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use crate::prelude::*;
use serde::{Deserialize, Serialize};
use std::default::Default;
#[derive(Serialize, Deserialize)]
pub struct GaussNewtonLS<L> {
linesearch: L,
}
impl<L> GaussNewtonLS<L> {
pub fn new(linesearch: L) -> Self {
GaussNewtonLS { linesearch }
}
}
impl<O, L> Solver<O> for GaussNewtonLS<L>
where
O: ArgminOp,
O::Param: Default
+ std::fmt::Debug
+ ArgminScaledSub<O::Param, f64, O::Param>
+ ArgminSub<O::Param, O::Param>
+ ArgminMul<f64, O::Param>,
O::Output: ArgminNorm<f64>,
O::Jacobian: ArgminTranspose
+ ArgminInv<O::Jacobian>
+ ArgminDot<O::Jacobian, O::Jacobian>
+ ArgminDot<O::Output, O::Param>
+ ArgminDot<O::Param, O::Param>,
O::Hessian: Default,
L: Clone + ArgminLineSearch<O::Param> + Solver<OpWrapper<LineSearchOP<O>>>,
{
const NAME: &'static str = "Gauss-Newton method with Linesearch";
fn next_iter(
&mut self,
op: &mut OpWrapper<O>,
state: &IterState<O>,
) -> Result<ArgminIterData<O>, Error> {
let param = state.get_param();
let residuals = op.apply(¶m)?;
let jacobian = op.jacobian(¶m)?;
let jacobian_t = jacobian.clone().t();
let grad = jacobian_t.dot(&residuals);
let p = jacobian_t.dot(&jacobian).inv()?.dot(&grad);
self.linesearch.set_search_direction(p.mul(&(-1.0)));
let line_op = OpWrapper::new_move(LineSearchOP { op: op.clone_op() });
let ArgminResult {
operator: line_op,
state:
IterState {
param: next_param,
cost: next_cost,
..
},
} = Executor::new(line_op, self.linesearch.clone(), param)
.grad(grad)
.cost(residuals.norm())
.ctrlc(false)
.run()?;
op.consume_op(line_op);
Ok(ArgminIterData::new().param(next_param).cost(next_cost))
}
fn terminate(&mut self, state: &IterState<O>) -> TerminationReason {
if (state.get_prev_cost() - state.get_cost()).abs() < std::f64::EPSILON.sqrt() {
return TerminationReason::NoChangeInCost;
}
TerminationReason::NotTerminated
}
}
#[doc(hidden)]
#[derive(Clone, Default, Serialize, Deserialize)]
pub struct LineSearchOP<O> {
op: O,
}
impl<O: ArgminOp> ArgminOp for LineSearchOP<O>
where
O::Jacobian: ArgminTranspose + ArgminDot<O::Output, O::Param>,
O::Output: ArgminNorm<f64>,
{
type Param = O::Param;
type Output = f64;
type Hessian = O::Hessian;
type Jacobian = O::Jacobian;
fn apply(&self, p: &Self::Param) -> Result<Self::Output, Error> {
Ok(self.op.apply(p)?.norm())
}
fn gradient(&self, p: &Self::Param) -> Result<Self::Param, Error> {
Ok(self.op.jacobian(p)?.t().dot(&self.op.apply(p)?))
}
fn hessian(&self, p: &Self::Param) -> Result<Self::Hessian, Error> {
self.op.hessian(p)
}
fn jacobian(&self, p: &Self::Param) -> Result<Self::Jacobian, Error> {
self.op.jacobian(p)
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::send_sync_test;
use crate::solver::linesearch::MoreThuenteLineSearch;
send_sync_test!(
gauss_newton_linesearch_method,
GaussNewtonLS<MoreThuenteLineSearch<Vec<f64>>>
);
}