code for numerically solving dynamic programming problems
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Makefile
README
bankProblem.cpp
bankProblem.h
bankProblem.py
bankProblem_orig.py
bellman.py
book.h
cake.py
consumptionSavings.cpp
consumptionSavings.h
consumptionSavings.py
cudaMonteCarlo.h
debugMsg.cpp
debugMsg.h
demo.cpp
ezrange.cpp
ezrange.h
ezrange_wrap.cpp
fileobj_example.cpp
gamblers_ruin2.py
globals.py
incrMonteCarlo.py
lininterp.py
lininterp2.py
markovChain.py
maximizer.cpp
maximizer.h
merton.cpp
merton.h
myFuncs.cpp
myFuncs.h
myTypes.h
myfuncs.py
optDividends.cpp
optDividends.h
optDividends.py
plot3d.py
ponzi2_fns.cpp
ponzi3.py
ponzi3_fns.cpp
ponzi4.py
ponzi4_fns.cpp
ponziGlobals.py
ponziProblem.cpp
ponziProblem.h
ponzi_params.cpp
table.py
test1.cpp
test2.cpp
test2.py
testBoost.cpp
testCuda.cpp
test_arbb.cpp
testarbb.cpp
testgsl.cpp

README


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.