scivex-optim 0.1.1

Scivex — Optimization, root finding, and numerical integration
Documentation 
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//! One-dimensional minimization algorithms.
//!
//! Find `x` that minimizes `f(x)` within a bracket `[a, b]`.

mod brent;
mod golden;

pub use brent::brent_min;
pub use golden::golden_section;

use scivex_core::Float;

/// Result of a 1-D minimization algorithm.
///
/// # Examples
///
/// ```
/// # use scivex_optim::minimize_1d::golden_section;
/// let result = golden_section(|x: f64| (x - 2.0).powi(2), 0.0, 5.0, 1e-10, 100).unwrap();
/// assert!(result.converged);
/// assert!((result.x_min - 2.0).abs() < 1e-5);
/// ```
#[cfg_attr(
    feature = "serde-support",
    derive(serde::Serialize, serde::Deserialize)
)]
#[derive(Debug, Clone)]
pub struct Minimize1dResult<T: Float> {
    /// The estimated minimizer.
    pub x_min: T,
    /// The function value at the minimizer: `f(x_min)`.
    pub f_min: T,
    /// Number of iterations performed.
    pub iterations: usize,
    /// Whether the algorithm converged within tolerances.
    pub converged: bool,
}