package ocplib-simplex

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A library implementing a simplex algorithm, in a functional style, for solving systems of linear inequalities

Install

Dune Dependency

Authors

Maintainers

Sources

ocplib-simplex-0.5.1.tbz
sha256=a5c814be4d18e60c525d37c5b21e880b05b42e7c57b351aa3d20173521d513cf
sha512=2cf2936792c4429556fa7349069474056d5ae4ca0cf8ad3587880ebbc32cec39fad9b36df7c1ae18fa15f89fe4291bdb5a350b20d0cf84ce5ae651a77d0dd163

CHANGES.md.html

unreleased

v0.5.1 (2024-03-28)

  • Add documentation for solving system (PR #16).

  • Separate types for coefficents and values (PR #17).

  • Remove the dependency on num (PR #19).

  • Remove messages at the App level (PR #22).

0.4.1 (2023-04-21)

  • Fix the issue 13 about strict formats (PR #18).

0.5 (2022-11-15)

  • Reworking the library build system, now only relying on dune. The Makefile is now clearer and simpler to use.

  • Logs are handled by the logs library and debug is activated by this library.

  • The Rat2 module now abstract bounds as strict upper, strict lower or soft bounds instead of pairs of rationals.

0.4 (2017-08-22)

  • Now, asserting bounds returns whether these bounds are trivially implied by those that are already known

  • Add a field nb_pivots in the environment to count the number of pivots that have been made so far.

0.3 (2016-11-09)

  • Bugfix in maximization

0.2 (2016-08-24)

  • Add support for linear optimization (!!!). An minimal example is given in tests/standalone_minimal_maximization.ml

  • Some bugfixes when assuming inconsistent bounds

  • Improve build and testing

0.1 (2016-07-11)

  • A functor called Basic provides three modules:

    • Core: provides some basic functions, and a function empty to create an empty environment

    • Assert: exports two functions var and polys to assert bounds on variables and polynomials, respectively

    • Solve: exports a function solve that tries to find a solution for the constrains

  • Two flags can be set when creating an empty environment to activate debug mode and some invariants verification

  • Implementation is fully functional, incremental and backtrackable

  • Linear optimization is not supported yet

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