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Connor Johnstone
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roadmap/features/03-rosenbrock23.md
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roadmap/features/03-rosenbrock23.md
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# Feature: Rosenbrock23 Method
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## Overview
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Rosenbrock23 is a 2nd/3rd order L-stable Rosenbrock-W method designed for stiff ODEs. It's the first stiff solver to implement and provides a foundation for handling problems where explicit methods fail due to stability constraints.
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**Key Characteristics:**
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- Order: 2(3) - actually 3rd order solution with 2nd order embedded for error
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- Stages: 3
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- L-stable: Excellent damping of high-frequency oscillations
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- Stiff-aware: Can handle stiffness ratios up to ~10^6
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- W-method: Uses approximate Jacobian (doesn't need exact)
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- Stiff-aware interpolation: 2nd order dense output
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## Why This Feature Matters
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- **Stiff problems**: Many real-world ODEs are stiff (chemistry, circuit simulation, etc.)
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- **Completes basic toolkit**: With DP5/Tsit5 for non-stiff + Rosenbrock23 for stiff, can handle most problems
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- **Foundation for auto-switching**: Enables automatic stiffness detection and algorithm selection
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- **Widely used**: MATLAB's ode23s is based on this method
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## Dependencies
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### Required Infrastructure
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- **Linear solver** (see feature #18)
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- LU factorization for dense systems
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- Generic `LinearSolver` trait
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- **Jacobian computation** (see feature #19)
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- Finite difference approximation
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- User-provided analytical Jacobian (optional)
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- Auto-diff integration (future)
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### Recommended to Implement First
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1. Linear solver infrastructure
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2. Jacobian computation
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3. Then Rosenbrock23
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## Implementation Approach
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### Mathematical Background
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Rosenbrock methods solve:
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```
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(I - γh*J) * k_i = h*f(y_n + Σa_ij*k_j) + h*J*Σc_ij*k_j
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```
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Where:
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- J is the Jacobian ∂f/∂y
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- γ is a method-specific constant
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- Stages k_i are computed by solving linear systems
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For Rosenbrock23:
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- γ = 1/(2 + √2) ≈ 0.2928932188
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- 3 stages requiring 3 linear solves per step
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- W-method: J can be approximate or outdated
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### Algorithm Structure
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```rust
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struct Rosenbrock23<D> {
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a_tol: SVector<f64, D>,
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r_tol: f64,
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// Tableau coefficients (as constants)
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// Linear solver (to be injected or created)
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}
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```
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### Step Procedure
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1. Compute or reuse Jacobian J = ∂f/∂y(t_n, y_n)
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2. Form W = I - γh*J
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3. Factor W (LU decomposition)
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4. For each stage i=1,2,3:
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- Compute RHS based on previous stages
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- Solve W*k_i = RHS
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5. Compute solution: y_{n+1} = y_n + b1*k1 + b2*k2 + b3*k3
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6. Compute error: err = e1*k1 + e2*k2 + e3*k3
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7. Store dense output coefficients
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### Tableau Coefficients
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From Shampine & Reichelt (1997) - MATLAB's ode23s:
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```
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γ = 1/(2 + √2)
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Stage matrix for ki calculations:
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a21 = 1.0
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a31 = 1.0
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a32 = 0.0
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c21 = -1.0707963267948966
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c31 = -0.31381995116890154
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c32 = 1.3846153846153846
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Solution weights:
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b1 = 0.5
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b2 = 0.5
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b3 = 0.0
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Error estimate:
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d1 = -0.17094382871185335
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d2 = 0.17094382871185335
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d3 = 0.0
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```
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### Jacobian Management
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Key decisions:
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- **When to update J**: Error signal, step rejection, every N steps
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- **Finite difference formula**: Forward or central differences
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- **Sparsity**: Dense for now, sparse in future
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- **Storage**: Cache J and LU factorization
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### Linear Solver Integration
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```rust
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trait LinearSolver<D> {
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fn factor(&mut self, matrix: &SMatrix<f64, D, D>) -> Result<(), Error>;
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fn solve(&self, rhs: &SVector<f64, D>) -> Result<SVector<f64, D>, Error>;
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}
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struct DenseLU<D> {
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lu: SMatrix<f64, D, D>,
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pivots: [usize; D],
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}
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```
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## Implementation Tasks
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### Infrastructure (Prerequisites)
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- [ ] **Linear solver trait and implementation**
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- [ ] Define `LinearSolver` trait
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- [ ] Implement dense LU factorization
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- [ ] Add solve method
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- [ ] Tests for random matrices
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- [ ] **Jacobian computation**
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- [ ] Forward finite differences: J[i,j] ≈ (f(y + ε*e_j) - f(y)) / ε
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- [ ] Epsilon selection (√machine_epsilon * max(|y[j]|, 1))
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- [ ] Cache for function evaluations
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- [ ] Test on known Jacobians
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### Core Algorithm
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- [ ] Define `Rosenbrock23` struct
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- [ ] Tableau constants
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- [ ] Tolerance fields
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- [ ] Jacobian update strategy fields
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- [ ] Linear solver instance
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- [ ] Implement `step()` method
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- [ ] Decide if Jacobian update needed
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- [ ] Compute J if needed
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- [ ] Form W = I - γh*J
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- [ ] Factor W
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- [ ] Stage 1: Solve for k1
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- [ ] Stage 2: Solve for k2
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- [ ] Stage 3: Solve for k3
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- [ ] Combine for solution
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- [ ] Compute error estimate
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- [ ] Return (y_next, error, dense_coeffs)
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- [ ] Implement `interpolate()` method
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- [ ] 2nd order stiff-aware interpolation
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- [ ] Uses k1, k2, k3
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- [ ] Jacobian update strategy
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- [ ] Update on first step
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- [ ] Update on step rejection
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- [ ] Update if error test suggests (heuristic)
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- [ ] Reuse otherwise
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- [ ] Implement constants
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- [ ] `ORDER = 3`
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- [ ] `STAGES = 3`
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- [ ] `ADAPTIVE = true`
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- [ ] `DENSE = true`
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### Integration
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- [ ] Add to prelude
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- [ ] Module exports
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- [ ] Builder pattern for configuration
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### Testing
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- [ ] **Stiff test: Van der Pol oscillator**
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- [ ] μ = 1000 (very stiff)
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- [ ] Explicit methods would need 100000+ steps
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- [ ] Rosenbrock23 should handle in <1000 steps
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- [ ] Verify solution accuracy
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- [ ] **Stiff test: Robertson problem**
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- [ ] Classic stiff chemistry problem
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- [ ] 3 equations, stiffness ratio ~10^11
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- [ ] Verify conservation properties
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- [ ] Compare to reference solution
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- [ ] **L-stability test**
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- [ ] Verify method damps oscillations
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- [ ] Test problem with large negative eigenvalues
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- [ ] Should remain stable with large time steps
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- [ ] **Convergence test**
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- [ ] Verify 3rd order convergence on smooth problem
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- [ ] Use linear test problem
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- [ ] Check error scales as h^3
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- [ ] **Jacobian update strategy test**
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- [ ] Count Jacobian evaluations
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- [ ] Verify not recomputing unnecessarily
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- [ ] Verify updates when needed
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- [ ] **Comparison test**
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- [ ] Same stiff problem with explicit method (DP5)
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- [ ] DP5 should require far more steps or fail
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- [ ] Rosenbrock23 should be efficient
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### Benchmarking
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- [ ] Van der Pol benchmark (μ = 1000)
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- [ ] Robertson problem benchmark
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- [ ] Compare to Julia implementation performance
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### Documentation
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- [ ] Docstring explaining Rosenbrock methods
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- [ ] When to use vs explicit methods
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- [ ] Stiffness indicators
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- [ ] Example with stiff problem
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- [ ] Notes on Jacobian strategy
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## Testing Requirements
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### Van der Pol Oscillator
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```rust
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// y1' = y2
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// y2' = μ(1 - y1²)y2 - y1
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// Initial: y1(0) = 2, y2(0) = 0
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// μ = 1000 (very stiff)
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```
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Integrate from t=0 to t=2000.
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Expected behavior:
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- Explicit method: >100,000 steps or fails
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- Rosenbrock23: ~500-2000 steps depending on tolerance
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- Should track limit cycle accurately
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### Robertson Problem
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```rust
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// y1' = -0.04*y1 + 1e4*y2*y3
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// y2' = 0.04*y1 - 1e4*y2*y3 - 3e7*y2²
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// y3' = 3e7*y2²
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// Conservation: y1 + y2 + y3 = 1
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```
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Integrate from t=0 to t=1e11 (log scale output)
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Verify:
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- Conservation law maintained
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- Correct steady-state behavior
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- Handles extreme stiffness ratio
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## References
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1. **Original Method**:
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- Shampine, L.F. and Reichelt, M.W. (1997)
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- "The MATLAB ODE Suite", SIAM J. Sci. Computing, 18(1), 1-22
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- DOI: 10.1137/S1064827594276424
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2. **Rosenbrock Methods Theory**:
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- Hairer, E. and Wanner, G. (1996)
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- "Solving Ordinary Differential Equations II: Stiff and DAE Problems"
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- Chapter IV.7
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3. **Julia Implementation**:
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- `OrdinaryDiffEq.jl/lib/OrdinaryDiffEqRosenbrock/`
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- Files: `rosenbrock_perform_step.jl`, `rosenbrock_caches.jl`
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4. **W-methods**:
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- Steihaug, T. and Wolfbrandt, A. (1979)
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- "An attempt to avoid exact Jacobian and nonlinear equations in the numerical solution of stiff differential equations"
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- Math. Comp., 33, 521-534
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## Complexity Estimate
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**Effort**: Large (20-30 hours)
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- Linear solver: 8-10 hours
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- Jacobian computation: 4-6 hours
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- Rosenbrock23 core: 6-8 hours
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- Testing and debugging: 6-8 hours
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**Risk**: High
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- First implicit method - new complexity
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- Linear algebra integration
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- Numerical stability issues possible
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- Jacobian update strategy tuning needed
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## Success Criteria
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- [ ] Solves Van der Pol (μ=1000) efficiently
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- [ ] Solves Robertson problem accurately
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- [ ] Demonstrates L-stability
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- [ ] Convergence test shows 3rd order
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- [ ] Outperforms explicit methods on stiff problems by 10-100x in steps
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- [ ] Jacobian reuse strategy effective (not recomputing every step)
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- [ ] Documentation complete with stiff problem examples
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- [ ] Performance within 2x of Julia implementation
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## Future Enhancements
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- [ ] User-provided analytical Jacobians
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- [ ] Sparse Jacobian support
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- [ ] More sophisticated update strategies
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- [ ] Rodas4, Rodas5P (higher-order Rosenbrock methods)
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- [ ] Krylov linear solvers for large systems
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