GitHub - Twizanex/bellman: code for numerically solving dynamic programming problems

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
Makefile		Makefile
README		README
bankProblem.cpp		bankProblem.cpp
bankProblem.h		bankProblem.h
bankProblem.py		bankProblem.py
bankProblem_orig.py		bankProblem_orig.py
bellman.py		bellman.py
book.h		book.h
cake.py		cake.py
consumptionSavings.cpp		consumptionSavings.cpp
consumptionSavings.h		consumptionSavings.h
consumptionSavings.py		consumptionSavings.py
cudaMonteCarlo.h		cudaMonteCarlo.h
debugMsg.cpp		debugMsg.cpp
debugMsg.h		debugMsg.h
demo.cpp		demo.cpp
ezrange.cpp		ezrange.cpp
ezrange.h		ezrange.h
ezrange_wrap.cpp		ezrange_wrap.cpp
fileobj_example.cpp		fileobj_example.cpp
gamblers_ruin2.py		gamblers_ruin2.py
globals.py		globals.py
incrMonteCarlo.py		incrMonteCarlo.py
lininterp.py		lininterp.py
lininterp2.py		lininterp2.py
markovChain.py		markovChain.py
maximizer.cpp		maximizer.cpp
maximizer.h		maximizer.h
merton.cpp		merton.cpp
merton.h		merton.h
myFuncs.cpp		myFuncs.cpp
myFuncs.h		myFuncs.h
myTypes.h		myTypes.h
myfuncs.py		myfuncs.py
optDividends.cpp		optDividends.cpp
optDividends.h		optDividends.h
optDividends.py		optDividends.py
plot3d.py		plot3d.py
ponzi2_fns.cpp		ponzi2_fns.cpp
ponzi3.py		ponzi3.py
ponzi3_fns.cpp		ponzi3_fns.cpp
ponzi4.py		ponzi4.py
ponzi4_fns.cpp		ponzi4_fns.cpp
ponziGlobals.py		ponziGlobals.py
ponziProblem.cpp		ponziProblem.cpp
ponziProblem.h		ponziProblem.h
ponzi_params.cpp		ponzi_params.cpp
table.py		table.py
test1.cpp		test1.cpp
test2.cpp		test2.cpp
test2.py		test2.py
testBoost.cpp		testBoost.cpp
testCuda.cpp		testCuda.cpp
test_arbb.cpp		test_arbb.cpp
testarbb.cpp		testarbb.cpp
testgsl.cpp		testgsl.cpp

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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.