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FrankWolfeOptimizer

oxiphysics_core::convex_analysis

Struct FrankWolfeOptimizer 

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pub struct FrankWolfeOptimizer {
    pub max_iter: usize,
    pub tol: f64,
}
Expand description

Frank-Wolfe (conditional gradient) method for constrained convex problems.

Minimises a smooth convex function f over a compact convex feasible set by solving a linear minimisation oracle at each step.

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§max_iter: usize

Maximum number of iterations.

§tol: f64

Tolerance on the Frank-Wolfe duality gap.

Implementations§

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impl FrankWolfeOptimizer

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pub fn new(max_iter: usize, tol: f64) -> Self

Create a new Frank-Wolfe optimiser.

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pub fn minimize( &self, x_init: Vec<f64>, grad_f: &dyn Fn(&[f64]) -> Vec<f64>, lmo: &dyn Fn(&[f64]) -> Vec<f64>, f_val: &dyn Fn(&[f64]) -> f64, ) -> FrankWolfeResult

Run Frank-Wolfe minimisation.

  • grad_f: gradient oracle ∇f(x).
  • lmo: linear minimisation oracle: given d, return argmin_{s ∈ C} d·s.
  • f_val: function value oracle f(x).
  • x_init: feasible starting point.

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