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https://github.com/mek97/reoptimization-algorithms/workflows/Build%20tests/badge.svg\n   :target: https://github.com/mek97/reoptimization-algorithms/\n\n.. image:: https://github.com/mek97/reoptimization-algorithms/workflows/Docs%20publish/badge.svg\n   :target: https://github.com/mek97/reoptimization-algorithms/\n\n.. image:: https://codecov.io/gh/mek97/reoptimization-algorithms/branch/master/graph/badge.svg\n   :target: https://codecov.io/gh/mek97/reoptimization-algorithms/\n\n.. image:: https://img.shields.io/badge/License-MIT-blue.svg\n   :target: https://opensource.org/licenses/MIT\n\n.. image:: https://img.shields.io/badge/code%20style-black-000000.svg\n   :target: https://github.com/psf/black\n\n\nPackage implementing some well known Reoptimization algorithms\n\n.. image:: https://mek97.github.io/reoptimization-algorithms/_images/header.png\n\n`Perterson Graph \u003chttps://en.wikipedia.org/wiki/Generalized_Petersen_grapht\u003e`_ (Made using `GeoGebra \u003chttps://www.geogebra.org/?lang=en\u003e`_)\n\n\n.. contents:: **Table of contents**\n\n============\nIntroduction\n============\nCurrently, considerable efforts must be put to find optimal solution for NP-Hard problems.\nReoptimisation deals with, If given an optimal solution to a problem instance I\\ :sub:`O`,\ncan we find a good approximated solution to instance I\\ :sub:`N`, where I\\ :sub:`N` is I\\ :sub:`O` with some 'local' modifications?\nThe goal in this repository is to expose some well known reoptimization algorithms.\n\n=====\nSetup\n=====\n\nRequirements\n~~~~~~~~~~~~\n\n* (**Recommended**) Python versions: \u003e=3.6, \u003c=3.9.*\n\nInstallation\n~~~~~~~~~~~~\n\n\n* Option 1\n\n  To Install the stable latest package from pypi host\n\n    ``pip install reoptimization-algorithms``\n\n* Option 2\n\n  To install directly from this repository execute the following in repository root directory\n\n    ``python setup.py install``\n\n\n=============\nDocumentation\n=============\n\nToy example\n~~~~~~~~~~~\n.. code-block:: python\n\n            import reoptimization_algorithms as ra\n\n            old_graph = (ra.UndirectedGraph().add_vertex(\"4\").add_edge(\"4\", \"5\").add_edge(\"40\", \"50\")\n                         .add_vertex(\"6\").add_edge(\"4\", \"8\").add_vertex(\"99\").delete_vertex(\"6\"))\n            attached_graph = ra.UndirectedGraph().add_edge(\"90\", \"95\")\n            attach_edges = [ra.Edge(\"4\", \"90\")]\n            old_solution = {\"8\"}\n\n            solution = ra.UnweightedPVCP.reoptimize_ptas(old_graph, attached_graph, attach_edges,\n                                                         old_solution, k = 3)\n            print(solution) # {\"4\"}\n\n\nFor detailed documentation and usage refer `here \u003chttps://mek97.github.io/reoptimization-algorithms/index.html\u003e`_\n\n\n\n==============\nImplementation\n==============\n\nImplementation basically consists of\n\n#. 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