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```
```// Copyright 2018 Stefan Kroboth
//
// copied, modified, or distributed except according to those terms.

//!
//! TODO

use std;
use ndarray::{Array1, Array2};
use errors::*;
use prelude::*;
use operator::ArgminOperator;
use result::ArgminResult;
use termination::TerminationReason;

/// Conjugate Gradient method struct (duh)
/// Maximum number of iterations
max_iters: u64,
/// current state
}

/// Indicates the current state of the Conjugate Gradient algorithm
/// Reference to the operator
operator: &'a ArgminOperator<'a>,
/// Current parameter vector
param: Array1<f64>,
/// Conjugate vector
p: Array1<f64>,
/// Residual
r: Array1<f64>,
/// Current number of iteration
iter: u64,
/// Current l2 norm of difference
norm: f64,
}

pub fn new(
operator: &'a ArgminOperator<'a>,
param: Array1<f64>,
p: Array1<f64>,
r: Array1<f64>,
) -> Self {
operator: operator,
param: param,
p: p,
r: r,
iter: 0_u64,
norm: std::f64::NAN,
}
}
}

pub fn new() -> Self {
max_iters: std::u64::MAX,
state: None,
}
}

/// Set maximum number of iterations
pub fn max_iters(&mut self, max_iters: u64) -> &mut Self {
self.max_iters = max_iters;
self
}
}

type Parameter = Array1<f64>;
type CostValue = f64;
type Hessian = Array2<f64>;
type StartingPoints = Self::Parameter;
type ProblemDefinition = &'a ArgminOperator<'a>;

/// Initialize with a given problem and a starting point
fn init(
&mut self,
operator: Self::ProblemDefinition,
init_param: &Self::StartingPoints,
) -> Result<()> {
let mut r = operator.y - &operator.apply(init_param);
if !operator.operator.is_square() {
r = operator.apply_transpose(&r);
}
let p = r.clone();
operator,
init_param.clone(),
r,
p,
));
Ok(())
}

/// Compute next point
fn next_iter(&mut self) -> Result<ArgminResult<Self::Parameter, Self::CostValue>> {
let mut state = self.state.take().unwrap();
let mut ap = state.operator.apply(&state.p);
if !state.operator.operator.is_square() {
ap = state.operator.apply_transpose(&ap);
}
let rtr = state.r.iter().map(|a| a.powf(2.0)).sum::<f64>();
let alpha: f64 = rtr
/ state
.p
.iter()
.zip(ap.iter())
.map(|(a, b)| a * b)
.sum::<f64>();
state.param = state.param + alpha * &state.p;
state.r = state.r - alpha * &ap;
let beta: f64 = state.r.iter().map(|a| a.powf(2.0)).sum::<f64>() / rtr;
state.p = beta * &state.p + &state.r;
state.iter += 1;
state.norm = state.r.iter().map(|a| a.powf(2.0)).sum::<f64>().sqrt();
let mut out = ArgminResult::new(state.param.clone(), state.norm, state.iter);
self.state = Some(state);
out.set_termination_reason(self.terminate());
Ok(out)
}

/// Indicates whether any of the stopping criteria are met
make_terminate!(self,
self.state.as_ref().unwrap().iter >= self.max_iters, TerminationReason::MaxItersReached;
self.state.as_ref().unwrap().norm <= self.state.as_ref().unwrap().operator.target_cost, TerminationReason::TargetCostReached;
);

make_run!(
Self::ProblemDefinition,
Self::StartingPoints,
Self::Parameter,
Self::CostValue
);
}

fn default() -> Self {
Self::new()
}
}
```