src/sliding_fb.rs

/*!
Solver for the point source localisation problem using a sliding
forward-backward splitting method.
*/

use numeric_literals::replace_float_literals;
use serde::{Deserialize, Serialize};
//use colored::Colorize;
//use nalgebra::{DVector, DMatrix};
use itertools::izip;
use std::iter::Iterator;
use std::ops::MulAssign;

use crate::fb::*;
use crate::forward_model::{BoundedCurvature, BoundedCurvatureGuess};
use crate::measures::merging::SpikeMerging;
use crate::measures::{DeltaMeasure, DiscreteMeasure, Radon, RNDM};
use crate::plot::Plotter;
use crate::prox_penalty::{ProxPenalty, StepLengthBound};
use crate::regularisation::SlidingRegTerm;
use crate::types::*;
use alg_tools::error::DynResult;
use alg_tools::euclidean::Euclidean;
use alg_tools::iterate::AlgIteratorFactory;
use alg_tools::mapping::{DifferentiableMapping, DifferentiableRealMapping};
use alg_tools::nalgebra_support::ToNalgebraRealField;
use alg_tools::norms::Norm;
use anyhow::ensure;
use std::ops::ControlFlow;

/// Transport settings for [`pointsource_sliding_fb_reg`].
#[derive(Clone, Copy, Eq, PartialEq, Serialize, Deserialize, Debug)]
#[serde(default)]
pub struct TransportConfig<F: Float> {
    /// Transport step length $θ$ normalised to $(0, 1)$.
    pub θ0: F,
    /// Factor in $(0, 1)$ for decreasing transport to adapt to tolerance.
    pub adaptation: F,
    /// A posteriori transport tolerance multiplier (C_pos)
    pub tolerance_mult_con: F,
    /// maximum number of adaptation iterations, until cancelling transport.
    pub max_attempts: usize,
}

#[replace_float_literals(F::cast_from(literal))]
impl<F: Float> TransportConfig<F> {
    /// Check that the parameters are ok. Panics if not.
    pub fn check(&self) -> DynResult<()> {
        ensure!(self.θ0 > 0.0);
        ensure!(0.0 < self.adaptation && self.adaptation < 1.0);
        ensure!(self.tolerance_mult_con > 0.0);
        Ok(())
    }
}

#[replace_float_literals(F::cast_from(literal))]
impl<F: Float> Default for TransportConfig<F> {
    fn default() -> Self {
        TransportConfig { θ0: 0.9, adaptation: 0.9, tolerance_mult_con: 100.0, max_attempts: 2 }
    }
}

/// Settings for [`pointsource_sliding_fb_reg`].
#[derive(Clone, Copy, Eq, PartialEq, Serialize, Deserialize, Debug)]
#[serde(default)]
pub struct SlidingFBConfig<F: Float> {
    /// Step length scaling
    pub τ0: F,
    // Auxiliary variable step length scaling for [`crate::sliding_pdps::pointsource_sliding_fb_pair`]
    pub σp0: F,
    /// Transport parameters
    pub transport: TransportConfig<F>,
    /// Generic parameters
    pub insertion: InsertionConfig<F>,
    /// Guess for curvature bound calculations.
    pub guess: BoundedCurvatureGuess,
}

#[replace_float_literals(F::cast_from(literal))]
impl<F: Float> Default for SlidingFBConfig<F> {
    fn default() -> Self {
        SlidingFBConfig {
            τ0: 0.99,
            σp0: 0.99,
            transport: Default::default(),
            insertion: Default::default(),
            guess: BoundedCurvatureGuess::BetterThanZero,
        }
    }
}

/// Internal type of adaptive transport step length calculation
pub(crate) enum TransportStepLength<F: Float, G: Fn(F, F) -> F> {
    /// Fixed, known step length
    #[allow(dead_code)]
    Fixed(F),
    /// Adaptive step length, only wrt. maximum transport.
    /// Content of `l` depends on use case, while `g` calculates the step length from `l`.
    AdaptiveMax { l: F, max_transport: F, g: G },
    /// Adaptive step length.
    /// Content of `l` depends on use case, while `g` calculates the step length from `l`.
    FullyAdaptive { l: F, max_transport: F, g: G },
}

#[derive(Clone, Debug, Serialize)]
pub struct SingleTransport<const N: usize, F: Float> {
    /// Source point
    x: Loc<N, F>,
    /// Target point
    y: Loc<N, F>,
    /// Original mass
    α_μ_orig: F,
    /// Transported mass
    α_γ: F,
    /// Helper for pruning
    prune: bool,
}

#[derive(Clone, Debug, Serialize)]
pub struct Transport<const N: usize, F: Float> {
    vec: Vec<SingleTransport<N, F>>,
}

/// Whether partiall transported points are allowed.
///
/// Partial transport can cause spike count explosion, so full or zero
/// transport is generally preferred. If this is set to `true`, different
/// transport adaptation heuristics will be used.
const ALLOW_PARTIAL_TRANSPORT: bool = true;
const MINIMAL_PARTIAL_TRANSPORT: bool = true;

impl<const N: usize, F: Float> Transport<N, F> {
    pub(crate) fn new() -> Self {
        Transport { vec: Vec::new() }
    }

    pub(crate) fn iter(&self) -> impl Iterator<Item = &'_ SingleTransport<N, F>> {
        self.vec.iter()
    }

    pub(crate) fn iter_mut(&mut self) -> impl Iterator<Item = &'_ mut SingleTransport<N, F>> {
        self.vec.iter_mut()
    }

    pub(crate) fn extend<I>(&mut self, it: I)
    where
        I: IntoIterator<Item = SingleTransport<N, F>>,
    {
        self.vec.extend(it)
    }

    pub(crate) fn len(&self) -> usize {
        self.vec.len()
    }

    // pub(crate) fn dist_matching(&self, μ: &RNDM<N, F>) -> F {
    //     self.iter()
    //         .zip(μ.iter_spikes())
    //         .map(|(ρ, δ)| (ρ.α_γ - δ.α).abs())
    //         .sum()
    // }

    /// Construct `μ̆`, replacing the contents of `μ`.
    #[replace_float_literals(F::cast_from(literal))]
    pub(crate) fn μ̆_into(&self, μ: &mut RNDM<N, F>) {
        assert!(self.len() <= μ.len());

        // First transported points
        for (δ, ρ) in izip!(μ.iter_spikes_mut(), self.iter()) {
            if ρ.α_γ.abs() > 0.0 {
                // Transport – transported point
                δ.α = ρ.α_γ;
                δ.x = ρ.y;
            } else {
                // No transport – original point
                δ.α = ρ.α_μ_orig;
                δ.x = ρ.x;
            }
        }

        // Then source points with partial transport
        let mut i = self.len();
        if ALLOW_PARTIAL_TRANSPORT {
            // This can cause the number of points to explode, so cannot have partial transport.
            for ρ in self.iter() {
                let α = ρ.α_μ_orig - ρ.α_γ;
                if ρ.α_γ.abs() > F::EPSILON && α != 0.0 {
                    let δ = DeltaMeasure { α, x: ρ.x };
                    if i < μ.len() {
                        μ[i] = δ;
                    } else {
                        μ.push(δ)
                    }
                    i += 1;
                }
            }
        }
        μ.truncate(i);
    }

    /// Constrution of initial transport `γ1` from initial measure `μ` and `v=F'(μ)`
    /// with step lengh τ and transport step length `θ_or_adaptive`.
    #[replace_float_literals(F::cast_from(literal))]
    pub(crate) fn initial_transport<G, D>(
        &mut self,
        μ: &RNDM<N, F>,
        _τ: F,
        τθ_or_adaptive: &mut TransportStepLength<F, G>,
        v: D,
    ) where
        G: Fn(F, F) -> F,
        D: DifferentiableRealMapping<N, F>,
    {
        use TransportStepLength::*;

        // Initialise transport structure weights
        for (δ, ρ) in izip!(μ.iter_spikes(), self.iter_mut()) {
            ρ.α_μ_orig = δ.α;
            ρ.x = δ.x;
            // If old transport has opposing sign, the new transport will be none.
            ρ.α_γ = if (ρ.α_γ > 0.0 && δ.α < 0.0) || (ρ.α_γ < 0.0 && δ.α > 0.0) {
                0.0
            } else {
                δ.α
            }
        }

        let γ_prev_len = self.len();
        assert!(μ.len() >= γ_prev_len);
        self.extend(μ[γ_prev_len..].iter().map(|δ| SingleTransport {
            x: δ.x,
            y: δ.x, // Just something, will be filled properly in the next phase
            α_μ_orig: δ.α,
            α_γ: δ.α,
            prune: false,
        }));

        // Calculate transport rays.
        match *τθ_or_adaptive {
            Fixed(θ) => {
                for ρ in self.iter_mut() {
                    ρ.y = ρ.x - v.differential(&ρ.x) * (ρ.α_γ.signum() * θ);
                }
            }
            AdaptiveMax { l: ℓ_F, ref mut max_transport, g: ref calculate_θτ } => {
                *max_transport = max_transport.max(self.norm(Radon));
                let θτ = calculate_θτ(ℓ_F, *max_transport);
                for ρ in self.iter_mut() {
                    ρ.y = ρ.x - v.differential(&ρ.x) * (ρ.α_γ.signum() * θτ);
                }
            }
            FullyAdaptive {
                l: ref mut adaptive_ℓ_F,
                ref mut max_transport,
                g: ref calculate_θτ,
            } => {
                *max_transport = max_transport.max(self.norm(Radon));
                let mut θτ = calculate_θτ(*adaptive_ℓ_F, *max_transport);
                // Do two runs through the spikes to update θ, breaking if first run did not cause
                // a change.
                for _i in 0..=1 {
                    let mut changes = false;
                    for ρ in self.iter_mut() {
                        let dv_x = v.differential(&ρ.x);
                        let g = &dv_x * (ρ.α_γ.signum() * θτ);
                        ρ.y = ρ.x - g;
                        let n = g.norm2();
                        if n >= F::EPSILON {
                            // Estimate Lipschitz factor of ∇v
                            let this_ℓ_F = (dv_x - v.differential(&ρ.y)).norm2() / n;
                            *adaptive_ℓ_F = adaptive_ℓ_F.max(this_ℓ_F);
                            θτ = calculate_θτ(*adaptive_ℓ_F, *max_transport);
                            changes = true
                        }
                    }
                    if !changes {
                        break;
                    }
                }
            }
        }
    }

    /// A posteriori transport adaptation.
    #[replace_float_literals(F::cast_from(literal))]
    pub(crate) fn aposteriori_transport<D>(
        &mut self,
        μ: &RNDM<N, F>,
        μ̆: &RNDM<N, F>,
        _v: &mut D,
        extra: Option<F>,
        ε: F,
        tconfig: &TransportConfig<F>,
        attempts: &mut usize,
    ) -> bool
    where
        D: DifferentiableRealMapping<N, F>,
    {
        *attempts += 1;

        // 1. If π_♯^1γ^{k+1} = γ1 has non-zero mass at some point y, but μ = μ^{k+1} does not,
        // then the ansatz ∇w̃_x(y) = w^{k+1}(y) may not be satisfied. So set the mass of γ1
        // at that point to zero, and retry.
        let mut all_ok = true;
        for (δ, ρ) in izip!(μ.iter_spikes(), self.iter_mut()) {
            if δ.α == 0.0 && ρ.α_γ != 0.0 {
                all_ok = false;
                ρ.α_γ = 0.0;
            }
        }

        // 2. Through bounding ∫ B_ω(y, z) dλ(x, y, z).
        //    through the estimate ≤ C ‖Δ‖‖γ^{k+1}‖ for Δ := μ^{k+1}-μ̆^k
        //    which holds for some some C if the convolution kernel in 𝒟 has Lipschitz gradient.
        let nγ = self.norm(Radon);
        let nΔ = μ.dist_matching(&μ̆) + extra.unwrap_or(0.0);
        let t = ε * tconfig.tolerance_mult_con;
        if nγ * nΔ > t && *attempts >= tconfig.max_attempts {
            all_ok = false;
        } else if nγ * nΔ > t {
            // Since t/(nγ*nΔ)<1, and the constant tconfig.adaptation < 1,
            // this will guarantee that eventually ‖γ‖ decreases sufficiently that we
            // will not enter here.
            //*self *= tconfig.adaptation * t / (nγ * nΔ);

            // We want a consistent behaviour that has the potential to set many weights to zero.
            // Therefore, we find the smallest uniform reduction `chg_one`, subtracted
            // from all weights, that achieves total `adapt` adaptation.
            let adapt_to = tconfig.adaptation * t / nΔ;
            let reduction_target = nγ - adapt_to;
            assert!(reduction_target > 0.0);
            if ALLOW_PARTIAL_TRANSPORT {
                if MINIMAL_PARTIAL_TRANSPORT {
                    // This reduces weights of transport, starting from … until `adapt` is
                    // exhausted. It will, therefore, only ever cause one extrap point insertion
                    // at the sources, unlike “full” partial transport.
                    //let refs = self.vec.iter_mut().collect::<Vec<_>>();
                    //refs.sort_by(|ρ1, ρ2| ρ1.α_γ.abs().partial_cmp(&ρ2.α_γ.abs()).unwrap());
                    // let mut it = refs.into_iter();
                    //
                    // Maybe sort by differential norm
                    // let mut refs = self
                    //     .vec
                    //     .iter_mut()
                    //     .map(|ρ| {
                    //         let val = v.differential(&ρ.x).norm2_squared();
                    //         (ρ, val)
                    //     })
                    //     .collect::<Vec<_>>();
                    // refs.sort_by(|(_, v1), (_, v2)| v2.partial_cmp(&v1).unwrap());
                    // let mut it = refs.into_iter().map(|(ρ, _)| ρ);
                    let mut it = self.vec.iter_mut().rev();
                    let _unused = it.try_fold(reduction_target, |left, ρ| {
                        let w = ρ.α_γ.abs();
                        if left <= w {
                            ρ.α_γ = ρ.α_γ.signum() * (w - left);
                            ControlFlow::Break(())
                        } else {
                            ρ.α_γ = 0.0;
                            ControlFlow::Continue(left - w)
                        }
                    });
                } else {
                    // This version equally reduces all weights. It causes partial transport, which
                    // has the problem that that we need to then adapt weights in both start and
                    // end points, in insert_and_reweigh, somtimes causing the number of spikes μ
                    // to explode.
                    let mut abs_weights = self
                        .vec
                        .iter()
                        .map(|ρ| ρ.α_γ.abs())
                        .filter(|t| *t > F::EPSILON)
                        .collect::<Vec<F>>();
                    abs_weights.sort_by(|a, b| a.partial_cmp(b).unwrap());
                    let n = abs_weights.len();
                    // Cannot have partial transport; can cause spike count explosion
                    let chg = abs_weights.into_iter().zip((1..=n).rev()).try_fold(
                        0.0,
                        |smaller_total, (w, m)| {
                            let mf = F::cast_from(m);
                            let reduction = w * mf + smaller_total;
                            if reduction >= reduction_target {
                                ControlFlow::Break((reduction_target - smaller_total) / mf)
                            } else {
                                ControlFlow::Continue(smaller_total + w)
                            }
                        },
                    );
                    match chg {
                        ControlFlow::Continue(_) => self.vec.iter_mut().for_each(|δ| δ.α_γ = 0.0),
                        ControlFlow::Break(chg_one) => self.vec.iter_mut().for_each(|ρ| {
                            let t = ρ.α_γ.abs();
                            if t > 0.0 {
                                if ALLOW_PARTIAL_TRANSPORT {
                                    let new = (t - chg_one).max(0.0);
                                    ρ.α_γ = ρ.α_γ.signum() * new;
                                }
                            }
                        }),
                    }
                }
            } else {
                // This version zeroes smallest weights, avoiding partial transport.
                let mut abs_weights_idx = self
                    .vec
                    .iter()
                    .map(|ρ| ρ.α_γ.abs())
                    .zip(0..)
                    .filter(|(w, _)| *w >= 0.0)
                    .collect::<Vec<(F, usize)>>();
                abs_weights_idx.sort_by(|(a, _), (b, _)| a.partial_cmp(b).unwrap());

                let mut left = reduction_target;

                for (w, i) in abs_weights_idx {
                    left -= w;
                    let ρ = &mut self.vec[i];
                    ρ.α_γ = 0.0;
                    if left < 0.0 {
                        break;
                    }
                }
            }

            all_ok = false
        }

        if !all_ok && *attempts >= tconfig.max_attempts {
            for ρ in self.iter_mut() {
                ρ.α_γ = 0.0;
            }
        }

        all_ok
    }

    /// Returns $‖μ\^k - π\_♯\^0γ\^{k+1}‖$
    pub(crate) fn μ0_minus_γ0_radon(&self) -> F {
        self.vec.iter().map(|ρ| (ρ.α_μ_orig - ρ.α_γ).abs()).sum()
    }

    /// Returns $∫ c_2 d|γ|$
    #[replace_float_literals(F::cast_from(literal))]
    pub(crate) fn c2integral(&self) -> F {
        self.vec
            .iter()
            .map(|ρ| ρ.y.dist2_squared(&ρ.x) / 2.0 * ρ.α_γ.abs())
            .sum()
    }

    #[replace_float_literals(F::cast_from(literal))]
    pub(crate) fn get_transport_stats(&self, stats: &mut IterInfo<F>, μ: &RNDM<N, F>) {
        // TODO: This doesn't take into account μ[i].α becoming zero in the latest tranport
        // attempt, for i < self.len(), when a corresponding source term also exists with index
        // j ≥ self.len(). For now, we let that be reflected in the prune count.
        stats.inserted += μ.len() - self.len();

        let transp = stats.get_transport_mut();

        transp.dist = {
            let (a, b) = transp.dist;
            (a + self.c2integral(), b + self.norm(Radon))
        };
        transp.untransported_fraction = {
            let (a, b) = transp.untransported_fraction;
            let source = self.iter().map(|ρ| ρ.α_μ_orig.abs()).sum();
            (a + self.μ0_minus_γ0_radon(), b + source)
        };
        transp.transport_error = {
            let (a, b) = transp.transport_error;
            //(a + self.dist_matching(&μ), b + self.norm(Radon))

            // This ignores points that have been not transported at all, to only calculate
            // destnation error; untransported_fraction accounts for not being able to transport
            // at all.
            self.iter()
                .zip(μ.iter_spikes())
                .fold((a, b), |(a, b), (ρ, δ)| {
                    let transported = ρ.α_γ.abs();
                    if transported > F::EPSILON {
                        (a + (ρ.α_γ - δ.α).abs(), b + transported)
                    } else {
                        (a, b)
                    }
                })
        };
    }

    /// Prune spikes with zero weight. To maintain correct ordering between μ and γ, also the
    /// latter needs to be pruned when μ is.
    pub(crate) fn prune_compat(&mut self, μ: &mut RNDM<N, F>, stats: &mut IterInfo<F>) {
        assert!(self.vec.len() <= μ.len());
        let old_len = μ.len();
        for (ρ, δ) in self.vec.iter_mut().zip(μ.iter_spikes()) {
            ρ.prune = !(δ.α.abs() > F::EPSILON);
        }
        μ.prune_by(|δ| δ.α.abs() > F::EPSILON);
        stats.pruned += old_len - μ.len();
        self.vec.retain(|ρ| !ρ.prune);
        assert!(self.vec.len() <= μ.len());
    }
}

impl<const N: usize, F: Float> Norm<Radon, F> for Transport<N, F> {
    fn norm(&self, _: Radon) -> F {
        self.iter().map(|ρ| ρ.α_γ.abs()).sum()
    }
}

impl<const N: usize, F: Float> MulAssign<F> for Transport<N, F> {
    fn mul_assign(&mut self, factor: F) {
        for ρ in self.iter_mut() {
            ρ.α_γ *= factor;
        }
    }
}

/// Iteratively solve the pointsource localisation problem using sliding forward-backward
/// splitting
///
/// The parametrisation is as for [`pointsource_fb_reg`].
/// Inertia is currently not supported.
#[replace_float_literals(F::cast_from(literal))]
pub fn pointsource_sliding_fb_reg<F, I, Dat, Reg, Plot, P, const N: usize>(
    f: &Dat,
    reg: &Reg,
    prox_penalty: &P,
    config: &SlidingFBConfig<F>,
    iterator: I,
    mut plotter: Plot,
    μ0: Option<RNDM<N, F>>,
) -> DynResult<RNDM<N, F>>
where
    F: Float + ToNalgebraRealField,
    I: AlgIteratorFactory<IterInfo<F>>,
    Dat: DifferentiableMapping<RNDM<N, F>, Codomain = F> + BoundedCurvature<F>,
    Dat::DerivativeDomain: DifferentiableRealMapping<N, F> + ClosedMul<F>,
    //for<'a> Dat::Differential<'a>: Lipschitz<&'a P, FloatType = F>,
    RNDM<N, F>: SpikeMerging<F>,
    Reg: SlidingRegTerm<Loc<N, F>, F>,
    P: ProxPenalty<Loc<N, F>, Dat::DerivativeDomain, Reg, F> + StepLengthBound<F, Dat>,
    Plot: Plotter<P::ReturnMapping, Dat::DerivativeDomain, RNDM<N, F>>,
{
    // Check parameters
    ensure!(config.τ0 > 0.0, "Invalid step length parameter");
    config.transport.check()?;

    // Initialise iterates
    let mut μ = μ0.unwrap_or_else(|| DiscreteMeasure::new());
    let mut γ = Transport::new();

    // Set up parameters
    // let opAnorm = opA.opnorm_bound(Radon, L2);
    //let max_transport = config.max_transport.scale
    //                    * reg.radon_norm_bound(b.norm2_squared() / 2.0);
    //let ℓ = opA.transport.lipschitz_factor(L2Squared) * max_transport;
    let ℓ = 0.0;
    let τ = config.τ0 / prox_penalty.step_length_bound(&f)?;

    let mut θ_or_adaptive = match f.curvature_bound_components(config.guess) {
        (_, Err(_)) => TransportStepLength::Fixed(config.transport.θ0),
        (maybe_ℓ_F, Ok(transport_lip)) => {
            let calculate_θτ = move |ℓ_F, max_transport| {
                let ℓ_r = transport_lip * max_transport;
                config.transport.θ0 / (ℓ + ℓ_F + ℓ_r)
            };
            match maybe_ℓ_F {
                Ok(ℓ_F) => TransportStepLength::AdaptiveMax {
                    l: ℓ_F, // TODO: could estimate computing the real reesidual
                    max_transport: 0.0,
                    g: calculate_θτ,
                },
                Err(_) => TransportStepLength::FullyAdaptive {
                    l: 10.0 * F::EPSILON, // Start with something very small to estimate differentials
                    max_transport: 0.0,
                    g: calculate_θτ,
                },
            }
        }
    };
    // We multiply tolerance by τ for FB since our subproblems depending on tolerances are scaled
    // by τ compared to the conditional gradient approach.
    let tolerance = config.insertion.tolerance * τ * reg.tolerance_scaling();
    let mut ε = tolerance.initial();

    // Statistics
    let full_stats = |μ: &RNDM<N, F>, ε, stats| IterInfo {
        value: f.apply(μ) + reg.apply(μ),
        n_spikes: μ.len(),
        ε,
        // postprocessing: config.insertion.postprocessing.then(|| μ.clone()),
        ..stats
    };
    let mut stats = IterInfo::new();

    // Run the algorithm
    for state in iterator.iter_init(|| full_stats(&μ, ε, stats.clone())) {
        // Calculate initial transport
        let v = f.differential(&μ);
        γ.initial_transport(&μ, τ, &mut θ_or_adaptive, v);

        let mut attempts = 0;

        // Solve finite-dimensional subproblem several times until the dual variable for the
        // regularisation term conforms to the assumptions made for the transport above.
        let (maybe_d, _within_tolerances, mut τv̆, μ̆) = 'adapt_transport: loop {
            // Set initial guess for μ=μ^{k+1}.
            γ.μ̆_into(&mut μ);
            let μ̆ = μ.clone();

            // Calculate τv̆ = τA_*(A[μ_transported + μ_transported_base]-b)
            //let residual_μ̆ = calculate_residual2(&γ1, &μ0_minus_γ0, opA, b);
            // TODO: this could be optimised by doing the differential like the
            // old residual2.
            // NOTE: This assumes that μ = γ1
            let mut τv̆ = f.differential(&μ̆) * τ;

            // Construct μ^{k+1} by solving finite-dimensional subproblems and insert new spikes.
            let (maybe_d, within_tolerances) = prox_penalty.insert_and_reweigh(
                &mut μ,
                &mut τv̆,
                τ,
                ε,
                &config.insertion,
                &reg,
                &state,
                &mut stats,
            )?;

            // A posteriori transport adaptation.
            if γ.aposteriori_transport(&μ, &μ̆, &mut τv̆, None, ε, &config.transport, &mut attempts)
            {
                break 'adapt_transport (maybe_d, within_tolerances, τv̆, μ̆);
            }

            stats.get_transport_mut().readjustment_iters += 1;
        };

        γ.get_transport_stats(&mut stats, &μ);

        // Merge spikes.
        // This crucially expects the merge routine to be stable with respect to spike locations,
        // and not to performing any pruning. That is be to done below simultaneously for γ.
        if config.insertion.merge_now(&state) {
            stats.merged += prox_penalty.merge_spikes(
                &mut μ,
                &mut τv̆,
                &μ̆,
                τ,
                ε,
                &config.insertion,
                &reg,
                Some(|μ̃: &RNDM<N, F>| f.apply(μ̃)),
            );
        }

        γ.prune_compat(&mut μ, &mut stats);

        let iter = state.iteration();
        stats.this_iters += 1;

        // Give statistics if requested
        state.if_verbose(|| {
            plotter.plot_spikes(iter, maybe_d.as_ref(), Some(&τv̆), &μ);
            full_stats(&μ, ε, std::mem::replace(&mut stats, IterInfo::new()))
        });

        // Update main tolerance for next iteration
        ε = tolerance.update(ε, iter);
    }

    //postprocess(μ, &config.insertion, f)
    postprocess(μ, &config.insertion, |μ̃| f.apply(μ̃))
}