stopt 5.8+dfsg-1build2 source package in Ubuntu

Changelog

stopt (5.8+dfsg-1build2) noble; urgency=medium

  * No-change rebuild with Python 3.12 as default

 -- Graham Inggs <email address hidden>  Fri, 19 Jan 2024 20:57:38 +0000

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Uploaded by:
Graham Inggs
Uploaded to:
Noble
Original maintainer:
Debian Math Team
Architectures:
any all
Section:
misc
Urgency:
Medium Urgency

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stopt_5.8+dfsg.orig-texdoc.tar.xz 552.7 KiB 5954ee4d4e77c9f9217d14343413ae2299c005d3736b8e971efc6359b3a3e2a1
stopt_5.8+dfsg.orig.tar.xz 393.5 KiB d53040903e930f0b33f9b8101a8de52f79c7e2dde4eb7d197ccd0e0e35a5337d
stopt_5.8+dfsg-1build2.debian.tar.xz 14.8 KiB 187c50a5a2186626cb6be1cda0694c258126cfadb224a88535b2414bc4690fbe
stopt_5.8+dfsg-1build2.dsc 3.0 KiB 7799a82a3513a3df483ce993bf8c5a687cccb7442507dfbc1551c495ae167f49

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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).

libstopt5: No summary available for libstopt5 in ubuntu noble.

No description available for libstopt5 in ubuntu noble.

libstopt5-dbgsym: No summary available for libstopt5-dbgsym in ubuntu noble.

No description available for libstopt5-dbgsym in ubuntu noble.

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.