# stopt 5.12+dfsg-2build1 source package in Ubuntu

## Changelog

stopt (5.12+dfsg-2build1) oracular; urgency=medium * No-change rebuild against mpi-defaults 1.17 -- Erich Eickmeyer <email address hidden> Thu, 01 Aug 2024 09:36:52 -0700

## Upload details

- Uploaded by:
- Erich Eickmeyer

- Uploaded to:
- Oracular

- Original maintainer:
- Debian Math Team

- Architectures:
- any all

- Section:
- misc

- Urgency:
- Medium Urgency

## See full publishing history Publishing

Series | Published | Component | Section | |
---|---|---|---|---|

Oracular | release | universe | misc |

## Downloads

File | Size | SHA-256 Checksum |
---|---|---|

stopt_5.12+dfsg.orig-texdoc.tar.xz | 552.7 KiB | 5954ee4d4e77c9f9217d14343413ae2299c005d3736b8e971efc6359b3a3e2a1 |

stopt_5.12+dfsg.orig.tar.xz | 394.2 KiB | 193ab6c96a7c01ee8c240cc17c8c2dca480abda23ca392945e352b5423709d2e |

stopt_5.12+dfsg-2build1.debian.tar.xz | 15.4 KiB | 6dcd4a8010edb1781f68950e104c99654ca746ed06d0bfcdf9fc1155d0b3593a |

stopt_5.12+dfsg-2build1.dsc | 2.7 KiB | 80fb273150f8a600512db350d568bcdebb4682abd50f106ec21448d876a7f953 |

### Available diffs

## Binary packages built by this source

- libstopt-dev: library for stochastic optimization problems (development package)
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 headers and the static libraries (libstopt-mpi

which allows for multithreading, and libstopt which does not).

- libstopt5t64: 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).

- libstopt5t64-dbgsym: debug symbols for libstopt5t64

- python3-stopt: library for stochastic optimization problems (Python 3 bindings)
The STochastic OPTimization library (StOpt) aims at providing tools in C++ for

solving some stochastic optimization problems encountered in finance or in the

industry. Python 3 bindings are provided by this package in order to allow one

to use the C++ library in a Python code.

- python3-stopt-dbgsym: debug symbols for python3-stopt

- stopt-doc: library for stochastic optimization problems (documentation)
The STochastic OPTimization library (StOpt) aims at providing tools in C++ for

solving some stochastic optimization problems encountered in finance or in the

industry. Python 3 bindings are also provided in order to allow one to use the

C++ library in a Python code.

.

This package contains the documentation about the type of problems that can be

solved, the mathematical framework, its implementation, and the examples.

- stopt-examples: library for stochastic optimization problems (programs examples)
This package provides some programs written to solve mathematical problems

using the StOpt library. The source code is provided, examples are available

in C++ and in Python. C++ source code has to be built against the libstopt-dev

package if one wants to run it.