Skip to content

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

  1. Install VAMOS from PyPI in an isolated Python environment.
  2. Run the Quickstart to solve a built-in benchmark with the public optimize(...) facade.
  3. Read Understanding optimization results to connect decision rows (X) with objective rows (F) and distinguish a returned set from its non-dominated subset.
  4. 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

Use Solve your own problem to define objectives, bounds, encodings, vectorized evaluation, and constraints with make_problem(...).

Use vamos create-problem 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

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 guided command-line workflow vamos quickstart or the stable CLI commands documented in CLI & Config
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