2 个仓库
Awesome GitHub RepositoriesObjective Function Minimization
Techniques for finding the minimum value of a target function through iterative sampling of hyperparameters.
Distinct from Hyperparameter Search Algorithms: Focuses on the act of minimizing a specific objective function, whereas Hyperparameter Search Algorithms focus on the sampling strategy used to narrow the space.
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Awesome Objective Function Minimization GitHub Repositories
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- hyperopt/hyperopt
hyperopt/hyperopt
7,582在 GitHub 上查看Hyperopt is a Python library for hyperparameter optimization designed to minimize scalar-valued objective functions. It operates as a stochastic search space engine that finds optimal input parameters by searching through real-valued, discrete, and conditional spaces. The framework distinguishes itself through its support for complex search space configurations, allowing for conditional parameter hierarchies where specific hyperparameters are sampled only if their parent parameters meet certain criteria. It is built as an asynchronous optimization framework, decoupling the generation of searc
Minimizes scalar-valued objective functions by iteratively sampling from a defined hyperparameter search space.
Python - facebookresearch/nevergrad
facebookresearch/nevergrad
4,151在 GitHub 上查看Nevergrad is a gradient-free optimization library and hyperparameter optimization framework designed to find the minimum of objective functions without using derivatives. It serves as an asynchronous optimization engine that decouples parameter suggestions from result reporting to support parallel function evaluations. The project specializes in multi-objective optimization to identify Pareto fronts for competing goals and provides a suite for benchmarking the performance and convergence of different optimization routines. It supports black-box system optimization, enabling the tuning of exte
Finds the minimum value of a target function using a variety of gradient-free algorithms.
Python
