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use crate::prelude::*;
use serde::{Deserialize, Serialize};
use std::default::Default;
#[derive(Clone, Serialize, Deserialize)]
pub struct Newton<F> {
gamma: F,
}
impl<F: ArgminFloat> Newton<F> {
pub fn new() -> Self {
Newton {
gamma: F::from_f64(1.0).unwrap(),
}
}
pub fn set_gamma(mut self, gamma: F) -> Result<Self, Error> {
if gamma <= F::from_f64(0.0).unwrap() || gamma > F::from_f64(1.0).unwrap() {
return Err(ArgminError::InvalidParameter {
text: "Newton: gamma must be in (0, 1].".to_string(),
}
.into());
}
self.gamma = gamma;
Ok(self)
}
}
impl<F: ArgminFloat> Default for Newton<F> {
fn default() -> Newton<F> {
Newton::new()
}
}
impl<O, F> Solver<O> for Newton<F>
where
O: ArgminOp<Float = F>,
O::Param: ArgminScaledSub<O::Param, O::Float, O::Param>,
O::Hessian: ArgminInv<O::Hessian> + ArgminDot<O::Param, O::Param>,
F: ArgminFloat,
{
const NAME: &'static str = "Newton method";
fn next_iter(
&mut self,
op: &mut OpWrapper<O>,
state: &IterState<O>,
) -> Result<ArgminIterData<O>, Error> {
let param = state.get_param();
let grad = op.gradient(¶m)?;
let hessian = op.hessian(¶m)?;
let new_param = param.scaled_sub(&self.gamma, &hessian.inv()?.dot(&grad));
Ok(ArgminIterData::new().param(new_param))
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::test_trait_impl;
test_trait_impl!(newton_method, Newton<f64>);
}