{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"LinearOperator abstracts structured matrix operations in PyTorch, enabling efficient computation on large matrices without materializing them in memory by exploiting their algebraic structure.","skillfed_tags":["matrix-free-computation","kernel-methods","gaussian-processes"],"use_cases":["Sampling from high-dimensional multivariate Gaussians with structured covariance matrices without materializing the full matrix.","Solving linear systems with low-rank-plus-diagonal structure efficiently via the Woodbury formula.","Kernel matrix operations in Gaussian process inference where the kernel matrix is too large to fit in memory.","Composing multiple structured matrix types and automatically leveraging efficient algorithms for the resulting composite structure.","Prototyping different structured matrix approximations for a model without rewriting linear algebra code for each variant."],"what_it_does":"LinearOperator is a PyTorch package that lets you define and work with structured matrices\u2014such as low-rank-plus-diagonal or triangular matrices\u2014without explicitly constructing them as dense tensors. Instead of materializing a large matrix in memory, you define it compositionally (e.g., `LowRankRootLinearOperator(C) + DiagLinearOperator(d)`) and LinearOperator automatically routes operations like solves and matrix-vector products through efficient structure-exploiting routines. It depends on torch and scipy and integrates with PyTorch's standard linear algebra API.\n\nThe package is currently in beta and supports positive semi-definite and triangular matrices. Its main value is enabling memory-efficient and computationally efficient workflows for large structured matrices\u2014particularly useful in probabilistic modeling and kernel methods\u2014without requiring you to hand-code specialized linear algebra routines. Operations like `torch.linalg.solve(A, b)` work transparently on LinearOperator objects, dispatching to the best available algorithm based on the matrix's declared structure.","worth_installing":"Yes, if you work with large structured matrices in PyTorch and need memory efficiency or computational speed. The low install friction and active maintenance support adoption. However, the pre-alpha status and incomplete feature set (symmetric, generic square, rectangular, and sparse operators not yet implemented) mean you should verify that positive semi-definite and triangular operators cover your use case and accept that the API may change."},"id":"linear-operator","links":{"html":"https://skillfed.io/packages/linear-operator","md":"https://skillfed.io/packages/linear-operator.md","pypi":"https://pypi.org/project/linear-operator/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-27","license_spdx":null,"license_treatment":"permissive","name":"linear-operator","python_support":"supports_current","summary":"A linear operator implementation, primarily designed for finite-dimensional positive definite operators (i.e. kernel matrices)."},"popularity":{"monthly_downloads":1139454,"position":4309,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.6.1"}