Function minimize 

pub fn minimize<T>(
    f: &impl ObjectiveFunction<T>,
    initial_point: &[T],
    config: &OptimizationConfig<T>,
) -> OptimizationResult<T>
where
    T: Float + Debug,

Minimizes an objective function using the BFGS (Broyden–Fletcher–Goldfarb–Shanno) method.

BFGS is a quasi-Newton method that approximates the Hessian matrix using gradient information. It maintains a positive definite approximation to the Hessian matrix and updates it iteratively.

Arguments

• f - The objective function to minimize
• initial_point - The starting point for optimization
• config - Configuration options for the optimization process

Returns

Returns an OptimizationResult containing the optimal point found and optimization statistics.

Examples

use algos::math::optimization::{ObjectiveFunction, OptimizationConfig};
use algos::math::optimization::bfgs::minimize;

// Define a simple quadratic function
struct Quadratic;

impl ObjectiveFunction<f64> for Quadratic {
    fn evaluate(&self, point: &[f64]) -> f64 {
        point.iter().map(|x| x * x).sum()
    }

    fn gradient(&self, point: &[f64]) -> Option<Vec<f64>> {
        Some(point.iter().map(|x| 2.0 * x).collect())
    }
}

let f = Quadratic;
let initial_point = vec![1.0, 1.0];
let config = OptimizationConfig::default();

let result = minimize(&f, &initial_point, &config);
assert!(result.converged);