Select and configure nonlinear solvers for root-finding f(x)=0, optimization min F(x), and least-squares problems — choose among Newton, Newton-Krylov, quasi-Newton (BFGS, L-BFGS), Broyden, Anderson acceleration, and Levenberg-Marquardt methods, configure line search or trust-region globalization, diagnose convergence rate (quadratic, linear, stagnated), and assess Jacobian quality and conditioning. Use when a Newton solver converges slowly or diverges, choosing between line search and trust region, debugging nonlinear iteration failures in FEM or phase-field codes, or selecting a solver for large-scale unconstrained optimization, even if the user only says "my Newton iterations aren't converging."
Installation
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Select and configure nonlinear solvers for root-finding f(x)=0, optimization min F(x), and least-squares problems — choose among Newton, Newton-Krylov, quasi-Newton (BFGS, L-BFGS), Broyden, Anderson acceleration, and Levenberg-Marquardt methods, configure line search or trust-region globalization, diagnose convergence rate (quadratic, linear, stagnated), and assess Jacobian quality and conditioning. Use when a Newton solver converges slowly or diverges, choosing between line search and trust region, debugging nonlinear iteration failures in FEM or phase-field codes, or selecting a solver for large-scale unconstrained optimization, even if the user only says "my Newton iterations aren't converging."
allowed-tools
Read, Bash, Write, Grep, Glob
metadata
{"author":"HeshamFS","version":"1.2.2","security_tier":"high","security_reviewed":true,"tested_with":["claude-code"],"last_evaluated":"2026-06-24","eval_cases":5,"last_reviewed":"2026-06-23","standards":["Nocedal & Wright (2006), Numerical Optimization (BFGS/L-BFGS, trust region, dogleg, Steihaug-CG, Wolfe conditions)","Dennis & Schnabel (1996), Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Newton, Broyden good/bad, line-search globalization)","Kelley (1995), Iterative Methods for Linear and Nonlinear Equations (inexact Newton, Newton-Krylov); Eisenstat & Walker (1996) forcing sequence","Walker & Ni (2011), Anderson acceleration for fixed-point iterations (Anderson 1965)","Levenberg (1944) / Marquardt (1963), Levenberg-Marquardt method for nonlinear least-squares"]}
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
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/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
