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数据与复现
bgpt-paper-search
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数据与复现
LI
linear-solvers
维护者 FreedomIntelligence · 最近更新 2026年4月1日
linear-solvers：Select 、 configure linear solvers ，用于 systems Ax=b in dense 、 sparse problems。 适合在choosing direct vs iterative methods，diagnosing convergence issues，estimating conditioning，selecting preconditioners，或 debugging stagnation in GMRES/CG/BiCGSTAB时使用。
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/linear-solvers
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- 提供 universal workflow to select solver，assess conditioning，、 diagnose convergence ，用于 linear systems arising in numerical 模拟s。
- python3 scripts/sparsity_stats.py --matrix .npy --json。
原始文档
SKILL.md 摘录
Requirements
- Python 3.8+
- NumPy, SciPy (for matrix operations)
- See individual scripts for dependencies
Inputs to Gather
| Input | Description | Example |
|---|---|---|
| Matrix size | Dimension of system | n = 1000000 |
| Sparsity | Fraction of nonzeros | 0.01% |
| Symmetry | Is A = Aᵀ? | yes |
| Definiteness | Is A positive definite? | yes (SPD) |
| Conditioning | Estimated condition number | 10⁶ |
Script Outputs (JSON Fields)
| Script | Key Outputs |
|---|---|
scripts/solver_selector.py | recommended, alternatives, notes |
scripts/convergence_diagnostics.py | rate, stagnation, recommended_action |
scripts/sparsity_stats.py | nnz, density, bandwidth, symmetry |
scripts/preconditioner_advisor.py | suggested, notes |
scripts/scaling_equilibration.py | row_scale, col_scale, notes |
scripts/residual_norms.py | residual_norms, relative_norms, converged |
适用场景
- 适合在choosing direct vs iterative methods，diagnosing convergence issues，estimating conditioning，selecting preconditioners，或 debugging stagnation in GMRES/CG/BiCGSTAB时使用。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
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