{"id":21336753,"url":"https://github.com/nazmusweb-coding/numerical-methods","last_synced_at":"2026-05-19T11:06:06.793Z","repository":{"id":257191892,"uuid":"857576800","full_name":"nazmusweb-coding/Numerical-Methods","owner":"nazmusweb-coding","description":"Some Numerical Methods Implementation in C++ and Python","archived":false,"fork":false,"pushed_at":"2024-09-17T15:55:43.000Z","size":23,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-22T14:43:20.358Z","etag":null,"topics":["cpp","numerical-algorithms","numerical-calculations","numerical-computation","numerical-integration","numerical-methods","python"],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nazmusweb-coding.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-15T02:55:26.000Z","updated_at":"2024-09-30T07:18:51.000Z","dependencies_parsed_at":"2024-09-17T18:34:09.758Z","dependency_job_id":"3f5c7277-c1b6-4324-a949-143d665b532c","html_url":"https://github.com/nazmusweb-coding/Numerical-Methods","commit_stats":null,"previous_names":["nazmusweb-coding/numerical-methods"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nazmusweb-coding%2FNumerical-Methods","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nazmusweb-coding%2FNumerical-Methods/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nazmusweb-coding%2FNumerical-Methods/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nazmusweb-coding%2FNumerical-Methods/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nazmusweb-coding","download_url":"https://codeload.github.com/nazmusweb-coding/Numerical-Methods/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243814893,"owners_count":20352038,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cpp","numerical-algorithms","numerical-calculations","numerical-computation","numerical-integration","numerical-methods","python"],"created_at":"2024-11-21T23:54:42.852Z","updated_at":"2026-05-19T11:06:06.721Z","avatar_url":"https://github.com/nazmusweb-coding.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Numerical Methods Repository\n\nWelcome to the Numerical Methods repository! This project includes implementations of various numerical methods in both C++ and Python. The Python implementations are designed to mimic C++ conventions to help C++ coders transition smoothly to Python.\n\n## Methods Implemented\n\n### 1. Simple Iteration / Brute Force Method\nA basic iterative approach for solving equations.\n\n### 2. Newton-Raphson Method\nA numerical technique for finding successively better approximations to the roots of a real-valued function.\n\n### 3. Bisection Method\nA root-finding method that repeatedly bisects an interval and then selects a subinterval in which a root must lie for further processing.\n\n### 4. Numerical Differentiation\n   - **Forward Finite Differences:** Approximates the derivative of a function using forward differences.\n   - **Central Finite Differences:** Approximates the derivative using central differences.\n   - **Backward Finite Differences:** Approximates the derivative using backward differences.\n\n### 5. Numerical Integration\n   - **Trapezoidal Rule:** Approximates the integral of a function using trapezoids.\n   - **Simpson's 1/3 Rule:** Approximates the integral of a function using parabolic segments.\n   - **Simpson's 3/8 Rule:** A variation of Simpson's rule using cubic polynomials.\n   - **Simpson's 1/3 Double Integration Rule:** Extends Simpson's 1/3 rule to double integrals.\n\n### 6. Interpolation\n   - **Linear Interpolation:** Estimates unknown values by assuming a linear relationship between known values.\n   - **Lagrange's Interpolation:** Provides a polynomial interpolation through a set of data points.\n   - **Newton's Method:** Provides an interpolating polynomial in Newton form.\n\n## Installation\n\nTo use the C++ and Python implementations, you'll need to have the appropriate compilers or interpreters installed:\n\n### C++:\n- Install a C++ compiler (e.g., `g++` or `clang++`).\n\n### Python:\n- Ensure you have Python 3.x installed. You can download it from [python.org](https://www.python.org/).\n\n## Usage\n\n### C++\n\nTo compile and run the C++ code, navigate to the C++ source directory and use the following commands:\n\n```bash\ng++ -o method_name method_name.cpp\n./method_name\n```\n\n##### Alternative Approache:\nYou can use an [online compiler](https://www.programiz.com/cpp-programming/online-compiler/) such as Programiz to compile and run your C++ code directly in the browser.\n\n### Python\n\nTo run the Python code, navigate to the Python source directory and use the following command:\n\n```bash\npython method_name.py\n```\n\n##### Alternative Approaches:\n- Google Colab: Use [Google Colab](https://colab.research.google.com/notebooks/intro.ipynb#scrollTo=5Y9LXc69ndOO) to run your Python code in a cloud-based environment.\n\n- JupyterLite: Try [JupyterLite](https://jupyterlite.github.io/demo/lab/index.html) (to try without login) for an online Jupyter notebook experience without needing to log in.\n\nThese platforms provide suitable environments for Python and are generally better than other online interpreters available.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnazmusweb-coding%2Fnumerical-methods","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnazmusweb-coding%2Fnumerical-methods","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnazmusweb-coding%2Fnumerical-methods/lists"}