# Nevergrad — Gradient-Free Optimization by Meta

> Nevergrad is a gradient-free optimization platform from Meta Research providing a unified interface to derivative-free optimizers for hyperparameter tuning, reinforcement learning, and scientific computing.

## Install

Save as a script file and run:

# Nevergrad — Gradient-Free Optimization by Meta

## Quick Use
```bash
pip install nevergrad
python -c "
import nevergrad as ng
def sphere(x):
    return sum(xi**2 for xi in x)
optimizer = ng.optimizers.NGOpt(parametrization=ng.p.Array(shape=(3,)), budget=100)
recommendation = optimizer.minimize(sphere)
print(recommendation.value)
"
```

## Introduction
Nevergrad is a Python library from Meta AI Research that provides gradient-free optimization algorithms under a common interface. It is designed for problems where gradients are unavailable or unreliable, such as hyperparameter tuning, reinforcement learning reward shaping, and simulation-based optimization.

## What Nevergrad Does
- Optimizes black-box functions without requiring gradients
- Provides 30+ optimization algorithms under a unified API
- Supports continuous, discrete, and mixed search spaces
- Offers built-in benchmarks for comparing optimizer performance
- Handles noisy objective functions with appropriate averaging

## Architecture Overview
Nevergrad defines a Parametrization system that describes the search space (scalars, arrays, choices, log-scales). An Optimizer wraps a specific algorithm (CMA-ES, differential evolution, PSO, etc.) and generates candidates via ask/tell. The ask method proposes parameter values; tell reports back the loss. This separation allows asynchronous and parallel evaluation.

## Self-Hosting & Configuration
- Install via pip with no heavy dependencies
- Define search spaces using ng.p.Scalar, ng.p.Array, or ng.p.Choice
- Select an optimizer or use NGOpt for automatic algorithm selection
- Set budget to control the total number of objective evaluations
- Use num_workers > 1 to evaluate candidates in parallel batches

## Key Features
- NGOpt meta-optimizer automatically picks the best algorithm for your problem
- Expressive parametrization supporting constraints and transformations
- Ask/tell interface enables asynchronous and distributed evaluation
- Built-in benchmarking suite for rigorous optimizer comparison
- Supports multi-objective optimization via Pareto front tracking

## Comparison with Similar Tools
- **Optuna** — Bayesian optimization with pruning for ML; Nevergrad covers broader optimization use cases beyond ML
- **Hyperopt** — TPE-based search for hyperparameters; Nevergrad offers more diverse algorithms
- **scipy.optimize** — classical numerical optimization; Nevergrad handles noisy, non-differentiable objectives
- **Ray Tune** — orchestrates trials at scale; Nevergrad focuses on the optimization algorithms themselves

## FAQ
**Q: What is NGOpt?**
A: NGOpt is a meta-optimizer that selects the best algorithm based on your problem characteristics (budget, dimensionality, noise level).

**Q: Can Nevergrad optimize discrete variables?**
A: Yes. Use ng.p.Choice for categorical variables and ng.p.TransitionChoice for ordered discrete parameters.

**Q: How do I run evaluations in parallel?**
A: Set num_workers and call ask() multiple times before calling tell() with results as they arrive.

**Q: Does Nevergrad support constraints?**
A: Yes. Apply register_cheap_constraint to penalize infeasible regions of the search space.

## Sources
- https://github.com/facebookresearch/nevergrad
- https://facebookresearch.github.io/nevergrad/

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