---
id: dwave-optimization
version: "0.7.2"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# dwave-optimization — Enables the formulation of nonlinear models for industrial optimization problems.

License: permissive · Maintenance: active · Downloads: 95.3K/mo

## 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 above — verify before relying.

Formulates nonlinear optimization models symbolically using NumPy-inspired syntax for use with D-Wave's Stride hybrid solver.

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

pip install dwave-optimization
uv add dwave-optimization
poetry add dwave-optimization

## Installing dwave-optimization

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.

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())

Requires Python 3.10 or later; precompiled wheels available for macOS, Windows, and Linux on x86_64 and ARM architectures.

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_current
- Install friction: medium
- Maintenance: active
- Downloads: 95.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

nonlinear optimization modeling, quantum hybrid solver formulation, d-wave stride optimization, symbolic optimization models, combinatorial optimization problems, array-based model construction, industrial optimization formulation, quantum-optimization, hybrid-solver, combinatorial-optimization

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