A numerical implementation of the LSQR algorithm for solving over-determined and rank-deficient systems. It utilizes the Lanczos bidiagonalization process to provide superior numerical stability compared to standard conjugate gradient methods.
Tech Stack
PythonMathematics & Statistics
Why LSQR-solver-course?
• High numerical stability
• Memory efficient for sparse matrices
• Handles rank-deficient systems
Limitations
• Requires mathematical expertise
• No GUI
• Limited documentation
3/4/2026
Last Update
14
Forks
0
Issues
MIT
License
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Competitor Cost
-$1,440
/ year (est. based on MATLAB Optimization Toolbox)
