{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"Formulates nonlinear optimization models symbolically using NumPy-inspired syntax for use with D-Wave's Stride hybrid solver.","skillfed_tags":["quantum-optimization","hybrid-solver","combinatorial-optimization"],"use_cases":["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"],"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\u2014like facility assignment, routing, or resource allocation\u2014as 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.\n\nThe 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\u20133.14 on major platforms.","worth_installing":"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."},"id":"dwave-optimization","links":{"html":"https://skillfed.io/packages/dwave-optimization","md":"https://skillfed.io/packages/dwave-optimization.md","pypi":"https://pypi.org/project/dwave-optimization/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-23","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"dwave-optimization","python_support":"supports_current","summary":"Enables the formulation of nonlinear models for industrial optimization problems."},"popularity":{"monthly_downloads":95340,"position":13268,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.7.2"}