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  2. XGBoost - Wikipedia

    en.wikipedia.org/wiki/XGBoost

    It works on Linux, Microsoft Windows, [7] and macOS. [8] From the project description, it aims to provide a "Scalable, Portable and Distributed Gradient Boosting (GBM, GBRT, GBDT) Library". It runs on a single machine, as well as the distributed processing frameworks Apache Hadoop , Apache Spark , Apache Flink , and Dask .

  3. HiGHS optimization solver - Wikipedia

    en.wikipedia.org/wiki/HiGHS_optimization_solver

    HiGHS is open-source software to solve linear programming (LP), mixed-integer programming (MIP), and convex quadratic programming (QP) models. [1] 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.

  4. CatBoost - Wikipedia

    en.wikipedia.org/wiki/Catboost

    It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. [7] It works on Linux , Windows , macOS , and is available in Python , [ 8 ] R , [ 9 ] and models built using CatBoost can be used for predictions in C++ , Java ...

  5. Gekko (optimization software) - Wikipedia

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

    The program produces parameter weights that minimize the sum of squared errors between the measured data points and the neural network predictions at those points. GEKKO uses gradient-based optimizers to determine the optimal weight values instead of standard methods such as backpropagation. The gradients are determined by automatic ...

  6. Barzilai-Borwein method - Wikipedia

    en.wikipedia.org/wiki/Barzilai-Borwein_method

    The Barzilai-Borwein method [1] is an iterative gradient descent method for unconstrained optimization using either of two step sizes derived from the linear trend of the most recent two iterates. This method, and modifications, are globally convergent under mild conditions, [ 2 ] [ 3 ] and perform competitively with conjugate gradient methods ...

  7. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

  8. LightGBM - Wikipedia

    en.wikipedia.org/wiki/LightGBM

    LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. [ 4 ] [ 5 ] It is based on decision tree algorithms and used for ranking , classification and other machine learning tasks.

  9. Gradient method - Wikipedia

    en.wikipedia.org/wiki/Gradient_method

    In optimization, a gradient method is an algorithm to solve problems of the form min x ∈ R n f ( x ) {\displaystyle \min _{x\in \mathbb {R} ^{n}}\;f(x)} with the search directions defined by the gradient of the function at the current point.