Guides & Tutorials
From an ordinary decision to a model you can test
Assigning work to a team begins with a list of tasks and people. Qualifications, availability, dependencies and competing priorities turn that list into a modeling problem.
A dependable optimization workflow then needs good data, a well-defined mathematical objective, a tested solver and an application that understands the result.
Our practical guides follow this journey. Each starts with a specific decision and makes the assumptions, constraints or output clear enough for the reader to inspect.
Start with the essentials
All guides- 01
Decide what the model may choose
Separate given facts from decision variables and define how a valid and useful answer will be recognized.
Your first optimization model Variables, constraints and objectives
- 02
Model the rules that matter
Distinguish hard limits from preferences and learn how operating details alter feasible decisions.
- 03
Prepare and verify the inputs
Check calendars, units, eligibility, missing values and the age of source data.
- 04
Read the result carefully
Learn what feasible, best-known and proven optimal mean, and how to compare a proposed plan with the one currently used.
Build the application around the solver
A correct mathematical assignment does not become a reliable production decision automatically. Preserve input versions, check results independently and define what the application does when computation or validation fails.
Work with QUBO and quantum problems
QUBO from formulation to result
Quantum circuits