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  2. HiGHS optimization solver - Wikipedia

    en.wikipedia.org/wiki/HiGHS_optimization_solver

    Written in C++ and published under an MIT license, HiGHS provides programming interfaces to C, Python, Julia, Rust, R, JavaScript, Fortran, and C#. 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 ...

  3. Off-by-one error - Wikipedia

    en.wikipedia.org/wiki/Off-by-one_error

    The correct number of sections for a fence is n − 1 if the fence is a free-standing line segment bounded by a post at each of its ends (e.g., a fence between two passageway gaps), n if the fence forms one complete, free-standing loop (e.g., enclosure accessible by surmounting, such as a boxing ring), or n + 1 if posts do not occur at the ends ...

  4. SciPy - Wikipedia

    en.wikipedia.org/wiki/SciPy

    SciPy (pronounced / ˈ s aɪ p aɪ / "sigh pie" [2]) is a free and open-source Python library used for scientific computing and technical computing. [3]SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

  5. List of optimization software - Wikipedia

    en.wikipedia.org/wiki/List_of_optimization_software

    Given a transformation between input and output values, described by a mathematical function, optimization deals with generating and selecting the best solution from some set of available alternatives, by systematically choosing input values from within an allowed set, computing the output of the function and recording the best output values found during the process.

  6. Test functions for optimization - Wikipedia

    en.wikipedia.org/wiki/Test_functions_for...

    The artificial landscapes presented herein for single-objective optimization problems are taken from Bäck, [1] Haupt et al. [2] and from Rody Oldenhuis software. [3] Given the number of problems (55 in total), just a few are presented here. The test functions used to evaluate the algorithms for MOP were taken from Deb, [4] Binh et al. [5] and ...

  7. Second-order cone programming - Wikipedia

    en.wikipedia.org/wiki/Second-order_cone_programming

    SOCPs can be solved by interior point methods [2] and in general, can be solved more efficiently than semidefinite programming (SDP) problems. [3] Some engineering applications of SOCP include filter design, antenna array weight design, truss design, and grasping force optimization in robotics. [ 4 ]

  8. Monkey patch - Wikipedia

    en.wikipedia.org/wiki/Monkey_patch

    The definition of the term varies depending upon the community using it. In Ruby, [2] Python, [3] and many other dynamic programming languages, the term monkey patch only refers to dynamic modifications of a class or module at runtime, motivated by the intent to patch existing third-party code as a workaround to a bug or feature which does not act as desired.

  9. Constrained optimization - Wikipedia

    en.wikipedia.org/wiki/Constrained_optimization

    If all the hard constraints are linear and some are inequalities, but the objective function is quadratic, the problem is a quadratic programming problem. It is one type of nonlinear programming. It can still be solved in polynomial time by the ellipsoid method if the objective function is convex; otherwise the problem may be NP hard.