sparsediffpy
Python bindings for SparseDiffEngine algorithmic differentiation
Decision gist · record as of 2026-08-14
Yes, if you need efficient sparse Jacobian or Hessian computation in Python. The package is actively maintained, has no known vulnerabilities, uses a permissive license, and provides a clean Python interface to a specialized C library. Install friction is moderate but manageable with pre-built wheels. Primary consideration: verify that the _sparsediffengine API matches your differentiation needs before committing to a production workflow.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later (requires_python: >=3.11).
- Medium install friction due to compiled wheels across multiple Python versions and platforms.
- Package is actively maintained with a recent release, and requires only numpy as a runtime dependency.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing use in commercial and proprietary projects with minimal restrictions beyond attribution.
last release 2026-07-13 (32 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,504,507 downloads/mo, #3,828 on PyPI
Alternatives
Verify before relying
pip install sparsediffpy
from sparsediffpy import _sparsediffengine- Specific capabilities and API surface of _sparsediffengine module beyond import.
- Performance characteristics or scale limits for sparse Jacobian/Hessian computation.
- Whether the package is suitable for production use or primarily experimental.
- Typical use patterns and integration with numpy workflows.
What it is and what it does
SparseDiffPy is a Python wrapper around SparseDiffEngine, a C library specialized in computing sparse Jacobians and Hessians—the first and second derivatives of functions that have sparse structure. It bridges high-level Python code to low-level compiled differentiation routines, allowing developers to leverage algorithmic differentiation without writing C directly.
The package depends only on numpy and targets modern Python versions (3.11+), with pre-built wheels available for macOS, Linux, and Windows across multiple architectures. It is actively maintained with no known security vulnerabilities. The primary use case is numerical optimization, machine learning, and scientific computing workflows where computing derivatives efficiently is critical and the underlying functions exhibit sparsity patterns that can be exploited for speed.
Use it for
- Computing sparse Jacobians for large-scale optimization problems where most partial derivatives are zero.
- Efficient Hessian computation in machine learning model training and hyperparameter optimization.
- Numerical simulations and scientific computing where derivative evaluation is a bottleneck.
- Sensitivity analysis in engineering and physics simulations with sparse dependency graphs.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need efficient sparse Jacobian or Hessian computation in Python.
The package is actively maintained, has no known vulnerabilities, uses a permissive license, and provides a clean Python interface to a specialized C library. Install friction is moderate but manageable with pre-built wheels. Primary consideration: verify that the _sparsediffengine API matches your differentiation needs before committing to a production workflow.
Install
sparsediffpy on PyPI
Before you install
Medium install friction due to compiled wheels across multiple Python versions and platforms. Package is actively maintained with a recent release, and requires only numpy as a runtime dependency.
Requires Python 3.11 or later (requires_python: >=3.11).
License in practice
Licensed under Apache-2.0 (permissive), allowing use in commercial and proprietary projects with minimal restrictions beyond attribution.
Quickstart
pip install sparsediffpy
from sparsediffpy import _sparsediffengineVerify before relying
- Specific capabilities and API surface of _sparsediffengine module beyond import.
- Performance characteristics or scale limits for sparse Jacobian/Hessian computation.
- Whether the package is suitable for production use or primarily experimental.
- Typical use patterns and integration with numpy workflows.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Actively maintained 32 days since the last release |
| First released | |
| Downloads | 1,504,507 / month, #3,828 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: sparsediffpy-0.6.1-cp311-cp311-macosx_10_9_universal2.whl; sparsediffpy-0.6.1-cp311-cp311-macosx_10_9_x86_64.whl; sparsediffpy-0.6.1-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; sparsediffpy-0.6.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; sparsediffpy-0.6.1-cp311-cp311-win_amd64.whl; sparsediffpy-0.6.1-cp312-cp312-macosx_10_13_universal2.whl; sparsediffpy-0.6.1-cp312-cp312-macosx_10_13_x86_64.whl; sparsediffpy-0.6.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; sparsediffpy-0.6.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; sparsediffpy-0.6.1-cp312-cp312-win_amd64.whl; sparsediffpy-0.6.1-cp313-cp313-macosx_10_13_universal2.whl; sparsediffpy-0.6.1-cp313-cp313-macosx_10_13_x86_64.whl; sparsediffpy-0.6.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; sparsediffpy-0.6.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; sparsediffpy-0.6.1-cp313-cp313-win_amd64.whl; sparsediffpy-0.6.1-cp314-cp314-macosx_10_15_universal2.whl; sparsediffpy-0.6.1-cp314-cp314-macosx_10_15_x86_64.whl; sparsediffpy-0.6.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; sparsediffpy-0.6.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; sparsediffpy-0.6.1-cp314-cp314-win_amd64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “sparse jacobian hessian computation”
- sparsediffpySparseDiffPy provides Python bindings to compute sparse Jacobians and…
- numdifftoolsComputes numerical derivatives, gradients, Jacobians, Hessians, and…
- tabmatProvides efficient matrix classes for tabular data that mix dense,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Mathematics packages
NetworkX provides data structures and algorithms for creating, analyzing, and manipulating graphs and networks, supporting everything from simple undirected graphs to complex directed and weighted networks.
kiwisolver is a Python binding to a fast C++ implementation of the Cassowary constraint solver, enabling you to solve systems of linear constraints and inequalities.
Install it if you need to solve constraint systems; skip it if you only need simple linear algebra.
SymPy is a Python library for symbolic mathematics, performing algebraic manipulation, calculus, equation solving, and mathematical expression simplification without numerical approximation.
ContourPy calculates contours of 2D quadrilateral grids using C++11 algorithms wrapped in Python, offering serial and multithreaded implementations without requiring Matplotlib as a dependency.
PyTorch provides GPU-accelerated tensor computation and automatic differentiation for building and training deep neural networks in Python.
onnxruntime loads and executes Open Neural Network Exchange (ONNX) models with a focus on inference performance across CPUs and accelerators.
Install it if you have ONNX models to run in production or development.
See also casadi · numdifftools · findiff · jax · Theano · autograd · Theano-PyMC · gpytorch · diffrax · nvidia-cusparselt-cu13