awesome-repositories.com
博客
MCP
awesome-repositories.com

通过 AI 驱动的搜索,发现最优秀的开源仓库。

探索精选搜索开源替代品自托管软件博客网站地图
项目MCP 服务器关于排名机制媒体报道
法律隐私政策服务条款
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

8 个仓库

Awesome GitHub RepositoriesNonlinear Optimization Solvers

Numerical methods and solvers for optimizing nonlinear systems, often used in motion planning and predictive control.

Distinguishing note: No existing candidates provided; this specifically addresses C-GMRES and nonlinear predictive control techniques.

Explore 8 awesome GitHub repositories matching scientific & mathematical computing · Nonlinear Optimization Solvers. Refine with filters or upvote what's useful.

Awesome Nonlinear Optimization Solvers GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • atsushisakai/pythonroboticsAtsushiSakai 的头像

    AtsushiSakai/PythonRobotics

    29,772在 GitHub 上查看↗

    PythonRobotics 是一个综合性的模块化机器人算法和教育模拟集合,专为自主导航、状态估计和运动控制而设计。该项目提供了一个包含路径规划、定位、建图和运动学的独立实现库,为研究人员和学生实验基础及高级机器人理论提供了资源。 该项目的特色在于以算法为中心的设计,每个模块都作为独立的脚本运行,允许进行独立测试和清晰的教学演示。每个实现都明确映射到学术文献或基础机器人教科书,确保数学模型和控制策略保持可验证性和准确性。用户可以在一个解耦的模拟环境中执行这些场景,该环境维护其内部状态和控制循环,无需外部依赖。 其功能涵盖了广泛的机器人领域,包括空中导航、双足运动和多关节手臂控制。它具有用于概率传感器融合、环境建图和轨迹跟踪的广泛工具包,所有这些都由高性能数值计算驱动。实时几何动画和状态估计直接使用标准绘图库从模拟数据中渲染。

    A motion planning and path tracking simulation with NMPC of C-GMRES Reference - documentation

    Pythonalgorithmanimationautonomous-driving
    在 GitHub 上查看↗29,772
  • workiva/go-datastructuresWorkiva 的头像

    Workiva/go-datastructures

    7,901在 GitHub 上查看↗

    go-datastructures is a collection of thread-safe and lock-free data structures designed for high-performance concurrent applications in Go. It provides a modular library of specialized algorithmic toolsets, including a lock-free collection library and an immutable data structure library. The project distinguishes itself through a suite of persistent AVL trees and hash array mapped tries that use branch-copying to preserve previous versions. It also implements non-blocking hash maps, queues, and tries that enable linearizable snapshots and concurrent updates without the use of mutual exclusion

    Implements a probabilistic global restart system for finding minimum or maximum values under nonlinear constraints.

    Go
    在 GitHub 上查看↗7,901
  • cvxpy/cvxpycvxpy 的头像

    cvxpy/cvxpy

    6,257在 GitHub 上查看↗

    CVXPY is a Python-embedded domain-specific language for modeling and solving convex optimization problems using natural mathematical syntax. It is built on a disciplined convex programming framework that automatically enforces convexity rules, ensuring that problems formulated by the user are valid for convex solvers. The project also functions as a multi-solver optimization interface, abstracting away backend details and dispatching problems to specialized solvers like ECOS, SCS, and Gurobi without manual configuration. Beyond standard convex optimization, CVXPY extends its reach to geometri

    Extends convex optimization to nonlinear programming using a disciplined grammar for expression validation.

    C++
    在 GitHub 上查看↗6,257
  • ceres-solver/ceres-solverceres-solver 的头像

    ceres-solver/ceres-solver

    4,499在 GitHub 上查看↗

    Ceres Solver 是一个 C++ 数值优化库,专注于非线性最小二乘法和无约束优化问题。它作为一个自动微分和鲁棒曲线拟合框架,提供了求解大规模数学模型的工具。 该库以其光束法平差 (bundle adjustment) 能力而著称,利用稀疏矩阵结构来优化 3D 场景点和相机参数。它利用双数自动微分来计算代价函数的导数,无需手动推导雅可比矩阵。 该项目涵盖了广泛的优化功能,包括非欧几里得空间的流形约束、用于减轻异常值的鲁棒损失函数,以及稠密和稀疏线性系统求解。它还提供了用于旋转表示转换、表格数据插值和参数协方差估计的实用工具。 提供针对 Android 和 iOS 目标的构建配置,以支持移动硬件优化。

    Optimizes memory and computation when the structural patterns of data change during the optimization process.

    C++
    在 GitHub 上查看↗4,499
  • robotlocomotion/drakeRobotLocomotion 的头像

    RobotLocomotion/drake

    3,910在 GitHub 上查看↗

    Drake is a robotics simulation framework and control system modeling tool used for designing, simulating, and verifying the dynamics of complex robotic systems. It functions as a multibody dynamics simulator and a mathematical optimization library, providing a suite of algorithms for trajectory optimization and the simulation of articulated robots. The framework is distinguished by its block-diagram system for composing dynamical subsystems and its ability to formulate and solve diverse mathematical programs, including linear, quadratic, and nonconvex nonlinear problems. It supports specializ

    Finds solutions for general nonlinear optimization problems using gradient-based or specialized non-convex solvers.

    C++drakerobotics
    在 GitHub 上查看↗3,910
  • sciml/differentialequations.jlSciML 的头像

    SciML/DifferentialEquations.jl

    3,121在 GitHub 上查看↗

    DifferentialEquations.jl is a comprehensive numerical library designed for solving ordinary, stochastic, delay, and algebraic differential equations. It functions as a high-performance solver suite that integrates scientific machine learning, probabilistic programming, and automated differentiation into a unified framework. By leveraging multiple dispatch and symbolic-numeric integration, the library provides a flexible environment for complex mathematical modeling and simulation. The project distinguishes itself through its ability to bridge traditional numerical analysis with modern machine

    Accelerates complex computations through automated sparsity detection, matrix coloring, and hardware offloading.

    Juliadaeddedelay-differential-equations
    在 GitHub 上查看↗3,121
  • google-deepmind/mujoco_menageriegoogle-deepmind 的头像

    google-deepmind/mujoco_menagerie

    3,055在 GitHub 上查看↗

    mujoco_menagerie is a curated library of physical robot specifications and XML model definitions designed for standardized dynamics and contact simulation. It provides a collection of high-quality robot model files for humanoids, quadrupeds, and manipulators, alongside detailed kinematic and inertial parameters used to reproduce real-world robot behavior in virtual environments. The project serves as a repository of robotics simulation assets and MJCF model definitions optimized for accuracy. It includes standardized model libraries specifically for bipedal, quadrupedal, and humanoid hardware

    MuJoCo solves sequential quadratic programs using a nonlinear least squares optimizer.

    Pythonmujocorobotics
    在 GitHub 上查看↗3,055
  • juliasymbolics/symbolics.jlJuliaSymbolics 的头像

    JuliaSymbolics/Symbolics.jl

    1,514在 GitHub 上查看↗

    Symbolics.jl is a foundational framework for symbolic mathematics, automated differentiation, and scientific compilation within the Julia programming language. It provides a comprehensive system for algebraic manipulation, expression simplification, and the construction of mathematical models, enabling users to represent complex physical and chemical systems as symbolic equations. The library distinguishes itself through a source-to-source compilation engine that translates high-level symbolic representations directly into optimized, parallelized, and hardware-specific numerical code. By util

    Provides automated sparsity detection for Jacobians and Hessians to skip zero-value computations during numerical execution.

    Juliacascomputer-algebra-systemhigh-performance
    在 GitHub 上查看↗1,514
  1. Home
  2. Scientific & Mathematical Computing
  3. Numerical and Mathematical Foundations
  4. Optimization & Solvers
  5. Nonlinear Optimization Solvers

探索子标签

  • Disciplined Grammar FrameworksFrameworks that extend convex optimization to nonlinear programming using a disciplined grammar for expression validation. **Distinct from Nonlinear Optimization Solvers:** Distinct from Nonlinear Optimization Solvers: focuses on the modeling grammar and validation, not the numerical solver itself.
  • Disciplined Nonlinear Programming GrammarsA grammar that validates and canonicalizes nonlinear expressions for dispatch to compatible solvers. **Distinct from Nonlinear Optimization Solvers:** Distinct from Nonlinear Optimization Solvers: provides a modeling grammar and validation layer, not the solver implementations themselves.
  • Performance Accelerators2 个子标签Utilities for accelerating numerical computations through sparsity detection, matrix coloring, and hardware offloading. **Distinct from Nonlinear Optimization Solvers:** Distinct from Nonlinear Optimization Solvers: focuses on general performance acceleration techniques for solvers rather than specific nonlinear optimization algorithms.