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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. HiGHS optimization solver - Wikipedia

    en.wikipedia.org/wiki/HiGHS_optimization_solver

    HiGHS is designed to solve large-scale models and exploits problem sparsity. Its performance relative to commercial and other open-source software is reviewed periodically using industry-standard benchmarks. [2] The term HiGHS may also refer to both the underlying project and the small team leading the software development.

  4. OR-Tools - Wikipedia

    en.wikipedia.org/wiki/OR-Tools

    Google OR-Tools is a free and open-source software suite developed by Google for solving linear programming (LP), mixed integer programming (MIP), constraint programming (CP), vehicle routing (VRP), and related optimization problems. [3] OR-Tools is a set of components written in C++ but provides wrappers for Java, .NET and Python.

  5. List of unsolved problems in computer science - Wikipedia

    en.wikipedia.org/wiki/List_of_unsolved_problems...

    The problem to determine all positive integers such that the concatenation of and in base uses at most distinct characters for and fixed [citation needed] and many other problems in the coding theory are also the unsolved problems in mathematics.

  6. Roundup (issue tracker) - Wikipedia

    en.wikipedia.org/wiki/Roundup_(issue_tracker)

    Roundup was designed by Ka-Ping Yee for the Software Carpentry project and was developed from 2001 to 2016 under the direction of Richard Jones. Since then, it has been developed by the Roundup community. It was the issue tracker for the Python programming language for 17 years before migrating to GitHub. [4]

  7. Stochastic programming - Wikipedia

    en.wikipedia.org/wiki/Stochastic_programming

    Then the expectation in the first-stage problem's objective function can be written as the summation: [(,)] = = (,) and, moreover, the two-stage problem can be formulated as one large linear programming problem (this is called the deterministic equivalent of the original problem, see section § Deterministic equivalent of a stochastic problem).

  8. SAT solver - Wikipedia

    en.wikipedia.org/wiki/SAT_solver

    In computer science and formal methods, a SAT solver is a computer program which aims to solve the Boolean satisfiability problem.On input a formula over Boolean variables, such as "(x or y) and (x or not y)", a SAT solver outputs whether the formula is satisfiable, meaning that there are possible values of x and y which make the formula true, or unsatisfiable, meaning that there are no such ...

  9. Dynamic programming - Wikipedia

    en.wikipedia.org/wiki/Dynamic_programming

    If the solution to any problem can be formulated recursively using the solution to its sub-problems, and if its sub-problems are overlapping, then one can easily memoize or store the solutions to the sub-problems in a table (often an array or hashtable in practice). Whenever we attempt to solve a new sub-problem, we first check the table to see ...