optimization-solvers 0.1.1

Numerical optimization solvers for unconstrained and simple-bounds constrained convex optimization problems. Wasm compatible
Documentation 
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use super::*;
pub mod backtracking;
pub use backtracking::*;
pub mod morethuente;
pub use morethuente::*;
pub mod morethuente_b;
pub use morethuente_b::*;
pub mod backtracking_b;
pub use backtracking_b::*;
pub mod gll_quadratic;
pub use gll_quadratic::*;
pub mod nosearch;
pub use nosearch::*;
pub trait LineSearch {
    fn compute_step_len(
        &mut self,
        x_k: &DVector<Floating>,         // current iterate
        eval_x_k: &FuncEvalMultivariate, // function evaluation at x_k
        direction_k: &DVector<Floating>, // direction of the ray along which we are going to search
        oracle: &mut impl FnMut(&DVector<Floating>) -> FuncEvalMultivariate, // oracle
        max_iter: usize, // maximum number of iterations during line search (if direction update is costly, set this high to perform more exact line search)
    ) -> Floating; //returns the scalar step size
}

pub trait SufficientDecreaseCondition {
    fn c1(&self) -> Floating; // Armijo senstivity
    fn sufficient_decrease(
        &self,
        f_k: &Floating,
        f_kp1: &Floating,
        grad_k: &DVector<Floating>,
        t: &Floating, // step size
        direction_k: &DVector<Floating>,
    ) -> bool {
        f_kp1 - f_k <= self.c1() * t * grad_k.dot(direction_k)
    }
}

pub trait CurvatureCondition {
    fn c2(&self) -> Floating; // curvature senstivity
    fn curvature_condition(
        &self,
        grad_k: &DVector<Floating>,
        grad_kp1: &DVector<Floating>,
        direction_k: &DVector<Floating>,
    ) -> bool {
        grad_kp1.dot(direction_k) >= self.c2() * grad_k.dot(direction_k)
    }
    fn strong_curvature_condition(
        &self,
        grad_k: &DVector<Floating>,
        grad_kp1: &DVector<Floating>,
        direction_k: &DVector<Floating>,
    ) -> bool {
        grad_kp1.dot(direction_k).abs() <= self.c2() * grad_k.dot(direction_k).abs()
    }
}

pub trait WolfeConditions: SufficientDecreaseCondition + CurvatureCondition {
    fn wolfe_conditions_with_directional_derivative(
        &self,
        f_k: &Floating,
        f_kp1: &Floating,
        grad_k: &DVector<Floating>,
        grad_kp1: &DVector<Floating>,
        t: &Floating, // step size
        direction_k: &DVector<Floating>,
    ) -> bool {
        self.sufficient_decrease(f_k, f_kp1, grad_k, t, direction_k)
            && self.curvature_condition(grad_k, grad_kp1, direction_k)
    }
    fn strong_wolfe_conditions_with_directional_derivative(
        &self,
        f_k: &Floating,
        f_kp1: &Floating,
        grad_k: &DVector<Floating>,
        grad_kp1: &DVector<Floating>,
        t: &Floating, // step size
        direction_k: &DVector<Floating>,
    ) -> bool {
        self.sufficient_decrease(f_k, f_kp1, grad_k, t, direction_k)
            && self.strong_curvature_condition(grad_k, grad_kp1, direction_k)
    }
}
// Blanket implementation for WolfeConditions
impl<T> WolfeConditions for T where T: SufficientDecreaseCondition + CurvatureCondition {}