pylbfgs

library0.2.0.16pypypi✓ verified 82d ago

PyLBFGS provides L-BFGS and OWL-QN optimization algorithms for large-scale unconstrained and bound-constrained optimization. Current version is 0.2.0.16, but development appears to be in maintenance mode with infrequent releases.

ai-ml

pip install pylbfgs

github ↗package ↗

INSTALL

IMPORT

SIG · PYLBFGS

pylbfgs

ai-mlpythonv0.2.0.16

Install

3.8s avg

Import

—

Disk

90MB

Pass rate

4/ 10

Env Coverage4 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.2.0.16 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 3.8s

py 3.11

✕ build_error

✓ 3.6s

py 3.12

✕ build_error

✓ 3.4s

py 3.13

✕ build_error

py 3.9

✕ build_error

✓ 4.4s

90MB installed

● package 90MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

LBFGS

✓ from lbfgs import LBFGS

✗ from pylbfgs import LBFGS

fmin_lbfgs

✓ from lbfgs import fmin_lbfgs

✗ from pylbfgs import fmin_lbfgs

LBFGSError

✓ from lbfgs import LBFGSError

✗ from pylbfgs import LBFGSError

Minimize a simple quadratic function using L-BFGS.

import numpy as np from pylbfgs import LBFGS def evaluate(x, g): # f(x) = (x-2)^2, gradient = 2*(x-2) f = np.sum((x - 2.0) ** 2) g[:] = 2.0 * (x - 2.0) return f x0 = np.array([0.0, 0.0]) optimizer = LBFGS() result = optimizer.minimize(evaluate, x0) print("Optimal x:", result)

Debug

Known issues

gotchaThe library does not support Python 3.10+ out of the box due to missing C extension compilation. Users may need to install from source or use a conda-forge build.

fix

Prefer using scipy.optimize.minimize with method='L-BFGS-B' as a more maintained alternative.

affects: >=0.2.0.16

deprecatedThe library is effectively unmaintained; the last release was in 2017. No updates for compatibility with modern Python or NumPy.

fix

Consider switching to scipy.optimize (L-BFGS-B) or pyLBFGS (note different casing: pylbfgs vs pyLBFGS).

affects: all

gotchaThe evaluate function must modify the gradient array in-place; failing to do so leads to incorrect optimization.

fix

Always assign g[:] = ... or use numpy's in-place operations like np.copyto(g, ...).

affects: all

Upgrade

Version history

0.2.0.16latest on PyPI · released Jun 20, 2024

Audit

Dependencies

No dependency data recorded yet.

Agent activity

18 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/dedupeio/pylbfgs ↗

packagehttps://pypi.org/project/pylbfgs/ ↗