Get started¶
Choose the shortest route that matches what you want to accomplish. Installation, first-run code, result interpretation, custom-problem guidance, and durable-study guidance each have one canonical page; this page only routes between them.
Try VAMOS¶
- Install VAMOS from PyPI in an isolated Python environment.
- Follow T01. Your first meaningful result to solve a built-in benchmark and interpret its actual output. The Quickstart retains the complete run-to-study sequence.
- Read Understanding optimization results to connect decision rows (
X) with objective rows (F) and distinguish a returned set from its non-dominated subset. - Use the API reference when you need exact signatures rather than tutorial guidance.
If Python itself is still new to you, begin with the Minimal Python Track.
Solve your own problem¶
Follow T02 to check a constrained model, then T03 to select a feasible alternative using explicit preferences.
Use Solve your own problem to define objectives, bounds, encodings, vectorized evaluation, and constraints with make_problem(...).
Use the Experimental vamos create-problem command when you prefer a generated file scaffold. Adding a reusable built-in problem to VAMOS is a contributor workflow and is documented separately in Adding a problem.
Run a reproducible study¶
Follow T06: plan and run, T07: measure quality, then T08: build a report. The shared setup includes standalone scripts and a recorded experiment whose metrics can be recomputed without optimization.
Use Durable studies when you need a persistent problem-by-algorithm-by-seed matrix with explicit task state, resume, retry, inspection, and summaries.
Use Run artifacts & replay for individual persisted runs and for the distinction between loading, verification, and executable replay.
Choose an interface¶
| Need | Recommended surface |
|---|---|
| A Python script or notebook | vamos.optimize(...) |
Interpret X, F, and a non-dominated subset |
OptimizationResult + Understanding results |
| A plain Python objective function | vamos.make_problem(...) + vamos.optimize(...) |
| A stable command-line workflow | The main runner, run-artifact commands, and study commands in CLI & Config |
| An interactive onboarding wizard | Experimental vamos quickstart |
| Multiple seeds in one small call | optimize(..., seed=[...]) returning StudyResult |
| A persistent experiment matrix | StudySpec, plan_study, create_study, and Study.run() |
| Exact parameters for a built-in algorithm | Public configuration objects from vamos.algorithms |
VAMOS 1.0 uses NumPy as its deterministic reference backend. Reproducibility is a same-environment promise, not a cross-platform or cross-backend bitwise guarantee. See Stability and versioning and Known limitations for the exact supported surface.
Continue from here¶
- Tutorials — follow a learning path or study an algorithm through a worked example.
- How-to Guides — find instructions for a specific operation.
- Reference — locate exact interfaces, capabilities, and contracts.
- Examples — choose maintained scripts, notebooks, or task-oriented guides.
- Troubleshooting — installation, dependency, configuration, and runtime issues.
- Algorithms & Backends — algorithm-specific parameters and backend notes.
- Analysis & Visualization — Experimental helpers to inspect and visualize optimization results.