library for stochastic optimization problems (shared library)

The STochastic OPTimization library (StOpt) aims at providing tools in C++ for solving some stochastic optimization problems encountered in finance or in the industry. Different methods are available:

 - dynamic programming methods based on Monte Carlo with regressions (global,
 local, kernel and sparse regressors), for underlying states following some
 uncontrolled Stochastic Differential Equations;
 - dynamic programming with a representation of uncertainties with a tree:
 transition problems are here solved by some discretizations of the commands,
 resolution of LP with cut representation of the Bellman values;
 - Semi-Lagrangian methods for Hamilton Jacobi Bellman general equations for
 underlying states following some controlled Stochastic Differential
 Equations;
 - Stochastic Dual Dynamic Programming methods to deal with stochastic stock
 management problems in high dimension. Uncertainties can be given by Monte
 Carlo and can be represented by a state with a finite number of values
 (tree);
 - Some branching nesting methods to solve very high dimensional non linear
 PDEs and some appearing in HJB problems. Besides some methods are provided
 to solve by Monte Carlo some problems where the underlying stochastic state
 is controlled.
 For each method, a framework is provided to optimize the problem and then
 simulate it out of the sample using the optimal commands previously computed.
 Parallelization methods based on OpenMP and MPI are provided in this
 framework permitting to solve high dimensional problems on clusters.

The library should be flexible enough to be used at different levels depending on the user's willingness.

This package contains the shared libraries: one which allows for multithreading (libstopt-mpi) and one which does not (libstopt).

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