Join GitHub today
code for numerically solving dynamic programming problems
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Code for solving dynamic programming optimization problems (i.e. Bellman equations) through value & policy function iteration. This code was developed as part of my paper, "Maturity Mismatch and Fractional-Reserve Banking" (see http://rncarpio.com for more on my economics research). Some projects that the code relies upon are: - Python, Scipy, Numpy : http://python.org, http://scipy.org - PythonXY, a convenient all-in-one distribution for Python : http://pythonxy.com - Intel's Thread Building Blocks library for parallel computing : http://threadingbuildingblocks.org - Boost.Python, for mixing C++ with Python : http://www.boost.org/doc/libs/release/libs/python/ - PyUBlas, a glue layer between numpy and C++ matrices : http://mathema.tician.de/software/pyublas I compiled and ran on Windows 7, Visual Studio 10. Other platforms should work, but I haven't tested them.