Added the roadmap
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Connor Johnstone
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roadmap/FEATURE_TEMPLATES.md
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roadmap/FEATURE_TEMPLATES.md
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# Feature File Templates
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This document contains brief summaries for features 6-38. Detailed feature files should be created when you're ready to implement each one, using the detailed examples in features 01-05 and 12 as templates.
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## How to Use This Document
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When ready to implement a feature:
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1. Copy the template structure from features/01-bs3-method.md or similar
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2. Fill in the details from the summary below
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3. Add implementation-specific details
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4. Create comprehensive testing requirements
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---
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## Feature 06: CallbackSet
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**Description**: Compose multiple callbacks with ordering
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**Dependencies**: Discrete callbacks
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**Effort**: Small
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**Key Points**: Builder pattern, execution priority, enable/disable
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## Feature 07: Saveat Functionality
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**Description**: Save solution at specific timepoints
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**Dependencies**: None
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**Effort**: Medium
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**Key Points**: Interpolation to exact times, dense vs sparse saving, memory efficiency
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## Feature 08: Solution Derivatives
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**Description**: Access derivatives at any time via interpolation
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**Dependencies**: None
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**Effort**: Small
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**Key Points**: `solution.derivative(t)` interface, use dense output or finite differences
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## Feature 09: DP8 Method
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**Description**: Dormand-Prince 8th order method
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**Dependencies**: None
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**Effort**: Medium
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**Key Points**: 13 stages, very high accuracy, tableau from literature
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## Feature 10: FBDF Method
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**Description**: Fixed-leading-coefficient BDF multistep method
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**Dependencies**: Linear solver, Nordsieck representation
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**Effort**: Large
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**Key Points**: Variable order (1-5), excellent for very stiff problems, complex state management
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## Feature 11: Rodas4/Rodas5P
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**Description**: Higher-order Rosenbrock methods
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**Dependencies**: Rosenbrock23
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**Effort**: Medium
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**Key Points**: 4th/5th order accuracy, more stages, better for higher accuracy stiff problems
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## Feature 13: Default Algorithm Selection
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**Description**: Smart defaults based on problem characteristics
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**Dependencies**: Auto-switching, multiple algorithms
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**Effort**: Medium
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**Key Points**: Analyze tolerance, problem size, choose algorithms automatically
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## Feature 14: Automatic Initial Stepsize
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**Description**: Algorithm to compute good initial dt
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**Dependencies**: None
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**Effort**: Medium
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**Key Points**: Based on Hairer & Wanner algorithm, uses local Lipschitz estimate
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## Feature 15: PresetTimeCallback
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**Description**: Callbacks at predetermined times
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**Dependencies**: Discrete callbacks
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**Effort**: Small
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**Key Points**: Efficient time-based events, integration with tstops
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## Feature 16: TerminateSteadyState
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**Description**: Auto-detect when solution reaches steady state
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**Dependencies**: Discrete callbacks
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**Effort**: Small
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**Key Points**: Monitor du/dt, terminate when small enough
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## Feature 17: SavingCallback
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**Description**: Custom saving logic beyond default
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**Dependencies**: CallbackSet
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**Effort**: Small
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**Key Points**: User-defined save conditions, memory-efficient for large problems
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## Feature 18: Linear Solver Infrastructure
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**Description**: Generic linear solver interface and dense LU
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**Dependencies**: None
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**Effort**: Large
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**Key Points**:
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- Trait-based design for flexibility
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- Dense LU factorization with partial pivoting
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- Solve Ax = b efficiently
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- Foundation for all implicit methods
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- Consider using nalgebra's built-in LU or implement custom
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## Feature 19: Jacobian Computation
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**Description**: Finite difference and auto-diff Jacobians
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**Dependencies**: None
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**Effort**: Large
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**Key Points**:
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- Forward finite differences: (f(y+εe_j) - f(y))/ε
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- Epsilon selection: √eps * max(|y_j|, 1)
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- Sparse Jacobian support (future)
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- Integration with AD crates (future)
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## Feature 20: Low-Storage Runge-Kutta
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**Description**: 2N/3N/4N storage variants for large systems
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**Dependencies**: None
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**Effort**: Medium
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**Key Points**: Specialized RK methods that reuse storage, critical for PDEs via method-of-lines
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## Feature 21: SSP Methods
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**Description**: Strong Stability Preserving RK methods
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**Dependencies**: None
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**Effort**: Medium
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**Key Points**: SSPRK22, SSPRK33, SSPRK43, SSPRK53, preserve TVD/monotonicity, for hyperbolic PDEs
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## Feature 22: Symplectic Integrators
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**Description**: Verlet, Leapfrog, KahanLi for Hamiltonian systems
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**Dependencies**: None (second-order ODE support already exists)
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**Effort**: Medium
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**Key Points**: Preserve energy/symplectic structure, special for p,q formulation
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## Feature 23: Verner Methods Suite
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**Description**: Complete Verner family (Vern6, Vern8, Vern9)
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**Dependencies**: Vern7
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**Effort**: Medium
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**Key Points**: Different orders for different accuracy needs, all highly efficient
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## Feature 24: SDIRK Methods
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**Description**: Singly Diagonally Implicit RK (KenCarp3/4/5)
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**Dependencies**: Linear solver, nonlinear solver
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**Effort**: Large
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**Key Points**: IMEX methods, good for semi-stiff problems, L-stable
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## Feature 25: Exponential Integrators
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**Description**: Exp4, EPIRK4, EXPRB53 for semi-linear stiff
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**Dependencies**: Matrix exponential computation
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**Effort**: Large
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**Key Points**: For du/dt = Lu + N(u), where L is linear stiff part
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## Feature 26: Extrapolation Methods
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**Description**: Richardson extrapolation with adaptive order
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**Dependencies**: Linear solver
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**Effort**: Large
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**Key Points**: High accuracy from low-order methods, variable order selection
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## Feature 27: Stabilized Methods
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**Description**: ROCK2, ROCK4, RKC for mildly stiff
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**Dependencies**: None
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**Effort**: Medium
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**Key Points**: Extended stability regions, good for PDEs with explicit time-stepping
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## Feature 28: I Controller
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**Description**: Basic integral controller
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**Dependencies**: None
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**Effort**: Small
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**Key Points**: Simplest adaptive controller, mainly for comparison/testing
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## Feature 29: Predictive Controller
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**Description**: Advanced predictive step size control
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**Dependencies**: None
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**Effort**: Medium
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**Key Points**: Predicts future error, more sophisticated than PID
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## Feature 30: VectorContinuousCallback
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**Description**: Multiple simultaneous event detection
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**Dependencies**: CallbackSet
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**Effort**: Medium
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**Key Points**: More efficient than separate callbacks, shared root-finding
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## Feature 31: PositiveDomain
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**Description**: Enforce positivity constraints
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**Dependencies**: CallbackSet
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**Effort**: Small
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**Key Points**: Ensures solution stays positive, important for physical systems
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## Feature 32: ManifoldProjection
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**Description**: Project solution onto constraint manifolds
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**Dependencies**: CallbackSet
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**Effort**: Medium
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**Key Points**: For constrained mechanical systems, projection step after integration
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## Feature 33: Nonlinear Solver Infrastructure
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**Description**: Newton and quasi-Newton methods
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**Dependencies**: Linear solver
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**Effort**: Large
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**Key Points**:
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- Newton's method for implicit stages
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- Line search
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- Convergence criteria
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- Foundation for SDIRK, FIRK methods
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## Feature 34: Krylov Linear Solvers
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**Description**: GMRES, BiCGStab for large sparse systems
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**Dependencies**: Linear solver infrastructure
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**Effort**: Large
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**Key Points**: Iterative solvers for when LU factorization too expensive
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## Feature 35: Preconditioners
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**Description**: ILU, Jacobi, custom preconditioners
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**Dependencies**: Krylov solvers
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**Effort**: Large
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**Key Points**: Accelerate Krylov methods, essential for large sparse systems
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## Feature 36: FSAL Optimization
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**Description**: First-Same-As-Last function evaluation reuse
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**Dependencies**: None
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**Effort**: Small
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**Key Points**: Reduce function evaluations by ~14% for FSAL methods (DP5, Tsit5, etc.)
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## Feature 37: Custom Norms
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**Description**: User-definable error norms
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**Dependencies**: None
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**Effort**: Small
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**Key Points**: L2, Linf, weighted norms, custom user functions
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## Feature 38: Step/Stage Limiting
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**Description**: Limit state values during integration
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**Dependencies**: None
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**Effort**: Small
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**Key Points**: Enforce bounds on solution, prevent non-physical values
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---
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## Creating Detailed Feature Files
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When you're ready to work on a feature, create a detailed file following this structure:
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1. **Overview**: What is it, key characteristics
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2. **Why This Feature Matters**: Motivation, use cases
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3. **Dependencies**: What must be built first
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4. **Implementation Approach**: Algorithm details, design decisions
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5. **Implementation Tasks**: Detailed checklist with subtasks
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6. **Testing Requirements**: Specific tests with expected results
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7. **References**: Papers, Julia code, textbooks
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8. **Complexity Estimate**: Effort and risk assessment
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9. **Success Criteria**: How to know it's done right
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10. **Future Enhancements**: What could be added later
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See `features/01-bs3-method.md`, `features/02-vern7-method.md`, `features/03-rosenbrock23.md`, etc. for complete examples.
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