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lp2graph

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Typed-graph representation of LP, MIP, and MILP formulations.

lp2graph is a focused library for representing optimization problems as typed graphs and deriving multiple views of them — schema, hybrid, and ground — from a single canonical data model. It ships:

  • A version-stable JSON schema for formulations.
  • Three view derivations that turn a formulation into a typed graph.
  • Structural metrics computed deterministically over the model and views.
  • SVG renderers and a static interactive viewer.
  • Export adapters to PyG, DGL, NetworkX, LaTeX, and Pyomo.

The core has no solver dependency. Pyomo, JuMP, and friends are optional extras.

Install

pip install lp2graph                       # core only
pip install "lp2graph[networkx]"           # NetworkX export
pip install "lp2graph[pyg]"                # PyG export
pip install "lp2graph[all]"                # everything

60-second tour

from lp2graph import load
from lp2graph.views import schema, hybrid, ground
from lp2graph.metrics.structural import structural_summary
from lp2graph.metrics.flags import presence_flags
from lp2graph.render.svg import render_svg

f = load("formulations/constraints/mip_2_1_big_m.json")

# Three views from one canonical model.
g_schema  = schema(f)                       # templates and indices
g_hybrid  = hybrid(f)                       # + offset-labeled edges
g_ground  = ground(f, {"I": 4})             # + degeneracy filters

# Metrics, deterministic.
print(structural_summary(g_schema)["graph_diameter"].value)
print(presence_flags(f)["has_big_m"].value)

# Render with the deliberate visual identity.
open("graph.svg", "w").write(render_svg(g_hybrid, title=f.name))

Or from the command line:

lp2graph validate formulations/constraints/lp_1_1_fixed_sequence.json
lp2graph render   formulations/constraints/mip_2_1_big_m.json --view hybrid --output mip_2_1.svg
lp2graph metrics  formulations/constraints/mip_2_4_time_indexed.json
lp2graph export   formulations/constraints/mip_2_8_pesp.json --format latex

Text ⇄ graph, and solving (deterministic, no LLM)

Beyond the views, lp2graph offers a bidirectional, deterministic interface between a formulation's text and its graph, plus a real solver back-end:

# graph -> paper-style LaTeX (\mathcal sets, \sum, \forall, big-M) and back
lp2graph latex   formulations/constraints/pesp_solvable.json --output pesp.tex
lp2graph parse   pesp.tex                         # LaTeX -> canonical JSON

# ground with instance data and solve the MILP (CBC / HiGHS / Gurobi)
lp2graph solve   formulations/constraints/assignment.json \
                 --instance corpus/validation/codec_pipeline/instances/assignment_4x4.json

# graph -> natural-language problem description (+ data tables)
lp2graph describe formulations/constraints/pesp_solvable.json --instance ...

from lp2graph import load, to_canonical_latex, from_canonical_latex, describe
from lp2graph.solve import Instance, solve

f = load("formulations/constraints/assignment.json")
g = from_canonical_latex(to_canonical_latex(f))   # text round-trips the graph
print(solve(g, Instance(cardinalities={"W": 4, "J": 4},
                        parameters={"c": [[9, 2, 7, 8], ...]})).objective)

The codec is a tested fixed point and the solvable content round-trips exactly; an end-to-end suite confirms the JSON→LaTeX→parse→ground→solve loop reproduces independently-known optima across CBC/HiGHS/Gurobi. See docs/text-graph-interface.md.

What goes in, what comes out

A formulation is a JSON document validated against schema/canonical.schema.json. It declares index families (e.g. I, T), parameters, variable templates, constraint templates with quantifiers and bindings, and an optional objective with first-class terms.

The schema view exposes templates and indices — the topology of the problem. The hybrid view adds per-term offset, sign, and modulo labels. The ground view materializes every instance at given index cardinalities and applies degeneracy filters.

See docs/data-model.md and docs/views.md for the long form.

Status

v0.1 is an alpha. The schema and the canonical model are stable for a representative cross-section of formulations; the catalog is in active expansion. Tracked open questions live as open-question issues so they are visible.

Documentation

Contributing

See CONTRIBUTING.md. Bugs, new formulations, design decisions, and good-first-issues all have dedicated templates.

AI disclaimer

This repository was developed with substantial assistance from AI coding tools (primarily Anthropic's Claude). Code, documentation and results have been reviewed by the author, who takes full responsibility for the content.

License

Apache 2.0. Portions adapted from joernmht/raiLPminerExperimentation under MIT — see docs/extraction-report.md.