STochastic OPTimization library in C++
Abstract
The STochastic OPTimization library (StOpt) aims at providing tools in C++ for solving some
stochastic optimization problems encountered in finance or in the industry.
A python binding is available for some C++ objects provided permitting to easily solve an optimization problem by regression.
Different methods are available :
- dynamic programming methods based on Monte Carlo with regressions (global, local and sparse regressors), for underlying states following some uncontrolled Stochastic Differential Equations (python binding provided).
- Semi-Lagrangian methods for Hamilton Jacobi Bellman general equations for underlying states following some controlled Stochastic Differential Equations (C++ only)
- Stochastic Dual Dynamic Programming methods to deal with stochastic stocks management problems in high dimension. A SDDP module in python is provided. To use this module, the transitional optimization problem has to written in C++ and mapped to python (examples provided).
- Some methods are provided to solve by Monte Carlo some problems where the underlying stochastic state is controlled.
- Some pure Monte Carlo Methods are proposed to solve some non linear PDEs
| Origin | Files produced by the author(s) |
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