Gradient-Free-Optimizers

Examples using Surfaces with Gradient-Free-Optimizers.

---

Basic GFO Usage

from gradient_free_optimizers import RandomSearchOptimizer
from surfaces.test_functions.algebraic import RastriginFunction

func = RastriginFunction(n_dim=5)

# Search space works directly - no conversion needed
opt = RandomSearchOptimizer(func.search_space)
opt.search(func, n_iter=100)

print(f"Best score: {opt.best_score:.6f}")
print(f"Best params: {opt.best_para}")

---

Different Optimizers

from gradient_free_optimizers import (
    RandomSearchOptimizer,
    HillClimbingOptimizer,
    SimulatedAnnealingOptimizer,
    BayesianOptimizer,
    ParticleSwarmOptimizer,
)
from surfaces.test_functions.algebraic import RastriginFunction

func = RastriginFunction(n_dim=10)

optimizers = [
    ('Random', RandomSearchOptimizer),
    ('HillClimbing', HillClimbingOptimizer),
    ('SimAnnealing', SimulatedAnnealingOptimizer),
    ('Bayesian', BayesianOptimizer),
    ('PSO', ParticleSwarmOptimizer),
]

for name, OptClass in optimizers:
    opt = OptClass(func.search_space)
    opt.search(func, n_iter=100)
    print(f"{name:15s}: {opt.best_score:.6f}")

---

With Memory Tracking

from gradient_free_optimizers import BayesianOptimizer
from surfaces.test_functions.algebraic import RosenbrockFunction

func = RosenbrockFunction(n_dim=5)

opt = BayesianOptimizer(func.search_space)
opt.search(func, n_iter=100, memory=True)

# Access search history
print(f"Total evaluations: {len(opt.search_data['score'])}")
print(f"Best score: {opt.best_score:.6f}")

# Convergence analysis
scores = opt.search_data['score']
best_so_far = [min(scores[:i+1]) for i in range(len(scores))]
print(f"Final best: {best_so_far[-1]:.6f}")