SimpliPy:
Efficient Simplification of Mathematical Expressions

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Usage

pip install simplipy

The compiled Rust extension (simplipy._core) is required: the inline phase (simplify, conversions, validation) runs on it exclusively, and there is no pure-Python fallback. Prebuilt wheels are published for Linux (x86_64/aarch64), macOS (x86_64/arm64) and Windows (x64) on CPython ≥ 3.12, so pip install simplipy does not compile anything for most users. Installing from the source distribution (an unsupported platform, or --no-binary) requires a Rust toolchain (rustup, MSRV 1.83). If the extension is missing at runtime, constructing an engine raises ImportError.

import simplipy as sp

engine = sp.SimpliPyEngine.load("acj-5-4-llm", install=True)   # a published ruleset artifact

# Simplify infix expressions
engine.simplify('x3 * sin(<constant> + 1) / (x3 * x3)')
# > '<constant>/x3'

# Simplify prefix expressions
engine.simplify(('/', '<constant>', '*', '/', '*', 'x3', '<constant>', 'x3', 'log', 'x3'))
# > ('/', '<constant>', 'log', 'x3')

simplify only simplifies: it answers in the form it was given -- a str in, a str out; explicit binary prefix in, explicit binary prefix out; the engine's native tagged form in (n-ary +/* bags are delimited: <add> ... </add>, <mul> ... </mul>), tagged out. To change the NOTATION, convert -- to_infix / to_prefix / to_tagged are pure syntactic conversions that never simplify -- and compose the two:

expr = ('/', '<constant>', '*', '/', '*', 'x3', '<constant>', 'x3', 'log', 'x3')

engine.to_infix(engine.simplify(expr))       # simplify, then render
# > '<constant> / log(x3)'

engine.simplify(engine.to_tagged(expr))      # convert, then simplify: the tagged answer
# > ['<mul>', '<constant>', '<div>', 'log', 'x3', '</mul>']

Normalization

The root-exported to_skeleton, to_expression, and normalize_variable_token helpers (also available as simplipy.normalization) canonicalize an expression so that two expressions that are "the same" up to variable renaming / constant values compare equal. Each takes all three forms (infix str, explicit prefix, tagged) and returns the one it was given; the canonicalization runs through the engine's internal state, so the answer does not depend on the dialect you passed.

import simplipy as sp

# Skeleton form: variables -> x{n}, EVERY numeric literal -> <constant>
sp.to_skeleton(['+', 'v1', '2.5'], engine)
# > ['+', 'x1', '<constant>']

# Expression form: variables canonicalized, numeric values kept
sp.to_expression(['+', 'V1', '3'], engine)
# > ['+', 'x1', '3']

# Classify / canonicalize a single token -> (normalized_token, is_variable)
sp.normalize_variable_token('X3')
# > ('x3', True)
sp.normalize_variable_token('sin')
# > ('sin', False)

More examples can be found in the documentation.

Performance

On a 65,536-expression symbolic-regression benchmark, paired per-row against SymPy's simplify (serial single-core, 1 s cap):

SimpliPy SymPy
Mean size ratio (lower is better) 0.97 1.09
Expressions strictly simplified 13.2% 22.3%
Expressions made bigger 0.00% 45.1%
Median per-row speedup ≈260× 1×

Compression on the SR benchmark

Full results, figures, and methodology: simplify guide · paper.

Development

Setup

To set up the development environment, run the following commands:

pip install -e .[dev]
pre-commit install

Tests

Test the package with pytest:

pytest tests --cov src --cov-report html

or to skip integration tests,

pytest tests --cov src --cov-report html -m "not integration"

Citation

@inproceedings{saegert2026breakingsimplificationbottleneckamortized,
  title   = {Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression},
  author  = {Paul Saegert and Ullrich Köthe},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
  year    = {2026},
  eprint  = {2602.08885},
  archivePrefix =  {arXiv},
  primaryClass  = {cs.LG},
  url     = {https://arxiv.org/abs/2602.08885},
}

% Optionally
@software{simplipy-2026,
    author = {Paul Saegert},
    title = {Efficient Simplification of Mathematical Expressions},
    year = 2026,
    publisher = {GitHub},
    version = {0.14.0},
    url = {https://github.com/psaegert/simplipy}
}

Project links

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Credits

Author: Paul Saegert

GitHub Statistics

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License expression

MIT
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Requires

Python >=3.12

Provides Extra

dev torch

Tags

symbolic-regression simplification expression prefix rewriting rust

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