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  2. Gekko (optimization software) - Wikipedia

    en.wikipedia.org/wiki/Gekko_(optimization_software)

    GEKKO works on all platforms and with Python 2.7 and 3+. By default, the problem is sent to a public server where the solution is computed and returned to Python. There are Windows, MacOS, Linux, and ARM (Raspberry Pi) processor options to solve without an Internet connection.

  3. Pyomo - Wikipedia

    en.wikipedia.org/wiki/Pyomo

    Pyomo allows users to formulate optimization problems in Python in a manner that is similar to the notation commonly used in mathematical optimization. Pyomo supports an object-oriented style of formulating optimization models, which are defined with a variety of modeling components: sets, scalar and multidimensional parameters, decision variables, objectives, constraints, equations ...

  4. Conjugate gradient method - Wikipedia

    en.wikipedia.org/wiki/Conjugate_gradient_method

    The conjugate gradient method can also be used to solve unconstrained optimization problems such as energy minimization. It is commonly attributed to Magnus Hestenes and Eduard Stiefel, [1] [2] who programmed it on the Z4, [3] and extensively researched it. [4] [5] The biconjugate gradient method provides a generalization to non-symmetric matrices.

  5. HiGHS optimization solver - Wikipedia

    en.wikipedia.org/wiki/HiGHS_optimization_solver

    It has no external dependencies. A convenient thin wrapper to Python is available via the highspy PyPI package. Although generally single-threaded, some solver components can utilize multi-core architectures. HiGHS is designed to solve large-scale models and exploits problem sparsity.

  6. OR-Tools - Wikipedia

    en.wikipedia.org/wiki/OR-Tools

    OR-Tools was created by Laurent Perron in 2011. [5]In 2014, Google's open source linear programming solver, GLOP, was released as part of OR-Tools. [1]The CP-SAT solver [6] bundled with OR-Tools has been consistently winning gold medals in the MiniZinc Challenge, [7] an international constraint programming competition.

  7. Runge–Kutta–Fehlberg method - Wikipedia

    en.wikipedia.org/wiki/Runge–Kutta–Fehlberg...

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Help; Learn to edit; Community portal; Recent changes; Upload file

  8. College football winners and losers: Taking stock of the ...

    www.aol.com/sports/college-football-winners...

    Liberty: The loss to Kennesaw State almost assuredly ended any chance Liberty had of making the expanded College Football Playoff. After going undefeated a season ago and losing by 39 to Oregon in ...

  9. Boolean satisfiability problem - Wikipedia

    en.wikipedia.org/wiki/Boolean_satisfiability_problem

    In logic and computer science, the Boolean satisfiability problem (sometimes called propositional satisfiability problem and abbreviated SATISFIABILITY, SAT or B-SAT) is the problem of determining if there exists an interpretation that satisfies a given Boolean formula.