16 repositorios
Collections of interactive software and solvers for mathematical computation.
Distinct from Problem Solving Guides: Distinct from general algorithmic problem solving: focuses on interactive tools for mathematical tasks rather than coding challenges.
Explore 16 awesome GitHub repositories matching scientific & mathematical computing · Mathematical Problem Solving Toolkits. Refine with filters or upvote what's useful.
This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed
Solves complex scientific equations by systematically exploring mathematical techniques and integrating symbolic reasoning.
This project is a comprehensive, crowdsourced directory of mathematical resources, functioning as a decentralized index of external educational materials. It organizes a vast collection of textbooks, lecture notes, and research tools into a hierarchical taxonomy, allowing users to navigate diverse mathematical disciplines through a version-controlled repository. The collection distinguishes itself by acting as a central hub for both academic discovery and practical application. It provides access to a wide array of interactive software, visualization tools, and step-by-step solvers designed t
Provides a collection of interactive software, visualization tools, and solvers for complex computations.
This project is a cryptographic mining protocol that establishes a distributed compute network for solving complex mathematical tasks. It functions as a decentralized infrastructure where registered mining nodes participate in proof of work mining to solve network problems in exchange for rewards. The system specializes in quantum-inspired problem solving by mapping tasks into Ising mathematical structures. These problems are processed using a hardware-agnostic computation model, allowing solvers to execute tasks across CPU, GPU, or quantum processing units. The protocol includes tools for m
Computes solutions for Ising mathematical problems using CPU, GPU, or QPU resources.
This project is a comprehensive reference for algorithms and data structures used to solve complex computational problems in competitive programming. It serves as a technical resource for implementing advanced mathematical programming, computational geometry, and graph theory. The repository provides detailed implementation guides for diversifying algorithmic techniques, including top-down and bottom-up dynamic programming optimization, number theory, and linear algebra. It features specific guides for complex tasks such as constructing planar graphs, solving linear Diophantine equations, and
Provides technical guides and solver implementations for number theory, linear algebra, and combinatorics.
OpenCompass is an open-source framework for standardized benchmarking of large language models. It provides a configurable evaluation pipeline that supports both objective and subjective assessment, using a dual-engine architecture to handle closed-form answer comparison and open-ended response rating. The framework is designed as a modular platform where datasets, models, and metrics are composed through declarative YAML configuration files. The framework distinguishes itself through its extensible model integration layer, which supports custom models, HuggingFace models, and third-party API
Measures numerical computation and problem-solving skills at high school and university levels using objective evaluation.
Performs multi-step mathematical reasoning to answer arithmetic, algebraic, and competition-level math questions.
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
Transforms mixed-integer problems into forms accepted by solvers while preserving convex structure.
Giskard es un framework de evaluación, librería de pruebas y sistema de monitoreo de calidad para modelos de lenguaje grandes (LLM) y agentes de IA. Sirve como un kit de herramientas para cuantificar el rendimiento y la fiabilidad del modelo, proporcionando capacidades especializadas para validar pipelines de generación aumentada por recuperación (RAG). El proyecto se distingue por una herramienta de red teaming automatizada y un escáner de seguridad diseñado para identificar vulnerabilidades, inyecciones de prompts y riesgos de seguridad. Utiliza sondeo adversarial y generación sintética de casos límite para cuantificar la robustez del modelo y detectar la divulgación de información. La plataforma cubre una amplia gama de capacidades, incluyendo la detección de precisión factual y alucinaciones, benchmarking de razonamiento y lógica, y detección de sesgos. Proporciona herramientas para pruebas de regresión, evaluación de componentes RAG y la generación automatizada de casos de prueba a partir de bases de conocimiento. El sistema incluye funciones de gestión para espacios de trabajo colaborativos, control de acceso basado en roles y pipelines de evaluación programados para monitorear la deriva del rendimiento a lo largo del tiempo.
Tests mathematical reasoning by assessing both the final correctness and the quality of the multi-step reasoning process.
Python-Guide-CN es una traducción al chino de una guía completa sobre programación idiomática en Python y desarrollo de software. Sirve como un tutorial de programación curado y referencia del ecosistema, proporcionando un camino estructurado para aprender la sintaxis de Python, las bibliotecas estándar y los patrones de codificación profesional. El proyecto se distingue por ofrecer instrucciones detalladas para configurar entornos de desarrollo en Windows, macOS y Linux. Se centra específicamente en la selección de intérpretes y la gestión de entornos virtuales para asegurar un espacio de trabajo consistente. La guía cubre una amplia gama de capacidades técnicas, incluyendo flujos de trabajo de pruebas de software, distribución de paquetes y la implementación de mejores prácticas de codificación. Proporciona además orientación sobre desarrollo web, construcción de APIs REST y computación científica, incluyendo análisis y visualización de datos.
Recommends toolkits and solvers for linear algebra and calculus using array-based data structures.
This project is a language model evaluation framework and benchmarking tool designed to measure the accuracy and performance of models across diverse datasets. It provides a system for implementing model-based graders, running standardized tests for mathematical reasoning, coding, and factuality, and calculating quantified performance metrics such as precision, recall, F1 scores, and pass-at-k. The framework utilizes model-based grading and rubrics to validate response quality against expert-defined criteria. It includes a multi-model benchmarking loop and a model-agnostic API interface to co
Measures language model accuracy on mathematical reasoning tasks by prompting step-by-step solving and comparing answers against ground truth.
OpenGpt is an agent orchestration platform and multimodal interface designed for building and deploying specialized AI personas. It allows users to create task-oriented agents with custom system prompts and behavioral constraints to automate professional, creative, and technical workflows. The project features a prompt engineering workflow that transforms simple user inputs into structured instructions to improve model accuracy. It integrates retrieval-augmented generation by connecting vector databases to the chat interface, enabling context-aware responses from private datasets. The platfo
Calculates solutions for mathematical questions and provides step-by-step prose explanations.
simpleRL-reason is a training framework designed to improve mathematical and logical deduction in large language models. It utilizes reinforcement learning and policy optimization to enhance the accuracy and transparency of step-by-step deduction chains. The project implements a pipeline that establishes baseline capabilities through supervised fine-tuning before applying reinforcement learning to maximize deductive accuracy. It features a reward modeling toolkit that calculates scalar feedback by comparing generated reasoning steps against verified mathematical ground truths. The framework
Includes a standardized evaluation suite to objectively measure numerical computation and multi-step problem-solving skills.
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
Formulates and solves linear, quadratic, and nonlinear optimization problems including custom gradient specifications.
Riskfolio-Lib is a Python portfolio optimization library and convex risk management tool. It provides a framework for calculating optimal asset allocations using convex risk measures and mathematical programming solvers, supporting linear, quadratic, and semidefinite programming. The library features a hierarchical risk parity framework and financial asset clustering tools to group similar instruments and improve diversification. It includes a portfolio backtesting engine for simulating investment strategies using historical data and cross-validation. The system covers a broad range of quant
Integrates with various mathematical program solvers to execute linear, quadratic, and semidefinite programming.
This project is a containerized search infrastructure designed to deploy a privacy-focused metasearch engine. It acts as a self-hosted search proxy that aggregates results from multiple external web, image, and academic search providers while anonymizing requests and stripping trackers to protect user identity. The system utilizes Docker to orchestrate the search instance, integrating caching mechanisms and reverse proxy support to ensure a private and efficient search environment. It employs a modular adapter-based integration to standardize diverse external API responses and a processing pi
Parses and calculates mathematical expressions entered by the user directly within the search interface.
DataFlow is an agent-based workflow orchestrator and data pipeline designed to synthesize, clean, and augment large-scale datasets for training large language models. It functions as a synthetic data generator and text curation tool, utilizing an intelligent assistant to assemble modular processing operators into functional pipelines based on user requirements. The project distinguishes itself through a low-code approach, providing a web-based visual interface for designing and monitoring multi-stage execution flows. It features an operator-based registry system that allows for the integratio
Removes non-mathematical or invalid problems from a dataset to ensure only valid reasoning tasks are processed.