SolverPilot

Current version: 0.4. Repairs near-indefinite QP certificates and adds explicit global QP/MIQP/MINLP, generalized power cones, CPU PDLP, bounded LP witness recovery and reuse observations. Historical 0.1.0rc2 correctness/trust hardening remains in place. See the 0.4 guide for examples and precise guarantees.

SolverPilot is a trust-aware optimization runtime and modeling layer for Python. It combines a small matrix-first solve API for LP/MILP/convex QP with a higher-level semantic modeling system that can compile into conic, smooth nonlinear, certified convex binary MINLP, and constraint-programming execution paths.

The project is deliberately conservative about claims: candidate solutions are independently validated where possible, backend capabilities are version/evidence aware, and local or backend-reported success is not silently promoted to a stronger proof category.

What SolverPilot provides

SolverPilot has two complementary layers.

Core runtime

• canonical LinearProblem / QuadraticProblem representations;
• LP, MILP, and continuous convex-QP execution;
• explicit or capability-aware backend routing;
• independent candidate validation and objective recomputation;
• infeasibility diagnostics;
• portfolio/fallback execution;
• repeated-solve Session support;
• auditable production-routing decisions with learned LP performance routing disabled.

Extended modeling platform — Track P P0–P9

• semantic Model, Variable, Parameter, and expression DAG;
• parameter-aware compile cache and partial recompilation;
• backend Capability Protocol 2.0 with version-bound evidence;
• exact/restricted bridge and reformulation infrastructure with transformation tapes;
• PersistentSession with fail-closed native-patch vs rebuild decisions;
• SOC/RSOC/PSD conic representation and validation;
• smooth continuous NLP + automatic differentiation through the pinned CasADi verification path;
• proof-aware certified convex binary MINLP orchestration;
• integer/Boolean/interval constraint programming with an exhaustive reference backend and optional OR-Tools CP-SAT.

The extended APIs are canonically imported from submodules such as solverpilot.model, solverpilot.conic, solverpilot.nlp, solverpilot.minlp, solverpilot.globalopt, and solverpilot.cp. The M30-frozen top-level solverpilot.__all__ surface remains unchanged in this release.

Verified trust, evaluation, extension, and application layers

The current release keeps the S2–S10 additive layers and adds correctness/trust hardening discovered during adversarial review of 0.1.0rc1. The historical 0.0.40rc2 artifacts remain separate provenance and are not overwritten.

• solverpilot.io: strict local JSON/CSV ingestion, bounded reads, explicit mapping, content hashes, and source provenance;
• solverpilot.evaluation: direct-run oracle construction and decomposed quality/runtime/failure regret;
• solverpilot.intelligence: versioned pre-solve feature schema, leakage audit, and simple train-only OOD/shift diagnostics;
• solverpilot.reporting: claim-safe structured explanations for core SolveResult;
• solverpilot.extensions: explicit trusted-code extension staging without creating a second backend/bridge registry;
• solverpilot.applications.tsp: exact small-instance references, conservative heuristics, independent validation, MILP and restricted CP compilation;
• solverpilot.applications.routing: CVRP/VRPTW model, validation, timelines, diagnostics, exact small-instance reference, MILP, and conservative heuristics;
• solverpilot.history: explicit opt-in local SQLite history that excludes raw solution vectors and raw problem arrays by design.

These APIs remain submodule-scoped and do not change the frozen 78-symbol top-level API.

Supported problem families and guarantee boundary

Important current limitations

• The learned production solver selector remains disabled; OOD diagnostics are not calibrated model-confidence probabilities.
• The S4 feature schema currently targets canonical LP/MILP/convex-QP fingerprints; conic/NLP/MINLP/CP/TSP/VRP do not yet share that learned-selector feature contract.
• solverpilot.reporting.explain_result and the automatic history adapters are centered on core SolveResult / LinearProblem / QuadraticProblem; application-specific TSP/VRP results have their own validators rather than a unified report/history adapter today.
• Extension activation is transactional under the manager's documented ownership assumptions, but caller-owned BackendRegistry / BridgeRegistry objects must not be mutated concurrently outside the manager during activation.
• Local-file symlink/path checks are fail-closed for static paths, but they are not a sandbox against a hostile process racing filesystem entries between validation and open.
• HistoryStore is a local plaintext SQLite store. It is opt-in and minimizes persisted solver/problem payloads, but it is not encrypted and caller-supplied metadata can still contain sensitive information.
• TSP/VRP exact reference algorithms are exponential and intentionally state-budgeted; the routing layer does not yet claim pickup-and-delivery, split delivery, stochastic travel time, live traffic, or real-time redispatch support.
• Exact SCIP and cuOpt GPU execution adapters are not shipped; readiness probes cannot qualify execution. See the 0.4 guide.

Requirements

Base runtime:

• Python 3.12, 3.13, or 3.14
• NumPy >=2.2,<3
• SciPy >=1.15,<2

Optional integrations are installed separately. OR-Tools 9.15 provides Python 3.12–3.14 wheels upstream; SolverPilot still requires its own cross-platform qualification before public support is promoted.

Installation

From PyPI

Install the latest published release from PyPI:

python -m pip install solverpilot

From the repository before PyPI publication

git clone https://github.com/Amirhosseinheidari/SolverPilot.git
cd solverpilot
python -m pip install .

For development:

python -m pip install ".[test]"
python -m pytest

Quick Start — LP

import numpy as np
from solverpilot import LinearProblem, solve

problem = LinearProblem.from_data(
    A=[[1.0, 1.0]],
    c=[1.0, 2.0],
    variable_lower=[0.0, 0.0],
    variable_upper=[1.0, 1.0],
    constraint_lower=[1.0],
    constraint_upper=[np.inf],
)

result = solve(problem)

print(result.status.value)       # valid_optimal
print(result.objective)          # 1.0
print(result.x)                  # approximately [1.0, 0.0]
print(result.validation.valid)   # True

Full example: examples/01_lp_basic.py

Core examples

Linear Programming (LP)

Use LinearProblem for continuous linear models. Lower/upper row arrays represent constraint_lower <= A @ x <= constraint_upper.

Full example: examples/01_lp_basic.py

Mixed-Integer Linear Programming (MILP)

Integer structure is declared through VariableDomain.

Full example: examples/02_milp_binary.py

Continuous convex QP

QuadraticProblem uses:

0.5 * x.T @ P @ x + q.T @ x + objective_offset

Full example: examples/03_qp_convex.py

Semantic modeling

The extended modeling layer builds a symbolic model and compiles it into the appropriate execution IR.

from solverpilot.model import Model

m = Model("semantic-lp")
x = m.variable(2, lower=0.0, upper=5.0, name="x")
m.add(x[0] + x[1] >= 1.0)
m.minimize(2.0 * x[0] + x[1])

compiled = m.compile()
result = compiled.solve()

print(type(compiled.execution_ir).__name__)  # LinearProblem
print(result.status.value)
print(result.objective)

Parameters preserve semantic structure while allowing data changes and compiler-cache reuse:

cost = m.parameter(2, value=[2.0, 1.0], name="cost")

See examples/08_semantic_model.py and the Extended Modeling API.

Conic modeling

The semantic layer supports SOC, RSOC, and PSD representation/validation.

from solverpilot.model import Model
from solverpilot.conic import ConicProblem

m = Model("soc")
x = m.variable(2)
t = m.variable(lower=0.0)
m.soc(t, x)
m.minimize(t)

compiled = m.compile(use_cache=False)
assert isinstance(compiled.execution_ir, ConicProblem)

Representation support is intentionally broader than verified solver support. PSD and generic quadratic-conic solving are not automatically authorized by the current backend conformance evidence.

See examples/09_conic_model.py.

Smooth nonlinear programming (NLP)

Install the pinned verification dependency:

python -m pip install "solverpilot[nlp]"

Example:

from solverpilot.model import Model

m = Model("nlp")
x = m.variable(lower=-4.0, upper=4.0)
m.minimize((x - 1.25) ** 4 + 0.2 * (x - 1.25) ** 2)
result = m.solve(x0=[0.0])

print(result.validation.valid)
print(result.local_optimal_candidate)
print(result.globally_proven)  # False for generic NLP

SolverPilot does not claim global optimality for generic NLPs. The optional CasADi/Ipopt path promotes a candidate only after independent primal/objective validation and recomputed KKT stationarity checks.

See examples/10_nlp_optional.py.

Certified convex binary MINLP

Install the NLP verification dependency:

python -m pip install "solverpilot[minlp]"

The current MINLP layer is intentionally narrow: binary discrete variables + certified convex supported nonlinear structure. It provides exact binary enumeration and an Outer Approximation orchestrator.

from solverpilot.model import Model

m = Model("binary-minlp")
x = m.variable(lower=0.0, upper=3.0)
z = m.binary()
m.minimize((x - 2.0) ** 2 + 0.2 * (1 - z))

result = m.compile().solve()
print(result.algorithm)
print(result.globally_proven)

globally_proven=True is emitted only when the certified scope, original-space validation, and bound-closure conditions all pass. In 0.2 this is a solver-certified proof under the compiler's convexity assumptions, not an independently reconstructed end-to-end proof; inspect result.proof_scope and result.independently_verified_global. General integer, nonconvex, nonlinear-equality, and indicator+MINLP compositions remain fail-closed.

See examples/11_minlp_optional.py.

Constraint programming (CP)

The CP layer is separate from the LP/MILP runtime; CP semantics are not silently converted to MILP.

A dependency-free exhaustive reference backend is included for small models:

from solverpilot.cp import CPModel

m = CPModel("tiny-cp")
x = m.int_var(0, 2, "x")
y = m.int_var(0, 2, "y")
m.add_all_different([x, y])
m.minimize(x + y)

result = m.solve()
print(result.objective)
print(result.validation.valid)

See examples/12_cp_reference.py.

For CP-SAT:

python -m pip install "solverpilot[cp]"

The integration is locked to OR-Tools 9.15.6755 in this release candidate. The native CP-SAT solve runs in an isolated subprocess so an earlier SciPy/HiGHS solve cannot poison OR-Tools with a conflicting HiGHS shared-library ABI. SolverPilot transfers only canonical JSON CP IR/results across that boundary and independently revalidates the assignment/objective in the parent process. Supported CP families include integer/Boolean variables, interval variables, linear constraints, AllDifferent, ExactlyOne, allowed Table, Element, Circuit, NoOverlap, Cumulative, and integer min/max objectives.

See examples/13_cp_sat_optional.py.

Choosing a backend

For the matrix-first runtime, omit backend for deterministic capability-aware routing:

result = solve(problem)
print(result.trace.backend)

Or request a backend explicitly:

result = solve(problem, backend="scipy-highs-ipm")

The Capability Protocol 2.0 extended layer is available under solverpilot.capabilities and can represent version-bound support, restrictions, verification evidence, incremental-update channels, starts, callbacks, and result artifacts without upgrading legacy declarations to verified claims.

CLI:

solverpilot-backend-health
solverpilot-capabilities --verify

Full example: examples/04_choose_backend.py

Understanding SolveResult

The matrix-first solve() API returns SolveResult.

Candidate-bearing success statuses are exposed only after validation. INFEASIBLE, UNBOUNDED, and INFEASIBLE_OR_UNBOUNDED normally reflect backend termination unless separate certificate/diagnostic evidence is explicitly recorded.

See examples/06_validation_diagnostics.py.

Infeasible problems and diagnostics

Diagnostics are opt-in:

result = solve(problem, diagnose_infeasible=True)
print(result.status.value)
print(result.diagnostics)

See examples/05_infeasible.py.

Repeated solves and persistence

Lightweight Session

Session tracks problem revisions and reports reuse conservatively.

See examples/07_reoptimization_session.py.

Track P PersistentSession

solverpilot.session.PersistentSession binds a semantic Model to one backend lifecycle. Each update is routed through one of:

• cold_build
• native_patch
• safe_rebuild
• full_rebuild
• no_mutation_reuse

A native patch is selected only when the exact granular capability and persistent-lifecycle evidence are verified. Reuse is not treated as a universal performance win.

Optional solver dependencies

An upstream package being installable is not by itself SolverPilot integration evidence. Release qualification remains platform/version specific.

What is not supported or not claimed yet

• learned LP performance routing in production;
• nonconvex or integer variables in the existing convex QuadraticProblem; use the separate GlobalQuadraticProblem;
• independent verification for every PSD matrix or transcendental cone dual;
• independent global optimality for arbitrary NLP/MINLP functions;
• nonsmooth atoms on the smooth Ipopt path; supported abs/max/min are available on the explicit bounded global path;
• exact SCIP and GPU execution adapters; readiness probes alone do not qualify them;
• nonlinear equalities/two-sided nonlinear constraints in the current MINLP proof path;
• global indicator bodies without a provable finite interval enclosure;
• automatic guarantee that repeated solves use native warm starts/factorization reuse;
• automatic independent certificates for every infeasible/unbounded backend termination;

Per-call tolerances=ValidationTolerances(...) is available on solve() and solve_production(). The 0.4 scope table maps each research item to its implemented API and remaining boundary.

See Known Limitations.

Examples

Core/runtime:

1. 01_lp_basic.py — LP
2. 02_milp_binary.py — binary MILP
3. 03_qp_convex.py — convex QP
4. 04_choose_backend.py — backend selection
5. 05_infeasible.py — diagnostics
6. 06_validation_diagnostics.py — result validation
7. 07_reoptimization_session.py — repeated solves

Extended modeling:

8. 08_semantic_model.py — semantic model/compiler
9. 09_conic_model.py — SOC representation
10. 10_nlp_optional.py — optional smooth NLP
11. 11_minlp_optional.py — optional certified binary MINLP
12. 12_cp_reference.py — dependency-free CP reference backend
13. 13_cp_sat_optional.py — optional OR-Tools CP-SAT
14. 14_persistent_session_optional.py — persistent semantic-model session

New in 0.4: global optimization, generalized power, and PDLP/certificate recovery. See all 24 examples in examples/README.md.

API Reference

• Stable top-level Public API
• Extended Modeling API
• Current frozen top-level API snapshot
• Current core backend contract
• Track P merge provenance

Trust and provenance

The original frozen Track P P0–P9 bundle was verified before forward-porting:

• source bundle SHA-256: 6cc282b9428450ab7bbc39cd6c768de2da2fc092f8d7f79cbbf319f657df24a2
• internal checksum rows: 816
• checksum mismatches: 0
• original Track P final version: 0.0.40
• OR-Tools qualification target: 9.15.6755

Frozen historical Track P documents remain under docs/history/track-p-p0-p9-frozen/. They intentionally retain the old OptiMind naming as provenance; the current public brand and namespace are SolverPilot / solverpilot.

Production routing

solve_production() remains conservative. The historical learned LP selector remains disabled; merging Track P does not authorize a new empirical performance-ranking claim.

Development, security, and release status

• Contributing
• Security policy
• Release process
• Known limitations

Normal CI targets Python 3.12–3.14. The exact-artifact cross-platform release qualification workflow remains the authority for public release support.

License

SolverPilot is licensed under the Apache License 2.0. Apache-2.0 permits commercial and closed-source use subject to its terms. It does not grant trademark rights.

Version 0.2 guide

See the 0.2 guide for new APIs, installation, numerical guarantees, batch limits and examples.