Classical Algorithms API

API reference for classical optimization algorithms in opt.classical.

Module Overview

from opt.classical import (
    BFGS,
    NelderMead,
    SimulatedAnnealing,
    HillClimbing,
    Powell,
    TrustRegion,
)

Common Interface

class ClassicalAlgorithm(AbstractOptimizer):
    def __init__(
        self,
        func: Callable,
        lower_bound: float,
        upper_bound: float,
        dim: int,
        max_iter: int,
        **kwargs
    ):
        pass

    def search(self) -> tuple[np.ndarray, float]:
        pass

Available Algorithms

• BFGS - Quasi-Newton method
• NelderMead - Simplex-based derivative-free
• SimulatedAnnealing - Probabilistic metaheuristic
• HillClimbing - Local search
• Powell - Conjugate direction method
• TrustRegion - Constrained optimization

Example Usage

from opt.classical import NelderMead, SimulatedAnnealing
from opt.benchmark.functions import rosenbrock

# Nelder-Mead
nm = NelderMead(
    func=rosenbrock,
    lower_bound=-5,
    upper_bound=10,
    dim=10,
    max_iter=1000
)
solution, fitness = nm.search()

# Simulated Annealing
sa = SimulatedAnnealing(
    func=rosenbrock,
    lower_bound=-5,
    upper_bound=10,
    dim=10,
    max_iter=1000,
    initial_temp=100,
    cooling_rate=0.95
)
solution, fitness = sa.search()

See Also

• Classical Algorithms - Algorithm details