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36 repository-uri

Awesome GitHub RepositoriesOptimization & Solvers

Explore 36 awesome GitHub repositories matching scientific & mathematical computing · Optimization & Solvers. Refine with filters or upvote what's useful.

Awesome Optimization & Solvers GitHub Repositories

Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
  • akullpp/awesome-javaAvatar akullpp

    akullpp/awesome-java

    48,240Vezi pe GitHub↗

    This project is a comprehensive, community-driven directory of software resources, libraries, and frameworks for the Java programming language. It serves as a centralized knowledge base designed to help developers discover tools and industry-standard solutions for building and maintaining software applications. The repository distinguishes itself through a hierarchical taxonomy that organizes a vast array of technical components into a structured, navigable tree. By relying on distributed peer contributions, the index remains a living resource that reflects current community-recommended pract

    Lists algorithms and libraries for finding solutions to constraint satisfaction problems.

    awesomeawesome-list
    Vezi pe GitHub↗48,240
  • deepspeedai/deepspeedAvatar deepspeedai

    deepspeedai/DeepSpeed

    42,528Vezi pe GitHub↗

    DeepSpeed is a high-performance library designed to scale deep learning model training and inference across massive clusters of GPUs and compute nodes. It provides a comprehensive suite of tools for distributed training, enabling the execution of models that exceed the memory capacity of single devices through advanced parameter partitioning, pipeline-based model parallelism, and memory-efficient state offloading. The framework distinguishes itself through specialized communication-efficient optimizers and hardware-aware acceleration techniques. By utilizing gradient compression, quantization

    The framework reduces memory usage and increases training speed for structural biology models using specialized kernels designed for large-scale sequence computations.

    Pythonbillion-parameterscompressiondata-parallelism
    Vezi pe GitHub↗42,528
  • exacity/deeplearningbook-chineseAvatar exacity

    exacity/deeplearningbook-chinese

    37,285Vezi pe GitHub↗

    This project is a comprehensive Chinese translation of a technical deep learning textbook, providing an educational resource on the theory and implementation of neural networks. It functions as a collaborative technical translation project designed to make complex academic AI literature accessible to non-English speakers. The project utilizes a community-driven translation model that integrates external suggestions and pull requests to refine linguistic accuracy and reduce bias. It employs standardized terminology mapping to ensure a uniform vocabulary throughout the translated content. To i

    Explains the use of constraint solvers to find function optima within feasible boundaries.

    TeX
    Vezi pe GitHub↗37,285
  • atsushisakai/pythonroboticsAvatar AtsushiSakai

    AtsushiSakai/PythonRobotics

    29,772Vezi pe GitHub↗

    PythonRobotics este o colecție cuprinzătoare de algoritmi modulari de robotică și simulări educaționale concepute pentru navigație autonomă, estimarea stării și controlul mișcării. Proiectul oferă o bibliotecă de implementări independente pentru planificarea traseului, localizare, mapare și cinematică, servind ca resursă pentru cercetători și studenți pentru a experimenta teorii robotice fundamentale și avansate. Proiectul se distinge printr-un design centrat pe algoritm, unde fiecare modul funcționează ca un script izolat, permițând testarea independentă și demonstrația pedagogică clară. Fiecare implementare este mapată explicit la literatura academică sau manualele fundamentale de robotică, asigurându-se că modelele matematice și strategiile de control rămân verificabile și precise. Utilizatorii pot executa aceste scenarii într-un mediu de simulare decuplat care își menține propria stare internă și bucle de control, nefiind necesare dependențe externe. Suprafața de capabilități acoperă o gamă largă de domenii robotice, inclusiv navigația aeriană, locomoția bipedă și controlul brațelor cu mai multe articulații. Dispune de seturi de instrumente extinse pentru fuziunea probabilistică a senzorilor, maparea mediului și urmărirea traiectoriei, toate alimentate de calcul numeric de înaltă performanță. Animațiile geometrice în timp real și estimările de stare sunt redate direct din datele de simulare folosind biblioteci standard de plotare.

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

    Pythonalgorithmanimationautonomous-driving
    Vezi pe GitHub↗29,772
  • fincept-corporation/finceptterminalAvatar Fincept-Corporation

    Fincept-Corporation/FinceptTerminal

    26,900Vezi pe GitHub↗

    FinceptTerminal is a quantitative finance platform and financial engineering library designed for asset valuation, risk management, and fixed-income analytics. It provides a comprehensive suite for algorithmic trading and investment strategy automation, integrating specialized language model agents and node-based workflows to automate market research and alpha generation. The project distinguishes itself with a dedicated game theory analysis engine for calculating Nash equilibria and simulating strategic interactions in competitive markets. It also features a specialized credit risk modeling

    Calculates competitive equilibrium prices and optimal allocations in multi-consumer economies using iterative processes.

    C++bloomberg-terminalcontributions-welcomefinance
    Vezi pe GitHub↗26,900
  • mxgmn/wavefunctioncollapseAvatar mxgmn

    mxgmn/WaveFunctionCollapse

    24,697Vezi pe GitHub↗

    WaveFunctionCollapse is a procedural generation engine that creates complex, non-repeating patterns by treating spatial arrangement as a constraint satisfaction problem. It functions as a stochastic solver that derives output structures from a single input example, ensuring that every element placed within a grid satisfies specific adjacency requirements relative to its neighbors. The system distinguishes itself by using an entropy-driven approach to grid collapse, where it iteratively selects the cell with the fewest remaining possibilities to trigger a cascade of logical updates. By decompo

    A probabilistic algorithm that derives output structures from input examples by treating generation as a constraint satisfaction problem with local dependencies.

    C#algorithmcsharpgamedev
    Vezi pe GitHub↗24,697
  • liabru/matter-jsAvatar liabru

    liabru/matter-js

    18,256Vezi pe GitHub↗

    Matter-js is a 2D rigid body physics engine written in JavaScript for simulating realistic physical interactions, collisions, and dynamics in web browsers. It functions as a web physics simulation library that calculates motion, gravity, and constraints for objects rendered on a web canvas. The library includes a built-in canvas physics visualizer to draw physical bodies, joints, and constraints for debugging and gameplay. It distinguishes itself through a plugin system that supports recursive dependency resolution and internal method patching to inject custom logic into the engine's executio

    Resolves physical contact and joint constraints using the Gauss-Seidel method.

    JavaScriptcanvasjavascriptphysics
    Vezi pe GitHub↗18,256
  • bulletphysics/bullet3Avatar bulletphysics

    bulletphysics/bullet3

    14,243Vezi pe GitHub↗

    Bullet3 is a professional physics simulation engine designed for calculating rigid body, soft body, and collision dynamics within 3D environments and robotics applications. It functions as a computational framework for determining complex geometric intersections and contact manifolds between objects in simulated space. The library distinguishes itself through a distributed rendering framework that scales heavy graphical workloads and scene generation tasks across large clusters of machines. This capability enables the production of massive datasets by distributing complex scene generation acr

    Resolves collisions and constraints by iteratively applying velocity impulses to bodies until the system reaches equilibrium.

    C++computer-animationgame-developmentkinematics
    Vezi pe GitHub↗14,243
  • google-deepmind/mujocoAvatar google-deepmind

    google-deepmind/mujoco

    13,957Vezi pe GitHub↗

    MuJoCo is a physics simulation engine designed for the dynamics of multi-joint articulated structures. It provides a computational framework for calculating the forces, velocities, and physical interactions of complex models within a virtual environment, supporting research in robotics, biomechanics, and machine learning. The engine utilizes a constraint-based dynamics solver and recursive algorithms to manage the motion of articulated systems. It includes a native graphical interface for real-time visualization, allowing users to inspect physical behaviors and contact dynamics as they occur.

    Calculates the motion of multi-joint systems by solving linear complementarity problems to enforce physical constraints and contact forces.

    C++mujocophysicsrobotics
    Vezi pe GitHub↗13,957
  • google/or-toolsAvatar google

    google/or-tools

    13,114Vezi pe GitHub↗

    OR-Tools is a software suite for combinatorial optimization, constraint programming, and mathematical modeling. It provides a framework for defining complex problems involving variables and logical constraints, enabling the systematic search for feasible or optimal solutions. The project features a high-performance core engine written in C++ that utilizes branch and bound search and local search metaheuristics to navigate large solution spaces. A language-agnostic wrapper layer allows these optimization capabilities to be accessed through idiomatic interfaces in multiple high-level programmin

    Acts as a comprehensive library for modeling and solving combinatorial optimization problems like routing and scheduling.

    C++combinatorial-optimizationlinear-programmingoperations-research
    Vezi pe GitHub↗13,114
  • z3prover/z3Avatar Z3Prover

    Z3Prover/z3

    11,936Vezi pe GitHub↗

    Z3 is an automated theorem prover and satisfiability modulo theories solver designed to determine the validity of complex logical formulas. It functions as a formal verification framework, enabling the systematic checking of hardware and software system specifications against defined logical constraints to identify inconsistencies or design flaws. The engine distinguishes itself through a combination of theory-specific decision procedures and symbolic execution capabilities. It employs conflict-driven clause learning and backtracking search algorithms to prune search spaces, while maintaining

    Provides a high-performance SMT solver for verifying hardware and software system specifications.

    C++
    Vezi pe GitHub↗11,936
  • statsmodels/statsmodelsAvatar statsmodels

    statsmodels/statsmodels

    11,260Vezi pe GitHub↗

    Statsmodels is a comprehensive Python library designed for statistical modeling, econometric research, and data analysis. It provides a robust framework for estimating and diagnosing a wide range of statistical models, enabling users to perform rigorous hypothesis testing, regression analysis, and complex data exploration within structured environments. The library distinguishes itself through its support for advanced statistical methodologies, including state space representation for dynamic systems and generalized linear frameworks that accommodate non-normal response variables. It offers s

    Employs numerical algorithms to ensure stable parameter estimation for linear and nonlinear models.

    Pythoncount-modeldata-analysisdata-science
    Vezi pe GitHub↗11,260
  • fastai/numerical-linear-algebraAvatar fastai

    fastai/numerical-linear-algebra

    10,703Vezi pe GitHub↗

    This project is a comprehensive library for numerical linear algebra and scientific computing, designed to provide optimized routines for matrix decomposition, statistical modeling, and high-performance data analysis. It serves as both a toolkit for solving complex linear systems and an educational resource for understanding the fundamental algorithms behind matrix factorizations and numerical solvers. The library distinguishes itself through a focus on randomized numerical linear algebra, utilizing probabilistic algorithms and approximate methods to perform dimensionality reduction and matri

    Implements iterative solvers for solving complex linear systems with high precision.

    Jupyter Notebookalgorithmsdata-sciencedeep-learning
    Vezi pe GitHub↗10,703
  • erincatto/box2dAvatar erincatto

    erincatto/box2d

    9,505Vezi pe GitHub↗

    Box2D is a 2D physics engine and rigid body simulator designed to compute collisions, forces, and joints in a two-dimensional plane. It functions as a collision detection library and a constraint-based physics solver that calculates the movement and interaction of solid objects using mass, friction, and restitution properties. The engine ensures consistency across different hardware architectures through a deterministic trigonometry implementation. It prevents high-speed objects from passing through boundaries using continuous collision detection and time-of-impact interpolation. The system

    Uses an iterative sequential impulse solver to resolve constraints and update object velocities for physical stability.

    C
    Vezi pe GitHub↗9,505
  • thealgorithms/c-sharpAvatar TheAlgorithms

    TheAlgorithms/C-Sharp

    8,049Vezi pe GitHub↗

    This project is a collection of reference implementations for algorithms, mathematics, cryptography, compression, and machine learning written in C#. It serves as an educational library providing standard implementations of sorting, searching, and graph theory algorithms. The repository covers a wide range of computational domains, including combinatorial optimization for constraint satisfaction and scheduling, as well as symmetric and classical cryptographic ciphers. It also provides reference code for lossless data compression techniques and fundamental machine learning primitives such as r

    Implements algorithms for finding configurations that satisfy a predefined set of constraints, such as board-game piece placement.

    C#algorithmalgorithmsalgorithms-and-data-structures
    Vezi pe GitHub↗8,049
  • workiva/go-datastructuresAvatar Workiva

    Workiva/go-datastructures

    7,901Vezi pe GitHub↗

    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
    Vezi pe GitHub↗7,901
  • guofei9987/scikit-optAvatar guofei9987

    guofei9987/scikit-opt

    6,583Vezi pe GitHub↗

    scikit-opt is a Python optimization library and numerical framework designed to solve complex global optimization problems. It provides a suite of metaheuristic algorithms and tools for finding global minima or maxima of objective functions. The library implements a variety of nature-inspired and swarm intelligence algorithms, including Genetic Algorithms, Particle Swarm Optimization, Differential Evolution, Simulated Annealing, and Ant Colony Optimization. It includes specialized solvers for discrete combinatorial challenges, such as the Traveling Salesman Problem. The framework supports th

    Offers a suite of algorithms inspired by natural phenomena, including PSO and Genetic Algorithms, for complex search problems.

    Python
    Vezi pe GitHub↗6,583
  • cvxpy/cvxpyAvatar cvxpy

    cvxpy/cvxpy

    6,257Vezi pe GitHub↗

    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

    Pass solver-specific parameters like iteration limits and accuracy tolerances through keyword arguments.

    C++
    Vezi pe GitHub↗6,257
  • nvidia/isaac-gr00tAvatar NVIDIA

    NVIDIA/Isaac-GR00T

    6,222Vezi pe GitHub↗

    Ships a pluggable solver framework for custom multiphysics interactions in simulations.

    Jupyter Notebook
    Vezi pe GitHub↗6,222
  • dimforge/rapierAvatar dimforge

    dimforge/rapier

    5,471Vezi pe GitHub↗

    Rapier este un motor de fizică cross-platform și un solver de dinamică a corpurilor rigide conceput pentru simulări 2D și 3D. Funcționează ca un sistem de detectare a coliziunilor și un framework de simulare robotică, oferind un API consistent pentru calcularea interacțiunilor fizice în medii diferite. Motorul se distinge prin capabilități robotice specializate, inclusiv un toolkit pentru importarea modelelor URDF și STL pentru a controla lanțurile multibody. Suportă mișcarea mecanică precisă prin calcule de cinematică inversă și aplicarea controlerelor PID pentru controlul dinamic al corpului. Suprafața de simulare acoperă o gamă largă de capabilități fizice, inclusiv controlere cinematice de caracter pentru traversarea terenului și modele ray-cast pentru dinamica vehiculelor. Dispune de un pipeline de coliziune multi-fază cu detectare continuă a coliziunilor pentru a preveni tunneling-ul obiectelor, precum și un sistem pentru constrângeri de mișcare articulată folosind articulații revolute, prismatice și sferice. Framework-ul include utilitare pentru importarea definițiilor geometrice externe prin formate STL, URDF și XML și oferă o interfață de debug agnostic-backend pentru vizualizarea stărilor fizice interne și a scenelor 3D.

    Implements an iterative sequential impulse solver to resolve rigid body interactions and joint constraints.

    Rust
    Vezi pe GitHub↗5,471
Înapoi12Înainte
  1. Home
  2. Scientific & Mathematical Computing
  3. Numerical and Mathematical Foundations
  4. Optimization & Solvers

Explorează sub-etichetele

  • Constraint Solvers6 sub-tag-uriAlgorithms and libraries for finding solutions to constraint satisfaction problems. **Distinguishing note:** None available; minting under scientific computing umbrella.
  • Iterative Solvers3 sub-tag-uriNumerical algorithms for minimizing loss functions or maximizing likelihood in statistical models. **Distinct from Optimization & Solvers:** Distinct from general optimization: focuses on parameter estimation for statistical models rather than physics or control systems.
  • Mathematical Optimization KernelsSpecialized computational kernels designed to accelerate mathematical operations and reduce memory overhead in large-scale models. **Distinguishing note:** Focuses on low-level kernel optimization for specific model architectures rather than general-purpose mathematical libraries.
  • Metaheuristic OptimizationOptimization algorithms inspired by natural phenomena for solving complex search problems. **Distinguishing note:** Covers swarm-based metaheuristics distinct from geometric or potential-field path planning.
  • Nonlinear Optimization Solvers3 sub-tag-uriNumerical 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.
  • Probabilistic SolversAlgorithms that derive output structures from input examples using probabilistic constraint satisfaction. **Distinguishing note:** Distinct from general procedural generation by its reliance on probabilistic inference from example data.