stopt 5.7+dfsg-1 source package in Ubuntu

Changelog

stopt (5.7+dfsg-1) unstable; urgency=medium

  * New upstream version 5.7+dfsg

 -- Pierre Gruet <email address hidden>  Mon, 26 Jun 2023 11:47:06 +0200

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

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Mantic release universe misc

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stopt_5.7+dfsg-1.dsc 3.0 KiB 592f424423021893ab343413d56b075c3f0efb14ba05ab56be449433b42e07bf
stopt_5.7+dfsg.orig-texdoc.tar.xz 552.7 KiB 26b18b0399fd608ade5fc53ac0241a331d28eda62c663d7d71ef9ef08ec30cc7
stopt_5.7+dfsg.orig.tar.xz 393.5 KiB 51269f33208a7fe00502c6903434b09b1fa0ac4249805a103a8731ef06a8b664
stopt_5.7+dfsg-1.debian.tar.xz 14.6 KiB ea8d1f4c21f400459eb239b0dd329accbc04519bf122413b3f5b2bebfaec9e70

Available diffs

No changes file available.

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.