cybrentq 0.1.1

A Cython implementation of Brent's root-finding method

cybrentq

A production-quality Cython implementation of Brent's root-finding method. The algorithm is a faithful translation of the C routine in scipy/optimize/Zeros/brentq.c: bisection guarded by inverse quadratic interpolation, with a secant fallback and bisection used whenever an interpolation step would not make adequate progress.

The public API mirrors the relevant subset of scipy.optimize.brentq and can serve as a drop-in replacement in most code paths.

Requires Python 3.10 or newer and a working C compiler (used during install to compile the Cython extension).

Quick start

This project uses uv for environment and dependency management, with a Makefile wrapping the most common commands.

# Install uv if you don't have it:
#   curl -LsSf https://astral.sh/uv/install.sh | sh   (macOS / Linux)
#   powershell -c "irm https://astral.sh/uv/install.ps1 | iex"   (Windows)

# Recommended: use the Makefile. PYTHON_VERSION=3.10 by default.
# The Cython extension is compiled during `make sync`.
make venv
make sync

The Makefile passes hardening flags the bare commands omit (--frozen --refresh --all-extras --all-groups --link-mode=copy, plus --seed --prompt=cybrentq on venv). Read the Makefile for the full invocations.

If you want a minimal install without the Makefile:

uv venv --python 3.10       # create the venv
uv sync                     # install project + default groups, compile Cython
uv sync --group test        # add the test group only
uv sync --no-dev            # consumer-style install (no dev groups)

uv sync reads pyproject.toml, provisions a matching interpreter, builds the C extension via the PEP 517 backend (setuptools + Cython), and writes a uv.lock. Commit uv.lock for reproducible builds across machines.

Editable iteration on the Cython source

uv sync installs the project into the managed venv. After editing _brentq.pyx, re-run uv sync --reinstall-package cybrentq (or just uv sync if you also bump the version) to force a recompile.

Usage

import math
from cybrentq import brentq

# Basic usage — finds sqrt(2)
root = brentq(lambda x: x * x - 2.0, 0.0, 2.0)

# Pass extra arguments via the args tuple
root = brentq(lambda x, target: x ** 3 - target, 0.0, 5.0, args=(27.0,))

# Diagnostics
root, info = brentq(lambda x: math.cos(x) - x, 0.0, 1.0, full_output=True)
print(info)
# RootResults(root=0.7390851332151607, iterations=8, function_calls=10,
#             converged=True, flag='converged')

Signature

brentq(
    f,                          # callable, f(x, *args) -> float
    a, b,                       # bracketing interval, f(a)*f(b) < 0
    args=(),                    # extra positional args forwarded to f
    xtol=2e-12,                 # absolute tolerance
    rtol=4*eps,                 # relative tolerance (>= 4 * machine eps)
    maxiter=100,                # iteration budget
    full_output=False,          # return RootResults alongside the root
    disp=True,                  # raise on non-convergence instead of returning a flag
) -> float | tuple[float, RootResults]

Errors

Condition	Behaviour
f(a) and f(b) have the same sign	ValueError
xtol <= 0, rtol < 4*eps, or maxiter < 1	ValueError
Iteration budget exhausted and disp=True	ConvergenceError
Iteration budget exhausted and disp=False	returns (root, RootResults(converged=False))
User-supplied f raises	exception propagates unchanged

Tests

make test                                   # all tests
make cov                                    # coverage run (see warning below)
make report                                 # print coverage report from last run

# Direct equivalents for `make test`:
uv run pytest                               # all tests
uv run pytest tests/test_basic.py           # just behaviour / error tests
uv run pytest tests/test_against_scipy.py   # head-to-head correctness vs scipy
uv run pytest tests/test_performance.py     # perf regression guard (CI-safe)

Note on make cov: the target wipes .c/.so artifacts and reinstalls cybrentq with CYTHON_TRACE=1 so that coverage.py (via the Cython.Coverage plugin) can report line coverage from _brentq.pyx. The traced build is significantly slower at runtime. After running make cov, run make sync before make bench — otherwise the traced extension will skew benchmark numbers.

The scipy-comparison suite asserts that:

• both implementations report converged=True on every problem,
• root estimates agree to within 1e-10 relative and absolute tolerance,
• iteration counts and function-call counts match exactly (Brent's method is deterministic, so any divergence flags a bug in our translation).

Benchmarks

The benchmark suite compares this Cython brentq against scipy.optimize.brentq and against pymodab (a C implementation of the Modified Anderson-Björck root-finding method) across nine problems.

make bench                                              # default config
make bench BENCH_ARGS="--n-calls 5000 --repeats 7"      # override args

# Direct equivalent:
uv run python benchmarks/bench_brentq.py --n-calls 5000 --repeats 7

Sample output (numbers depend on the machine, Python build, and library versions):

problem                            scipy (µs)  cython (µs)   modab (µs)    cy/sc    mb/sc
-----------------------------------------------------------------------------------------
x^2 - 2 (cheap)                         8.374        0.518        2.665   16.16x    3.14x
x^3 - x - 2 (cheap)                     8.957        0.875        2.925   10.24x    3.06x
cos(x) - x                              7.978        0.782        2.718   10.20x    2.94x
exp(x) - 2                              9.862        0.981        2.950   10.05x    3.34x
sin(x), root at pi                      6.623        0.287        2.210   23.04x    3.00x
(x-1)^3 (flat)                          3.566        0.257        1.770   13.90x    2.02x
atan(1000*(x-0.5)) (steep)              3.692        0.341        1.864   10.82x    1.98x
poly degree 5                          13.879        2.010        4.445    6.90x    3.12x
expensive f (sum of 50 sines)          69.151       57.117       59.970    1.21x    1.15x
-----------------------------------------------------------------------------------------
cython vs scipy: gmean 9.24x  min 1.21x  max 23.04x
modab  vs scipy: gmean 2.52x  min 1.15x  max 3.34x

Columns: cy/sc is scipy_time / cython_time (Cython speedup over scipy); mb/sc is scipy_time / modab_time (pymodab speedup over scipy). Higher is better in both columns.

Before each timed problem the runner calls all three implementations and asserts the roots agree to within 1e-9 — a benchmark with a silent correctness regression is worse than one that doesn't run.

A few things to note when reading the numbers:

• Brent's method usually converges in 6–15 iterations, so most of the wall time of a single solve is spent inside f, not inside the algorithm. When f is expensive (the "sum of 50 sines" row), all implementations spend the vast majority of their time in f and look nearly identical.
• The Cython win is largest when f is cheap, because that is when the algorithm's own overhead matters most.
• pymodab's Modified Anderson-Björck typically uses fewer f evaluations than Brent — but each iteration carries pure-Python wrapper overhead here, so it lands between scipy and our Cython version.

Building distributions

make build            # equivalent to: uv build
                      # produces sdist + wheel under dist/

Make targets

make help lists every target with a short description.

Target	Action
venv	Create the project venv via uv venv (PYTHON_VERSION=3.10 by default)
lock	Resolve and write uv.lock (uv lock --upgrade --refresh)
outdated	Show outdated direct and transitive deps
sync	Sync the venv with uv.lock (all groups + extras)
test	Run the test suite without coverage
cov / test/cov	Recompile extension with CYTHON_TRACE=1 and run under coverage.py (no pytest-cov). Run make sync afterward to restore the fast build.
report / cov/report	Print the coverage report from the last make cov
bench	Run the benchmark suite (override args via BENCH_ARGS=…)
lint / lint/check	Read-only ruff check
lint/fix	Rewrites files — ruff check --fix
fmt / fmt/check	Read-only ruff format --check
fmt/fix	Rewrites files — ruff format
check	lint/check + fmt/check (CI-safe)
build / dist	Build sdist + wheel into dist/
clean	Remove build artifacts and coverage data

Bare lint and fmt are intentionally read-only; mutation requires the explicit /fix variant so typos don't rewrite the tree.

Linting and formatting

The project pins ruff (configuration in pyproject.toml under [tool.ruff]). Enabled rule families:

Code	Family
E / W	pycodestyle errors / warnings
F	pyflakes (unused imports, undefined names)
I	isort
N	pep8-naming
UP	pyupgrade
C90	mccabe complexity (max-complexity = 10)
A	flake8-builtins (shadowing)
B	flake8-bugbear
PERF	perflint
PT	flake8-pytest-style
RUF	ruff-native rules
SIM	flake8-simplify
TRY	tryceratops (exception hygiene)

Ruff lints .py files only — _brentq.pyx is outside the ruff scope.

make check            # lint + format check, no mutations (use this in CI)
make lint/fix         # rewrites files: ruff check --fix
make fmt/fix          # rewrites files: ruff format

Layout

src/cybrentq/
  _brentq.pyx           # the algorithm
  _brentq.pyi           # type stubs for IDEs / mypy
  __init__.py           # re-exports brentq, RootResults, ConvergenceError
  py.typed              # PEP 561 marker
tests/
  conftest.py           # shared fixture: 11 canonical root-finding problems
  test_basic.py         # behaviour, error paths, edge cases
  test_against_scipy.py # parity vs scipy
  test_performance.py   # perf regression guard
benchmarks/
  bench_brentq.py       # speed comparison vs scipy and pymodab
Makefile                # venv, sync, test, cov, bench, lint, fmt, build
pyproject.toml          # project metadata + ruff/coverage/pytest config

License

MIT.