pymc-devs/pytensor
Jun 2025 – Oct 2025
2 Months active
Languages Used
Python
Technical Skills
Linear AlgebraNumerical ComputingPerformance OptimizationScientific Computing
Aidan Costello
PROFILE
Aidan Costello
Worked on performance and robustness improvements for linear algebra routines in the pymc-devs/pytensor repository, focusing on Cholesky decomposition and related solvers. Refactored existing Python code to replace high-level scipy.linalg calls with direct LAPACK routines using scipy.linalg.get_lapack_funcs, optimizing for contiguous arrays and enhancing error handling for non-positive definite matrices and empty inputs. Added benchmarking and test scaffolding to quantify performance gains and support future validation. These changes aimed to improve throughput and numerical stability in scientific and probabilistic computing workflows, leveraging skills in linear algebra, numerical computing, and performance optimization to streamline core functionality for downstream users.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
255
Activity Months2
Your Network
50 peopleShared Repositories
50
Work History
October 2025
1 Commits • 1 Features
Oct 1, 20252025-10 Monthly Summary for pymc-devs/pytensor: Implemented LAPACK-backed linear algebra path by replacing scipy.linalg calls for cho_solve, lu_factor, and solve_triangular. Tidied imports and removed an ndim check. Added empty tests scaffold for the new functionality. These changes aim to boost performance and numerical stability for linear-algebra workflows, improving end-user throughput in probabilistic modeling and downstream PyTensor/PyMC workloads.
1 Commits • 1 Features
Oct 1, 20252025-10 Monthly Summary for pymc-devs/pytensor: Implemented LAPACK-backed linear algebra path by replacing scipy.linalg calls for cho_solve, lu_factor, and solve_triangular. Tidied imports and removed an ndim check. Added empty tests scaffold for the new functionality. These changes aim to boost performance and numerical stability for linear-algebra workflows, improving end-user throughput in probabilistic modeling and downstream PyTensor/PyMC workloads.
October 2025
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 — pymc-devs/pytensor: Focused Cholesky optimization to improve performance and robustness. Replaced high-level calls with direct LAPACK routines via scipy.linalg.get_lapack_funcs, with optimizations for contiguous arrays, improved error handling for non-positive definite matrices, support for empty inputs, and added a benchmarking test to quantify gains. Implemented under commit 236e50d3317bbfeca390410ae42003c2d8f24028.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 — pymc-devs/pytensor: Focused Cholesky optimization to improve performance and robustness. Replaced high-level calls with direct LAPACK routines via scipy.linalg.get_lapack_funcs, with optimizations for contiguous arrays, improved error handling for non-positive definite matrices, support for empty inputs, and added a benchmarking test to quantify gains. Implemented under commit 236e50d3317bbfeca390410ae42003c2d8f24028.
Activity
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Quality Metrics
Correctness95.0%
Maintainability80.0%
Architecture80.0%
Performance95.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Linear AlgebraNumerical ComputingPerformance OptimizationScientific Computing
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