# Solver Decision Tree

> Comprehensive decision guide for selecting linear solvers for Ax = b.

- Skill: `tools-only/solver-decision-tree` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/solver-decision-tree`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/solver-decision-tree/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-29
- Page: https://skillmd.com/skills/tools-only/solver-decision-tree

---

# Solver Decision Tree

Comprehensive decision guide for selecting linear solvers for Ax = b.

## Matrix Classification

### Key Properties to Determine

| Property | How to Check | Impact |
|----------|--------------|--------|
| Symmetric | A = Aᵀ | Enables CG, MINRES |
| Positive definite | All eigenvalues > 0 | Enables CG, Cholesky |
| Sparse | nnz/n² < 0.01 | Iterative preferred |
| Well-conditioned | κ(A) < 10⁶ | Standard methods work |
| Banded | Nonzeros near diagonal | Band solvers efficient |

### Quick Classification

```
Check symmetry: ||A - Aᵀ||_F / ||A||_F < ε
Check SPD: try Cholesky, if succeeds → SPD
Check sparsity: nnz / n² gives density
Check conditioning: estimate κ using power iteration or Lanczos
```

## Primary Decision Tree

```
START: Need to solve Ax = b
│
├─ Is n < 5000 and matrix dense?
│   └── YES → Use DIRECT solver
│       ├── Symmetric PD → Cholesky (LLᵀ)
│       ├── Symmetric indefinite → LDLᵀ (Bunch-Kaufman)
│       ├── Nonsymmetric → LU with pivoting
│       └── Least squares → QR or SVD
│
└── NO → Use ITERATIVE solver
    │
    ├─ Is matrix symmetric?
    │   │
    │   ├── YES → Is it positive definite?
    │   │   │
    │   │   ├── YES (SPD) → CG (Conjugate Gradient)
    │   │   │   ├── Elliptic PDE → AMG preconditioner
    │   │   │   ├── Banded structure → IC(0) or IC(k)
    │   │   │   └── General sparse → AMG or IC
    │   │   │
    │   │   └── NO/Unknown (symmetric indefinite)
    │   │       ├── Eigenvalues both signs → MINRES
    │   │       ├── Near-singular → SYMMLQ
    │   │       └── Preconditioner: ILU or block diagonal
    │   │
    │   └── NO (nonsymmetric) → Is it nearly symmetric?
    │       │
    │       ├── YES (A ≈ Aᵀ) → BiCGSTAB
    │       │   ├── Smooth convergence needed → BiCGSTAB(ℓ)
    │       │   └── Preconditioner: ILUT or AMG
    │       │
    │       └── NO (strongly nonsymmetric) → GMRES
    │           ├── Memory limited → GMRES(m) restarted
    │           ├── Very nonsymmetric → Full GMRES if affordable
    │           └── Preconditioner: ILU(k), ILUT, or AMG
```

## Direct Solver Selection

### When to Use Direct

| Condition | Direct Recommended |
|-----------|-------------------|
| n < 5000 | Usually |
| Dense matrix | Yes |
| Multiple RHS | Yes (factor once) |
| Need exact solution | Yes |
| High accuracy required | Yes |
| Robustness critical | Yes |

### Direct Solver Types

| Matrix Type | Method | Complexity |
|-------------|--------|------------|
| General dense | LU with pivoting | O(n³) |
| Symmetric dense | LDLᵀ | O(n³/2) |
| SPD dense | Cholesky | O(n³/3) |
| Sparse | Sparse LU (SuperLU, UMFPACK) | O(nnz^α), α ≈ 1.5 |
| Banded | Band LU/Cholesky | O(n × b²) |

### Memory Considerations

| Method | Memory | Fill-in |
|--------|--------|---------|
| Dense LU | O(n²) | N/A |
| Sparse LU | O(n) to O(n²) | Depends on ordering |
| Cholesky | Half of LU | Less fill-in |
| Iterative | O(n) × k | No fill-in |

## Iterative Solver Details

### Conjugate Gradient (CG)

**Requirements:** A must be SPD

**Convergence:**
```
||e_k||_A ≤ 2 × ((√κ - 1)/(√κ + 1))^k × ||e_0||_A
```

| Condition Number | Iterations (to 10⁻⁶) |
|------------------|----------------------|
| κ = 10 | ~6 |
| κ = 100 | ~20 |
| κ = 1000 | ~60 |
| κ = 10⁶ | ~2000 |

**Breakdown:** CG is breakdown-free for SPD matrices.

### GMRES

**Requirements:** None (works for any nonsingular matrix)

**Properties:**
- Minimizes residual over Krylov subspace
- Optimal for nonsymmetric systems
- Memory: O(m × n) for m iterations

**Restart Strategy:**
```
GMRES(m): Restart after m iterations
├── m = 20-30: Typical for memory-limited
├── m = 50-100: Better convergence
└── m = n: Full GMRES (no restart)
```

**When GMRES stalls:**
- Increase restart parameter m
- Improve preconditioner
- Check for near-singularity

### BiCGSTAB

**Requirements:** Works for nonsymmetric, best if A ≈ Aᵀ

**Properties:**
- Lower memory than GMRES
- May have irregular convergence
- Two matrix-vector products per iteration

**Variants:**
| Variant | Properties |
|---------|------------|
| BiCGSTAB | Standard |
| BiCGSTAB(ℓ) | Smoother convergence, higher cost |
| IDR(s) | Alternative, can be faster |

### MINRES

**Requirements:** A symmetric (not necessarily PD)

**Properties:**
- Works for indefinite symmetric systems
- Minimizes residual (like GMRES for symmetric)
- Short recurrences (memory efficient)

**Use for:**
- Saddle-point systems
- Systems from variational problems
- Eigenvalue problems (shift-invert)

## Special Matrix Structures

### Saddle-Point Systems

```
[A   B ] [x]   [f]
[Bᵀ  -C] [y] = [g]
```

**Solvers:**
- Schur complement method
- Uzawa iteration
- Block preconditioned GMRES/MINRES

**Preconditioners:**
```
Block diagonal: [A⁻¹   0  ]
                [0    S⁻¹]

Block triangular: [A⁻¹  0 ]
                  [Bᵀ   S⁻¹]
```

Where S = C + BᵀA⁻¹B is the Schur complement.

### Banded Systems

For bandwidth b << n:

| Method | Cost | When to Use |
|--------|------|-------------|
| Band LU | O(nb²) | Direct, small b |
| Thomas (tridiag) | O(n) | b = 1 |
| Cyclic reduction | O(n log n) | Parallel |

### Block Systems

When A has natural block structure:

```
Block Jacobi: Solve diagonal blocks independently
Block Gauss-Seidel: Sequential block updates
Block ILU: Factor with block structure preserved
```

## Solver Selection by Application

### Elliptic PDEs (Laplacian-like)

| Grid | Recommended |
|------|-------------|
| Structured | CG + FFT or Multigrid |
| Unstructured | CG + AMG |
| Anisotropic | CG + line/plane smoothing |

### Parabolic PDEs (Heat equation)

| Implicit Method | Recommended |
|-----------------|-------------|
| Backward Euler | CG + IC/AMG (SPD) |
| Crank-Nicolson | CG + IC/AMG (SPD) |
| BDF | Same as above |

### Hyperbolic PDEs (Advection-dominated)

| Character | Recommended |
|-----------|-------------|
| Pure advection | GMRES + ILU (nonsymmetric) |
| Advection-diffusion | GMRES/BiCGSTAB + ILUT |
| High Péclet | Upwind + GMRES + strong ILU |

### Navier-Stokes

| Formulation | Recommended |
|-------------|-------------|
| Coupled velocity-pressure | Block preconditioned GMRES |
| Projection methods | Poisson: CG + AMG |
| SIMPLE-like | Momentum: BiCGSTAB, Pressure: CG |

### Phase-Field

| Equation | Recommended |
|----------|-------------|
| Allen-Cahn | CG + AMG (if implicit) |
| Cahn-Hilliard | GMRES + Block preconditioner (4th order) |
| Mixed form | CG + AMG for each block |

## Failure Modes and Remedies

### Common Problems

| Symptom | Cause | Solution |
|---------|-------|----------|
| No convergence | Poor preconditioner | Stronger preconditioner |
| Slow convergence | High condition number | Scale matrix, better preconditioner |
| Breakdown | Singular or near-singular | Check matrix, use pseudo-inverse |
| Oscillation | Loss of orthogonality | Increase GMRES restart |
| NaN/Inf | Overflow | Scale matrix |

### When All Else Fails

1. **Check the matrix**: Is it actually nonsingular?
2. **Scale rows/columns**: Equilibrate to unit row norms
3. **Try direct solver**: Even for large sparse, may work
4. **Regularize**: Add small diagonal for near-singular
5. **Reformulate problem**: Different discretization

## Quick Reference Table

| Matrix Type | First Choice | Preconditioner | Backup |
|-------------|--------------|----------------|--------|
| SPD, elliptic | CG | AMG | CG + IC |
| SPD, general | CG | IC(k) | Cholesky |
| Sym. indef. | MINRES | Block diag | GMRES |
| Nonsym., mild | BiCGSTAB | ILUT | GMRES |
| Nonsym., strong | GMRES(50) | ILU(k) | GMRES(100) |
| Saddle-point | Block GMRES | Schur approx | Uzawa |
| Dense | LU/Cholesky | - | - |