{"id":25019624,"url":"https://github.com/djeada/numerical-methods","last_synced_at":"2025-04-13T04:09:01.081Z","repository":{"id":38063582,"uuid":"174411276","full_name":"djeada/Numerical-Methods","owner":"djeada","description":"Comprehensive library of numerical methods implemented in Python. It includes solutions to various mathematical problems, detailed explanations of each method, illustrative examples, and comparisons with prominent scientific libraries like Numpy, Scikit-Learn, and SciPy. ","archived":false,"fork":false,"pushed_at":"2025-04-09T19:04:44.000Z","size":2151,"stargazers_count":11,"open_issues_count":1,"forks_count":7,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-13T04:08:47.898Z","etag":null,"topics":["jupyter-notebook","linear-algebra","matplotlib","numerical-methods","numpy","python","scikit-learn","scipy"],"latest_commit_sha":null,"homepage":"https://adamdjellouli.com/articles/numerical_methods","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/djeada.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2019-03-07T19:57:48.000Z","updated_at":"2025-04-10T08:39:30.000Z","dependencies_parsed_at":"2023-01-29T22:31:13.199Z","dependency_job_id":"9d58a15b-1765-4055-9f5e-da41849fd385","html_url":"https://github.com/djeada/Numerical-Methods","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FNumerical-Methods","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FNumerical-Methods/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FNumerical-Methods/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FNumerical-Methods/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/djeada","download_url":"https://codeload.github.com/djeada/Numerical-Methods/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248661704,"owners_count":21141450,"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":["jupyter-notebook","linear-algebra","matplotlib","numerical-methods","numpy","python","scikit-learn","scipy"],"created_at":"2025-02-05T11:51:14.595Z","updated_at":"2025-04-13T04:09:01.050Z","avatar_url":"https://github.com/djeada.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Numerical Methods\nComprehensive library of numerical methods implemented in Python. It includes solutions to various mathematical problems, detailed explanations of each method, illustrative examples, and comparisons with prominent scientific libraries like Numpy, Scikit-Learn, and SciPy.\n\n![Demo](https://user-images.githubusercontent.com/37275728/189313603-b409b2be-41b5-4de6-9d4f-2bd8f6e41565.png)\n\n## Requirements\n\n* Python 3.10+\n* Whatever library is mentioned in the project's requirements.txt file.\n\n## Installation\n\nTo run *.py* scripts the recommended approach is to use virtualenv:\n\n    $ virtualenv env\n    $ source env/bin/activate\n    $ pip install -r requirements.txt\n    $ python path/to/main.py\n\nFor *.ipynb* notebooks you do not need to install anything locally on your PC. You may run all of the examples on the official website of Jupyter Notebooks using a demo version:\n\nhttps://jupyter.org/try\n\nTo run the notebooks locally, use the following command:\n\n    $ jupyter notebook path/to/notebook.ipynb\n\n## Topics\n\n### Root And Extrema Finding\n\nMethod | Notes | Implementation | Examples\n------ | ----- | -------------- | --------\n| Bisection Method | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/1_root_and_extrema_finding/bisection_method.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/1_root_and_extrema_finding/bisection_search/implementation/bisection_search.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/1_root_and_extrema_finding/bisection_search/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Secant Method | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/1_root_and_extrema_finding/secant_method.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/1_root_and_extrema_finding/secant_method/implementation/secant_method.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/1_root_and_extrema_finding/secant_method/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Relaxation Method | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/1_root_and_extrema_finding/relaxation_method.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/1_root_and_extrema_finding/relaxation_method/implementation/relaxation_method.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/1_root_and_extrema_finding/relaxation_method/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Golden Ratio Search | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/1_root_and_extrema_finding/golden_ratio_search.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/1_root_and_extrema_finding/golden_ratio_search/implementation/golden_ratio_search.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/1_root_and_extrema_finding/golden_ratio_search/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Newton Raphson | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/1_root_and_extrema_finding/newtons_method.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/1_root_and_extrema_finding/newton_raphson/implementation/newton_raphson.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/1_root_and_extrema_finding/newton_raphson/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Gradient Descent | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/1_root_and_extrema_finding/gradient_descent.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/1_root_and_extrema_finding/gradient_descent/implementation/gradient_descent.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/1_root_and_extrema_finding/gradient_descent/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n\n### Systems Of Equations\n\nMethod | Notes | Implementation | Examples\n------ | ----- | -------------- | --------\n| Inverse Matrix | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/2_systems_of_equations/inverse_matrix.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/2_systems_of_equations/matrix_inverse/implementation/inverse_matrix.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/2_systems_of_equations/matrix_inverse/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Gaussian Elimination | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/2_systems_of_equations/gaussian_elimination.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/2_systems_of_equations/gaussian_elimination/implementation/gaussian_elimination.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/2_systems_of_equations/gaussian_elimination/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| LU Decomposition | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/2_systems_of_equations/lu_decomposition.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/2_systems_of_equations/lu_decomposition/implementation/lu_decomposition.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/2_systems_of_equations/lu_decomposition/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Gauss Seidel Method | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/2_systems_of_equations/gauss_seidel.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/2_systems_of_equations/gauss_seidel/implementation/gauss_seidel.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/2_systems_of_equations/gauss_seidel/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Jacobi Method | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/2_systems_of_equations/jacobi_method.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/2_systems_of_equations/jacobi_method/implementation/jacobi_method.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/2_systems_of_equations/jacobi_method/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n\n### Differentiation\n\nMethod | Notes | Implementation | Examples\n------ | ----- | -------------- | --------\n| Taylor series | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/3_differentiation/taylor_series.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/3_derivatives/taylor_series/implementation/taylor_series.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/3_derivatives/taylor_series/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Forward difference | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/3_differentiation/forward_difference.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/3_derivatives/forward_difference/implementation/forward_difference.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/3_derivatives/forward_difference/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Backward difference | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/3_differentiation/backward_difference.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/3_derivatives/backward_difference/implementation/backward_difference.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/3_derivatives/backward_difference/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Central difference | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/3_differentiation/central_difference.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/3_derivatives/central_difference/implementation/central_difference.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/3_derivatives/central_difference/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n\n### Integration\n\nMethod | Notes | Implementation | Examples\n------ | ----- | -------------- | --------\n| Midpoint Rule | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/4_integration/midpoint_rule.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/4_integration/midpoint_rule/implementation/midpoint_rule.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/tree/master/src/4_integration/midpoint_rule/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Trapezoidal Rule | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/4_integration/trapezoidal_rule.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/4_integration/trapezoid_rule/implementation/trapezoid_rule.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/tree/master/src/4_integration/trapezoid_rule/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Simpson's Rule | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/4_integration/simpsons_rule.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/4_integration/simpson/implementation/simpson_rule.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/tree/master/src/4_integration/simpson/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Monte Carlo Integration | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/4_integration/monte_carlo.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/4_integration/monte_carlo_integral/implementation/monte_carlo_integral.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/tree/master/src/4_integration/monte_carlo_integral/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n\n### Matrices\n\nMethod | Notes | Implementation | Examples\n------ | ----- | -------------- | --------\n| Eigenvalues and Eigenvectors | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/5_matrices/eigenvalues_and_eigenvectors.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/5_matrices/eigenvalues_and_eigenvectors/implementation/eigenvalues_and_eigenvectors.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/tree/master/src/5_matrices/eigenvalues_and_eigenvectors/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Power Method | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/5_matrices/power_method.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/5_matrices/power_method/implementation/power_method.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/tree/master/src/5_matrices/power_method/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| QR Method | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/5_matrices/qr_method.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/5_matrices/qr_method/implementation/qr_method.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/tree/master/src/5_matrices/qr_method/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Eigenvalue Decomposition (EVD) | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/5_matrices/eigen_value_decomposition.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/5_matrices/eigen_value_decomposition/implementation/eigen_value_decomposition.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/tree/master/src/5_matrices/eigen_value_decomposition/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Singular Value Decomposition (SVD) | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/5_matrices/singular_value_decomposition.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/5_matrices/singular_value_decomposition/implementation/singular_value_decomposition.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/tree/master/src/5_matrices/singular_value_decomposition/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n\n### Regression\n\nMethod | Notes | Implementation | Examples\n------ | ----- | -------------- | --------\n| Linear Interpolation | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/6_regression/linear_interpolation.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/6_regression/linear_interpolation/implementation/linear_interpolation.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Least Squares | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/6_regression/least_squares.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/6_regression/least_squares/implementation/least_squares.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Cubic Spline | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/6_regression/cubic_spline_interpolation.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/6_regression/cubic_spline/implementation/cubic_spline.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Lagrange Polynomial | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/6_regression/lagrange_polynomial_interpolation.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/6_regression/lagrange_polynomial/implementation/lagrange_polynomial.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/6_regression/lagrange_polynomial/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Newton's Polynomial | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/6_regression/newton_polynomial.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/6_regression/newton_polynomial/implementation/newton_polynomial.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/6_regression/newton_polynomial/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Gaussian Interpolation | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/6_regression/gaussian_interpolation.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/6_regression/gaussian_interpolation/implementation/gaussian_interpolation.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/6_regression/gaussian_interpolation/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Thin Plate Spline Interpolation | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/6_regression/thin_plate_spline_interpolation.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/6_regression/thin_plate_spline_interpolation/implementation/thin_plate_spline_interpolation.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/6_regression/thin_plate_spline_interpolation/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n\n### Ordinary Differential Equations\n\nMethod | Notes | Implementation | Examples\n------ | ----- | -------------- | --------\n| Euler's Method | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/7_ordinary_differential_equations/eulers_method.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/7_ordinary_differential_equations/eulers/implementation/eulers.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/tree/master/src/7_ordinary_differential_equations/eulers/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Heun's Method | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/7_ordinary_differential_equations/heuns_method.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/7_ordinary_differential_equations/heuns/implementation/heuns.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/tree/master/src/7_ordinary_differential_equations/heuns/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Runge Kutta | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/7_ordinary_differential_equations/runge_kutta.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/7_ordinary_differential_equations/runge_kutta/implementation/runge_kutta.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/tree/master/src/7_ordinary_differential_equations/runge_kutta/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n| Picard's Method | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/notes/7_ordinary_differential_equations/picards_method.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/blob/master/src/7_ordinary_differential_equations/picard/implementation/picard.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e \u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Numerical-Methods/tree/master/src/7_ordinary_differential_equations/picard/examples/example.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e \u003c/a\u003e |\n\n## References\n\n### Books\n\n- **Burden, Richard L.; Faires, J. Douglas**  \n  *Numerical Analysis, 9th Edition*  \n  [Amazon Link](https://amzn.to/4jcJ6Ku)\n\n- **Epperson, James F.**  \n  *An Introduction to Numerical Methods and Analysis*  \n  [Amazon Link](https://amzn.to/4hZUdWl)\n\n- **Press, William H.; Teukolsky, Saul A.; Vetterling, William T.; Flannery, Brian P.**  \n  *Numerical Recipes: The Art of Scientific Computing, 3rd Edition*  \n  [Amazon Link](https://amzn.to/4liZQ4w)\n\n- **Heath, Michael T.**  \n  *Scientific Computing: An Introductory Survey*  \n  [Amazon Link](https://amzn.to/41YDYE5)\n\n- **Giordano, Nicholas J.; Nakanishi, Hisao**  \n  *Computational Physics*  \n  [Amazon Link](https://amzn.to/4cmFQtN)\n\n- **Chapra, Steven C.**  \n  *Applied Numerical Methods with MATLAB for Engineers and Scientists*  \n  [Amazon Link](https://amzn.to/42h0Z49)\n\n- **LeVeque, Randall J.**  \n  *Finite Difference Methods for Ordinary and Partial Differential Equations*  \n  [Amazon Link](https://amzn.to/3RyCpqm)\n\n### Online Resources\n\n- [MIT OpenCourseWare: Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/)\n- [Wikiversity: Cubic Spline Interpolation](https://en.wikiversity.org/wiki/Cubic_Spline_Interpolation)\n- [Statnotes: Statistical Concepts by Dr. Garson](https://faculty.chass.ncsu.edu/garson/PA765/statnote.htm)\n- [Numerical Methods Lectures, SDSU](https://jmahaffy.sdsu.edu/courses/s18/math541/Lectures.html)\n- [Numerical Analysis: U of A Engineering Courses](https://engcourses-uofa.ca/books/numericalanalysis)\n- [Numerical Methods by John Foster, UT Austin](https://johnfoster.pge.utexas.edu/numerical-methods-book)\n- [Numerical Methods Course Material, NYU](https://math.nyu.edu/~stadler/num1/material/)\n- [Fundamentals of Numerical Computation by Tobin A. Driscoll and Richard J. Braun](https://fncbook.com/)\n\n## Contributing\n\nContributions are welcome! If you'd like to propose a major change, please open an issue first to discuss your ideas. \n\nWhen contributing, ensure you update relevant tests as needed to maintain the integrity of the project.\n\n## License\n\nThis project is licensed under the [MIT License](https://choosealicense.com/licenses/mit/).\n\n## Star History\n\n[![Star History Chart](https://api.star-history.com/svg?repos=djeada/Numerical-Methods\u0026type=Date)](https://star-history.com/#djeada/Numerical-Methods\u0026Date)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdjeada%2Fnumerical-methods","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdjeada%2Fnumerical-methods","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdjeada%2Fnumerical-methods/lists"}