nonlinear-solvers

Goal

Provide a universal workflow to select a nonlinear solver, configure globalization strategies, and diagnose convergence for root-finding, optimization, and least-squares problems.

Requirements

• Python 3.10+
• NumPy (for Jacobian diagnostics)
• SciPy (optional, for advanced analysis)

Inputs to Gather

Input	Description	Example
Problem type	Root-finding, optimization, least-squares	root-finding
Problem size	Number of unknowns	n = 10000
Jacobian availability	Analytic, finite-diff, unavailable	analytic
Jacobian cost	Cheap or expensive to compute	expensive
Constraints	None, bounds, equality, inequality	none
Smoothness	Is objective/residual smooth?	yes
Residual history	Sequence of residual norms	1,0.1,0.01,...

Decision Guidance

Solver Selection Flowchart

Is Jacobian available and cheap?
├── YES → Problem size?
│   ├── Small (n < 1000) → Newton (full)
│   └── Large (n ≥ 1000) → Newton-Krylov
└── NO → Is objective smooth?
    ├── YES → Memory limited?
    │   ├── YES → L-BFGS or Broyden
    │   └── NO → BFGS
    └── NO → Anderson acceleration or Picard

Quick Reference

Problem Type	First Choice	Alternative	Globalization
Small root-finding	Newton	Broyden	Line search
Large root-finding	Newton-Krylov	Anderson	Trust region
Optimization	L-BFGS	BFGS	Wolfe line search
Least-squares	Levenberg-Marquardt	Gauss-Newton	Trust region
Bound constrained	L-BFGS-B	Trust-region reflective	Projected

Script Outputs (JSON Fields)

Script	Key Outputs
scripts/solver_selector.py	recommended, alternatives, notes
scripts/convergence_analyzer.py	converged, convergence_type, estimated_rate, diagnosis
scripts/jacobian_diagnostics.py	condition_number, jacobian_quality, rank_deficient
scripts/globalization_advisor.py	strategy, line_search_type, trust_region_type, parameters
scripts/residual_monitor.py	patterns_detected, alerts, recommendations
scripts/step_quality.py	ratio, step_quality, accept_step, trust_radius_action

Workflow

1. Characterize problem - Identify type, size, Jacobian availability
2. Select solver - Run scripts/solver_selector.py
3. Choose globalization - Run scripts/globalization_advisor.py
4. Analyze Jacobian - If available, run scripts/jacobian_diagnostics.py
5. Monitor residuals - During solve, use scripts/residual_monitor.py
6. Analyze convergence - Run scripts/convergence_analyzer.py
7. Evaluate steps - For trust region, use scripts/step_quality.py

Conversational Workflow Example

User: My Newton solver for a phase-field simulation is converging very slowly. After 50 iterations, the residual only dropped from 1 to 0.1.

Agent workflow:

1. Analyze convergence:

   python3 scripts/convergence_analyzer.py --residuals 1,0.8,0.6,0.5,0.4,0.3,0.2,0.15,0.12,0.1 --json
   

2. Check globalization strategy:

   python3 scripts/globalization_advisor.py --problem-type root-finding --jacobian-quality ill-conditioned --previous-failures 0 --json
   

3. Recommend: Switch to trust region with Levenberg-Marquardt regularization, or use Newton-Krylov with better preconditioning.

Pre-Solve Checklist

• Confirm problem type (root-finding, optimization, least-squares)
• Assess Jacobian availability and cost
• Check initial guess quality
• Set appropriate tolerances
• Choose globalization strategy
• Prepare to monitor convergence

CLI Examples

# Select solver for large unconstrained optimization
python3 scripts/solver_selector.py --size 50000 --smooth --memory-limited --json

# Select solver for a small nonlinear least-squares (data-fitting) problem
python3 scripts/solver_selector.py --problem-type least-squares --size 6 --jacobian-available --smooth --json

# Analyze convergence from residual history
python3 scripts/convergence_analyzer.py --residuals 1,0.1,0.01,0.001,0.0001 --tolerance 1e-6 --json

# Diagnose Jacobian quality
python3 scripts/jacobian_diagnostics.py --matrix jacobian.txt --json

# Get globalization recommendation
python3 scripts/globalization_advisor.py --problem-type optimization --jacobian-quality good --json

# Globalization for a distant initial guess (favors trust region)
python3 scripts/globalization_advisor.py --problem-type root-finding --jacobian-quality good --far-from-solution --json

# Monitor residual patterns
python3 scripts/residual_monitor.py --residuals 1,0.8,0.9,0.7,0.75,0.6 --target-tolerance 1e-8 --json

# Evaluate step quality for trust region
python3 scripts/step_quality.py --predicted-reduction 0.5 --actual-reduction 0.4 --step-norm 0.8 --gradient-norm 1.0 --trust-radius 1.0 --json

Error Handling

Error	Cause	Resolution
problem_size must be positive	Invalid size	Check problem dimension
problem_size (...) exceeds maximum (...)	Size above 10 billion cap	Re-check the unit/value
constraint_type must be one of...	Unknown constraint	Use: none, bound, equality, inequality
problem_type must be one of...	Unknown problem type	Use: root-finding, optimization, least-squares
residuals must be non-negative	Invalid residual data	Check residual computation
residuals must be finite	NaN/Inf in residual data	Sanitize residual history
residual list length (...) exceeds limit (...)	More than 100,000 entries	Downsample the history
Matrix file not found	Invalid path	Verify Jacobian file exists
Matrix file exceeds size limit ...	Matrix file too large	Use a smaller / sparser matrix

Interpretation Guidance

Convergence Type

Type	Meaning	Action
quadratic	Optimal Newton (order p ≈ 2)	Continue, near solution
superlinear	Ratios shrinking toward 0 (1 < p < 2); quasi-Newton working	Monitor for stagnation
linear	Constant contraction ratio (p ≈ 1); a small constant ratio is fast-linear, not superlinear	May improve with preconditioner
sublinear	Too slow (ratio → 1)	Change method or formulation
stagnated	No progress	Check Jacobian, preconditioner
diverged	Increasing residual	Add globalization, check Jacobian

Jacobian Quality

Quality	Condition Number	Action
good	< 10⁶	Standard Newton works
moderately-conditioned	10⁶ - 10¹⁰	Consider scaling
ill-conditioned	> 10¹⁰	Use regularization
near-singular	∞	Reformulate or use LM

Step Quality (Trust Region)

Ratio ρ	Quality	Trust Radius
ρ < 0	very_poor	Shrink aggressively
ρ < 0.25	marginal	Shrink
0.25 ≤ ρ < 0.75	good	Maintain
ρ ≥ 0.75	excellent	Expand if at boundary

Verification checklist

Do not trust a "solved" claim until these concrete artifacts are recorded:

• Logged the full residual norm history and ran convergence_analyzer.py --residuals <history>; recorded convergence_type and estimated_rate, and confirmed converged: true against the actual solver tolerance (not the default 1e-10).
• Confirmed the residual sequence is monotone-decreasing or fed it to residual_monitor.py; recorded patterns_detected and verified it does NOT include diverging, oscillating, plateau, or slow_convergence while still above tolerance.
• If a Jacobian is available, ran jacobian_diagnostics.py --matrix J.txt and recorded condition_number and jacobian_quality; for an analytic Jacobian, passed --finite-diff-matrix and confirmed finite_diff_error is below ~1e-2 (no "Large discrepancy" note).
• Checked rank_deficient from jacobian_diagnostics.py is false (or documented why a rank-deficient/near-singular Jacobian is expected and that Levenberg-Marquardt regularization is in use).
• For a trust-region solve, evaluated accepted steps with step_quality.py and recorded the reduction ratio; confirmed accepted steps have ratio >= 0.25 (not very_poor/poor) and that the trust_radius_action matches the recorded ρ.
• Recorded the solver and globalization actually used and confirmed they match solver_selector.py and globalization_advisor.py recommendations for the stated problem type, size, and Jacobian quality (e.g., large/expensive-Jacobian → Newton-Krylov; least-squares → Levenberg-Marquardt trust region).
• Re-confirmed convergence after any change to tolerance, initial guess, or preconditioner — the convergence type can flip (e.g., quadratic → linear/stagnated) and must be re-classified, not assumed.

Common pitfalls & rationalizations

Tempting shortcut	Why it's wrong / what to do
"The residual ratio is a small constant (~0.1), so it's converging superlinearly."	A constant contraction ratio is linear, not superlinear — convergence_analyzer.py reports this as linear (annotated "fast linear"). Superlinear requires the ratio to tend to zero (order p > 1.2). Don't claim Newton-quality convergence from a flat ratio.
"It stopped without erroring, so the solver converged."	Run completion is not convergence. Check converged from convergence_analyzer.py/residual_monitor.py against the real tolerance; a stagnated or plateau result also "stops" but has not solved f(x)=0.
"Two iterations look like they're shrinking, so the rate is fine."	Order estimation needs at least 3 strictly decreasing positive residuals; with fewer, convergence_analyzer.py returns unknown/falls back to rate-only. Gather more iterations before quoting a convergence type.
"I coded the analytic Jacobian, so it must be right."	A wrong Jacobian still produces some step. Run jacobian_diagnostics.py --finite-diff-matrix and confirm finite_diff_error is small; a "Large discrepancy with finite-diff" note means the analytic Jacobian is buggy, which silently degrades Newton to linear convergence.
"Newton diverged, so I'll just shrink the global tolerance and call it close enough."	Divergence (convergence_type: diverged, or diverging pattern) signals a bad step direction or far-from-solution start — add globalization. Run globalization_advisor.py (use --far-from-solution / report failures) and switch to a trust region or damped step instead of loosening the target.
"Trust-region step decreased the objective, so accept and expand the radius."	Acceptance and radius growth depend on the reduction ratio ρ, not just sign. step_quality.py only flags expand when ρ ≥ 0.75 and the step hit the boundary; a small positive ρ (marginal) means accept-but-shrink. Use the recorded trust_radius_action.
"The Jacobian is large and expensive, but full Newton is the gold standard, so I'll form it anyway."	For n ≥ 1000 or expensive Jacobians, solver_selector.py routes to matrix-free Newton-Krylov (JFNK) precisely because forming/factoring J is infeasible; use Jacobian-vector products plus a preconditioner instead.

Security

Input Validation

• --size (problem size) is validated as a positive integer, bounded at 10 billion
• --residuals are validated as finite non-negative numbers, capped at 100,000 entries
• --tolerance and --target-tolerance are validated as finite positive numbers
• --problem-type and --constraint-type are validated against fixed allowlists
• --jacobian-quality is validated against a fixed allowlist (good, ill-conditioned, etc.)
• Step quality parameters (predicted-reduction, actual-reduction, step-norm, gradient-norm, trust-radius) are validated as finite numbers

File Access

• jacobian_diagnostics.py reads a single matrix file specified by --matrix; no directory traversal beyond the given path
• Matrix files are size-limited and loaded with allow_pickle=False to prevent code execution
• All other scripts read no external files; inputs are provided via CLI arguments
• Scripts write only to stdout (JSON output)

Tool Restrictions

• Read: Used to inspect script source, references, and user configuration files
• Bash: Used to execute the six Python analysis scripts (solver_selector.py, convergence_analyzer.py, jacobian_diagnostics.py, globalization_advisor.py, residual_monitor.py, step_quality.py) with explicit argument lists
• Write: Used to save analysis results or solver recommendations; writes are scoped to the user's working directory
• Grep/Glob: Used to locate relevant files and search references

Safety Measures

• No eval(), exec(), or dynamic code generation
• All subprocess calls use explicit argument lists (no shell=True)
• Matrix dimension limits prevent memory exhaustion when loading Jacobian files
• Residual history analysis operates on bounded-length numeric arrays only

Limitations

• No global convergence guarantee: All methods may fail for pathological problems
• Jacobian accuracy: Finite-difference Jacobian may be inaccurate near discontinuities
• Large dense problems: May require specialized solvers not covered here
• Constrained optimization: Complex constraints need SQP or interior point methods

References

• references/solver_decision_tree.md - Problem-based solver selection
• references/method_catalog.md - Method details and parameters
• references/convergence_diagnostics.md - Diagnosing convergence issues
• references/globalization_strategies.md - Line search and trust region

Version History

• v1.2.2 (2026-06-24): Added a "Verification checklist" (evidence tied to each script's JSON outputs — convergence type/rate, residual patterns, Jacobian condition/finite-diff error, rank, trust-region step ratio, and solver/globalization agreement) and a "Common pitfalls & rationalizations" table covering constant-ratio-vs-superlinear, run-completion-vs-convergence, too-few-iterations, unverified analytic Jacobians, divergence handling, trust-region acceptance, and large/expensive-Jacobian routing
• v1.2.0 (2026-06-23): Added --problem-type to solver_selector.py with a nonlinear least-squares path (Levenberg-Marquardt / Gauss-Newton); reordered solver selection so problem size dominates high-accuracy and routes large/expensive-Jacobian problems to Newton-Krylov; added --far-from-solution to globalization_advisor.py and surfaced Levenberg-Marquardt as the trust-region type for least-squares; corrected convergence classification so constant-ratio sequences are linear (not superlinear); RFC-8259-safe JSON (no -Infinity); input-validation hardening
• v1.1.0 (2026-03-26): Optimized agent-discovery description, evaluation suite, security review docs, standardized metadata block, CHANGELOG
• v1.0.0: Initial release with 6 analysis scripts