dwave-optimization
Enables the formulation of nonlinear models for industrial optimization problems.
Decision gist · record as of 2026-08-14
Yes, if you are actively using or evaluating D-Wave's Stride solver for nonlinear or combinatorial optimization. The package is actively maintained, has no known vulnerabilities, and provides a clean Python interface for model formulation. Install friction is moderate but manageable. Not worth installing if you have no D-Wave solver access or are looking for a general-purpose local optimizer.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; precompiled wheels available for macOS, Windows, and Linux on x86_64 and ARM architectures.
- Medium install friction due to compiled wheels across multiple Python versions and architectures.
- Actively maintained with recent release (22 days old) and ongoing repository activity.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions.
last release 2026-07-23 (22 days) · last repo commit 2026-08-14 · 30 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 95,340 downloads/mo, #13,268 on PyPI
Alternatives
Verify before relying
pip install dwave-optimization
from dwave.optimization import Model
import numpy as np
model = Model()
flows = model.constant([[0, 4, 2], [3, 0, 7], [1, 8, 0]])
assignment = model.list(3)
model.minimize((flows * assignment).sum())- Whether D-Wave Stride solver access or additional setup is required beyond this package to actually solve models
- Performance characteristics and scalability limits for model complexity
- Integration requirements with the broader D-Wave Ocean SDK ecosystem
What it is and what it does
dwave-optimization is a library for building nonlinear optimization models using a symbolic, array-based syntax inspired by NumPy. It lets you express combinatorial and nonlinear optimization problems—like facility assignment, routing, or resource allocation—as Python code, then submit those models to D-Wave's Stride hybrid solver for solution. The package handles model construction and provides generators for common problem types; the actual solving happens through D-Wave's hybrid quantum-classical infrastructure.
The library targets industrial optimization workflows where traditional solvers struggle with large, nonlinear, or combinatorial search spaces. You define decision variables (lists, arrays, or constants), express constraints and objectives symbolically, and the package translates that into a form Stride can process. It depends only on NumPy and is available as precompiled wheels for Python 3.10–3.14 on major platforms.
Use it for
- Formulate quadratic assignment problems (assigning facilities to locations to minimize cost) for hybrid solving
- Build nonlinear constraint models for industrial scheduling or resource allocation tasks
- Prototype combinatorial optimization problems before submitting to D-Wave's Stride solver
- Generate standard optimization problem instances (e.g., TSP variants) using built-in model generators
- Express symbolic optimization objectives that would be tedious to encode manually in lower-level APIs
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are actively using or evaluating D-Wave's Stride solver for nonlinear or combinatorial optimization.
The package is actively maintained, has no known vulnerabilities, and provides a clean Python interface for model formulation. Install friction is moderate but manageable. Not worth installing if you have no D-Wave solver access or are looking for a general-purpose local optimizer.
Install
dwave-optimization on PyPI
Before you install
Medium install friction due to compiled wheels across multiple Python versions and architectures. Actively maintained with recent release (22 days old) and ongoing repository activity.
Requires Python 3.10 or later; precompiled wheels available for macOS, Windows, and Linux on x86_64 and ARM architectures.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions.
Quickstart
pip install dwave-optimization
from dwave.optimization import Model
import numpy as np
model = Model()
flows = model.constant([[0, 4, 2], [3, 0, 7], [1, 8, 0]])
assignment = model.list(3)
model.minimize((flows * assignment).sum())Verify before relying
- Whether D-Wave Stride solver access or additional setup is required beyond this package to actually solve models
- Performance characteristics and scalability limits for model complexity
- Integration requirements with the broader D-Wave Ocean SDK ecosystem
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Actively maintained 22 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 95,340 / month, #13,268 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Operating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPython |
Evidence: dwave_optimization-0.7.2-cp310-cp310-macosx_10_13_x86_64.whl; dwave_optimization-0.7.2-cp310-cp310-macosx_11_0_arm64.whl; dwave_optimization-0.7.2-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; dwave_optimization-0.7.2-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; dwave_optimization-0.7.2-cp310-cp310-win_amd64.whl; dwave_optimization-0.7.2-cp311-cp311-macosx_10_13_x86_64.whl; dwave_optimization-0.7.2-cp311-cp311-macosx_11_0_arm64.whl; dwave_optimization-0.7.2-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; dwave_optimization-0.7.2-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; dwave_optimization-0.7.2-cp311-cp311-win_amd64.whl; dwave_optimization-0.7.2-cp312-abi3-macosx_10_13_x86_64.whl; dwave_optimization-0.7.2-cp312-abi3-macosx_11_0_arm64.whl; dwave_optimization-0.7.2-cp312-abi3-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; dwave_optimization-0.7.2-cp312-abi3-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; dwave_optimization-0.7.2-cp312-abi3-win_amd64.whl; dwave_optimization-0.7.2-cp314-cp314t-macosx_10_15_x86_64.whl; dwave_optimization-0.7.2-cp314-cp314t-macosx_11_0_arm64.whl; dwave_optimization-0.7.2-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; dwave_optimization-0.7.2-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; dwave_optimization-0.7.2-cp314-cp314t-win_amd64.whl
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