{"id":19391018,"url":"https://github.com/madrury/linalg","last_synced_at":"2026-04-08T14:31:36.407Z","repository":{"id":75757776,"uuid":"42832121","full_name":"madrury/linalg","owner":"madrury","description":"A linear algebra library in C.  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The code is optimized for readability and clarity instead of raw efficiency (though it tries not to ignore issues of efficiency completely).\n\nLinear Algebra\n--------------\n\n`linalg` contains two datatypes in its core linear algebra engine, `vector` and `matrix`:\n\n  - `vector` is a (one dimensional) vector of real numbers (C `double`s).  The underlying data is stored as a C array, and so occupies contiguous memory locations in the computer's memory.\n  - `matrix` is a (two dimensional) matrix of real numbers (C `double`s).  The underlying data is stored in row-major order, so each row occupies contiguous memory locations in the computer's memory.\n\nTo complement these data types, `linalg` contains many functions for performing linear algebraic operations.  For example\n\n  - `matrix_vector_multiply` computes the product vector of a matrix and vector.\n  - `matrix_multiply` computes the product matrix of two matrices.\n  - `matrix_multiply_MtN` computes the product of the transpose of one matrix with another.\n\nLinear equations can be solved using `linsolve_qr`, which adopts a strategy of computing the QR matrix factorization of the left hand side.  To access the underlying matrix factorization, use `qr_decomp`.\n\nRegression\n----------\n\n`linalg` also includes functions for regression.  Use `linreg_fit` to fit a linear regression given a design matrix `X` and a response vector `y`.\n\nTests\n-----\n\nThe routines in `linalg` are extensively unit tested, which also serve as simple examples of use.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmadrury%2Flinalg","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmadrury%2Flinalg","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmadrury%2Flinalg/lists"}