Constrained Optimization Algorithms

Algorithms designed to handle optimization problems with constraints.

Overview

Constrained optimization algorithms can handle problems with equality and inequality constraints, bounds, and other restrictions on the solution space.

Available Algorithms

• Penalty Method - Converts constrained to unconstrained problem
• Barrier Method - Interior point approach
• Augmented Lagrangian - Lagrange multiplier method
• Sequential Quadratic Programming - Iterative quadratic approximation
• Trust Region Constrained - Constrained trust region

Usage Example

from opt.constrained import PenaltyMethod
from opt.benchmark.functions import sphere

def constraint_eq(x):
    return x[0] + x[1] - 1.0  # x[0] + x[1] = 1

def constraint_ineq(x):
    return x[0] - 0.5  # x[0] >= 0.5

optimizer = PenaltyMethod(
    func=sphere,
    lower_bound=-5,
    upper_bound=5,
    dim=2,
    max_iter=100,
    constraints_eq=[constraint_eq],
    constraints_ineq=[constraint_ineq]
)
best_solution, best_fitness = optimizer.search()

See Also

• API Reference - Complete API documentation