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Solve a problem

Once you have defined your optimization problem by creating components and building a model, you can solve it using the Optimizer class.

Creating an Optimizer​

import amigo as am

# Assume model is already built and initialized
opt = am.Optimizer(model)

Running Optimization​

The simplest way to solve a problem is:

opt.optimize()

This runs the optimization using default settings and updates the model with the optimal solution.

Optimizer Options​

Configure the optimizer with custom options:

options = {
    'max_iter': 500,
    'tol': 1e-8,
    'print_level': 5,
}

opt.set_options(options)
opt.optimize()

Common options include:

  • max_iter: Maximum number of iterations
  • tol: Convergence tolerance
  • print_level: Verbosity level (0-12)
  • mu_strategy: Barrier parameter strategy

Accessing Results​

After optimization, extract the solution from the model:

# Get optimal values
x_opt = model.get_input("component.x")
f_opt = model.get_objective("component.f")
constraint = model.get_constraint("component.g")

print(f"Optimal x: {x_opt}")
print(f"Objective: {f_opt}")
print(f"Constraint: {constraint}")

Complete Workflow​

import amigo as am

# 1. Create component
class MyComponent(am.Component):
    def __init__(self):
        super().__init__()
        self.add_input("x", value=0.0, lower=-10.0, upper=10.0)
        self.add_objective("f")
        self.add_constraint("g", upper=0.0)
    
    def compute(self):
        x = self.inputs["x"]
        self.objective["f"] = x * x
        self.constraints["g"] = x - 5.0

# 2. Build model
model = am.Model("optimization")
model.add_component("comp", 1, MyComponent())
model.build_module()
model.initialize()

# 3. Solve
opt = am.Optimizer(model)
opt.optimize()

# 4. Extract solution
x_opt = model.get_input("comp.x")
f_opt = model.get_objective("comp.f")

print(f"Solution: x = {x_opt:.6f}, f(x) = {f_opt:.6f}")
tip

Set print_level: 5 to see detailed iteration output during optimization.

See the Optimizer API documentation for complete details on configuration options.