validate activations.

A 'How To' for creating a COM Big Integer Library that uses Visual C++ ATL Wizard and Wei Dai's Crypto++ Library.

Protect Software with Product Keys based on the Advanced Encryption Standard (AES).

Learn about a library that creates a new fixed point variable type.

Rotate images using geometry.

Learn about the differences between little- and big-endian representations.

The STL is primarily made up of templates for containers, iterators, and algorithms, but it also has a few utility templates. Learn more in this excerpt from "Effective C++, Third Edition: 55 Specific Ways to Improve Your Programs and Designs."

Number theory is a branch of math that can be very attractive.

The template class template class CLongInt supports long integer arithmetic. The template parameter bits specifies the size of a CLongInt in bits.

Five discrete probability distributions most common for use in computer simulations, gaming, artificial intelligence decision making, and environment modeling. The classes differ from other routine mathematical attempts at probability computation with a few tricks that extend the range of allowable inputs. A comparison with Monte Carlo techniques to probability computation is given.