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/*!
Iterative algorithms for solving the finite-dimensional subproblem with constraint.
*/

use nalgebra::DVector;
use numeric_literals::replace_float_literals;
use itertools::izip;
//use std::iter::zip;
use std::cmp::Ordering::*;

use alg_tools::iterate::{
    AlgIteratorFactory,
    AlgIteratorState,
};
use alg_tools::nalgebra_support::ToNalgebraRealField;
use alg_tools::norms::{Dist, L1};
use alg_tools::nanleast::NaNLeast;

use crate::types::*;
use super::{
    InnerMethod,
    InnerSettings
};
use super::nonneg::nonneg_soft_thresholding;
use super::l1squared_unconstrained::l1squared_prox;

/// Return maximum of `dist` and distnce of inteval `[lb, ub]` to zero.
#[replace_float_literals(F::cast_from(literal))]
pub(super) fn max_interval_dist_to_zero<F : Float>(dist  : F, lb : F, ub : F) -> F {
    if lb < 0.0 {
        if ub > 0.0 {
            dist
        } else {
            dist.max(-ub)
        }
    } else /* ub ≥ 0.0*/ {
        dist.max(lb)
    }
}

/// Returns the ∞-norm minimal subdifferential of $x ↦ (β/2)|x-y|_1^2 - g^⊤ x + λ\|x\|₁ +δ_{≥}(x)$ at $x$.
///
/// `v` will be modified and cannot be trusted to contain useful values afterwards.
#[replace_float_literals(F::cast_from(literal))]
fn min_subdifferential<F : Float + nalgebra::RealField>(
    y : &DVector<F>,
    x : &DVector<F>,
    g : &DVector<F>,
    λ : F,
    β : F
) -> F {
    let mut val = 0.0;
    let tmp = β*y.dist(x, L1);
    for (&g_i, &x_i, y_i) in izip!(g.iter(), x.iter(), y.iter()) {
        let (mut lb, mut ub) = (-g_i, -g_i);
        match x_i.partial_cmp(y_i) {
            Some(Greater) => { lb += tmp; ub += tmp },
            Some(Less) => { lb -= tmp; ub -= tmp },
            Some(Equal) => { lb -= tmp; ub += tmp },
            None => {},
        }
        match x_i.partial_cmp(&0.0) {
            Some(Greater) => { lb += λ; ub += λ },
            // Less should not happen
            Some(Less|Equal) => { lb = F::NEG_INFINITY; ub += λ },
            None => {},
        };
        val = max_interval_dist_to_zero(val, lb, ub);
    }
    val
}

#[replace_float_literals(F::cast_from(literal))]
fn lbd_soft_thresholding<F : Float>(v : F, λ : F, b : F) -> F
{
    match (b >= 0.0, v >= b) {
        (true, false)  => b,
        (true, true)   => b.max(v - λ),         // soft-to-b from above
        (false, true)  => super::unconstrained::soft_thresholding(v, λ),
        (false, false)  => 0.0.min(b.max(v - λ)), // soft-to-0 with lower bound
    }
}

/// Calculate $prox_f(x)$ for $f(x)=\frac{β}{2}\norm{x-y}_1^2 + δ_{≥0}(x)$.
///
/// To derive an algorithm for this, we can use
/// $prox_f(x) = prox_{f_0}(x - y) - y$ for
/// $f_0(z)=\frac{β}{2}\norm{z}_1^2 + δ_{≥-y}(z)$.
/// Now, the optimality conditions for $w = prox_{f_0}(x)$ are
/// $$\tag{*}
///     x ∈ w + β\norm{w}_1\sign w + N_{≥ -y}(w).
/// $$
/// If we know $\norm{w}_1$, then this is easily solved by lower-bounded soft-thresholding.
/// We find this by sorting the elements by the distance to the 'locked' lower-bounded
/// soft-thresholding target ($0$ or $-y_i$).
/// Then we loop over this sorted vector, increasing our estimate of $\norm{w}_1$ as we decide
/// that the soft-thresholding parameter `β\norm{w}_1` has to be such that the passed elements
/// will reach their locked value (after which they cannot change anymore, for a larger
/// soft-thresholding parameter. This has to be slightly more fine-grained for account
/// for the case that $-y_i<0$ and $x_i < -y_i$.
///
/// Indeed, we denote by x' and w' the subset of elements such that w_i ≠ 0 and w_i > -y_i,
/// we can calculate by applying $⟨\cdot, \sign w'⟩$ to the corresponding lines of (*) that
/// $$
///     \norm{x'} = \norm{w'} + β \norm{w}_1 m.
/// $$
/// Having a value for $t = \norm{w}-\norm{w'}$, we can then calculate
/// $$
///     \norm{x'} - t = (1+β m)\norm{w}_1,
/// $$
/// from where we can calculate the soft-thresholding parameter $λ=β\norm{w}_1$.
/// Since we do not actually know the unlocked elements, but just loop over all the possibilities
/// for them, we have to check that $λ$ is above the current lower bound for this parameter
/// (`shift` in the code), and below a value that would cause changes in the locked set
/// (`max_shift` in the code).
#[replace_float_literals(F::cast_from(literal))]
pub(super) fn l1squared_nonneg_prox<F :Float + nalgebra::RealField>(
    sorted : &mut Vec<(F, F, F, Option<(F, F)>)>,
    x : &mut DVector<F>,
    y : &DVector<F>,
    β : F
) {
    // nalgebra double-definition bullshit workaround
    //let max = alg_tools::NumTraitsFloat::max;
    let abs = alg_tools::NumTraitsFloat::abs;

    *x -= y;

    for (az_x_i, &x_i, &y_i) in izip!(sorted.iter_mut(), x.iter(), y.iter()) {
        // The first component of each az_x_i contains the distance of x_i to the
        // soft-thresholding limit. If it is negative, it is always reached.
        // The second component contains the absolute value of the result for that component
        // w_i of the solution, if the soft-thresholding limit is reached.
        // This is stored here due to the sorting, although could otherwise be computed directly.
        // Likewise the third component contains the absolute value of x_i.
        // The fourth component contains an initial lower bound.
        let a_i = abs(x_i);
        let b = -y_i;
        *az_x_i = match (b >= 0.0, x_i >= b) {
            (true, false)  => (x_i-b, b, a_i, None),  // w_i=b, so sorting element negative!
            (true, true)   => (x_i-b, b, a_i, None),  // soft-to-b from above
            (false, true)  => (a_i, 0.0, a_i, None),  // soft-to-0
            (false, false) => (a_i, 0.0, a_i, Some((b, b-x_i))),   // soft-to-0 with initial limit
        };
    }
    sorted.as_mut_slice()
          .sort_unstable_by(|(a, _, _, _), (b, _, _, _)| NaNLeast(*a).cmp(&NaNLeast(*b)));

    let mut nwlow = 0.0;
    let mut shift = 0.0;
    // This main loop is over different combinations of elements of the solution locked
    // to the soft-thresholding lower bound (`0` or `-y_i`), in the sorted order of locking.
    for (i, az_x_i) in izip!(0.., sorted.iter()) {
        // This 'attempt is over different combinations of elements locked to the
        // lower bound (`-y_i ≤ 0`). It calculates `max_shift` as the maximum shift that
        // can be done until the locking would change (or become non-strictly-complementary).
        // If the main rule (*) gives an estimate of `λ` that stays below `max_shift`, it is
        // accepted. Otherwise `shift` is updated to `max_shift`, and we attempt again,
        // with the non-locking set that participates in the calculation of `λ` then including
        // the elements that are no longer locked to the lower bound.
        'attempt: loop {
            let mut nwthis = 0.0; // contribution to ‖w‖ from elements with locking
                                  //soft-thresholding parameter = `shift`
            let mut nxmore = 0.0; // ‖x'‖ for those elements through to not be locked to
                                  // neither the soft-thresholding limit, nor the lower bound
            let mut nwlbd = 0.0;  // contribution to ‖w‖ from those elements locked to their
                                  // lower bound
            let mut m = 0;
            let mut max_shift = F::INFINITY; // maximal shift for which our estimate of the set of
                                             // unlocked elements is valid.
            let mut max_shift_from_lbd = false; // Whether max_shift comes from the next element
                                                // or from a lower bound being reached.
            for az_x_j in sorted[i as usize..].iter() {
                if az_x_j.0 <= shift {
                    nwthis += az_x_j.1;
                } else {
                    match az_x_j.3 {
                        Some((l, s)) if shift < s => {
                            if max_shift > s {
                                max_shift_from_lbd = true;
                                max_shift = s;
                            }
                            nwlbd += -l;
                        },
                        _ => {
                            nxmore += az_x_j.2;
                            if m == 0 && max_shift > az_x_j.0 {
                                max_shift = az_x_j.0;
                                max_shift_from_lbd = false;
                            }
                            m += 1;
                        }
                    }
                }
            }

            // We need ‖x'‖ = ‖w'‖ + β m ‖w‖, i.e. ‖x'‖ - (‖w‖-‖w'‖)= (1 + β m)‖w‖.
            let tmp = β*(nxmore - (nwlow + nwthis + nwlbd))/(1.0 + β*F::cast_from(m));
            if tmp > max_shift {
                if max_shift_from_lbd {
                    shift = max_shift;
                    continue 'attempt;
                } else {
                    break 'attempt
                }
            } else if tmp < shift {
                // TODO: this should probably be an assert!(false)
                break 'attempt;
            } else {
                // success
                x.zip_apply(y, |x_i, y_i| *x_i = y_i + lbd_soft_thresholding(*x_i, tmp, -y_i));
                return
            }
        }
        shift = az_x_i.0;
        nwlow += az_x_i.1;
    }
    // TODO: this fallback has not been verified to be correct
    x.zip_apply(y, |x_i, y_i| *x_i = y_i + lbd_soft_thresholding(*x_i, shift, -y_i));
}

/// Proximal point method implementation of [`l1squared_nonneg`].
/// For detailed documentation of the inputs and outputs, refer to there.
///
/// The `λ` component of the model is handled in the proximal step instead of the gradient step
/// for potential performance improvements.
#[replace_float_literals(F::cast_from(literal).to_nalgebra_mixed())]
pub fn l1squared_nonneg_pp<F, I>(
    y : &DVector<F::MixedType>,
    g : &DVector<F::MixedType>,
    λ_ : F,
    β_ : F,
    x : &mut DVector<F::MixedType>,
    τ_ : F,
    θ_ : F,
    iterator : I
) -> usize
where F : Float + ToNalgebraRealField,
      I : AlgIteratorFactory<F>
{
    let λ = λ_.to_nalgebra_mixed();
    let β = β_.to_nalgebra_mixed();
    let mut τ = τ_.to_nalgebra_mixed();
    let θ = θ_.to_nalgebra_mixed();
    let mut tmp = std::iter::repeat((0.0, 0.0, 0.0, None)).take(x.len()).collect();
    let mut iters = 0;

    iterator.iterate(|state| {
        // Primal step: x^{k+1} = prox_{(τβ/2)|.-y|_1^2+δ_{≥0}+}(x^k - τ(λ𝟙^⊤-g))
        x.apply(|x_i| *x_i -= τ*λ);
        x.axpy(τ, g, 1.0);
        l1squared_nonneg_prox(&mut tmp, x, y, τ*β);
        
        iters += 1;
        // This gives O(1/N^2) rates due to monotonicity of function values.
        // Higher acceleration does not seem to be numerically stable.
        τ += θ;

        // This gives O(1/N^3) rates due to monotonicity of function values.
        // Higher acceleration does not seem to be numerically stable.
        //τ + = F::cast_from(iters).to_nalgebra_mixed()*θ;

        state.if_verbose(|| {
            F::from_nalgebra_mixed(min_subdifferential(y, x, g, λ, β))
        })
    });

    iters
}

/// PDPS implementation of [`l1squared_nonneg`].
/// For detailed documentation of the inputs and outputs, refer to there.
///
/// The `λ` component of the model is handled in the proximal step instead of the gradient step
/// for potential performance improvements.
/// The parameter `θ` is used to multiply the rescale the operator (identity) of the PDPS model.
#[replace_float_literals(F::cast_from(literal).to_nalgebra_mixed())]
pub fn l1squared_nonneg_pdps<F, I>(
    y : &DVector<F::MixedType>,
    g : &DVector<F::MixedType>,
    λ_ : F,
    β_ : F,
    x : &mut DVector<F::MixedType>,
    τ_ : F,
    σ_ : F,
    θ_ : F,
    iterator : I
) -> usize
where F : Float + ToNalgebraRealField,
      I : AlgIteratorFactory<F>
{
    let λ = λ_.to_nalgebra_mixed();
    let β = β_.to_nalgebra_mixed();
    let τ = τ_.to_nalgebra_mixed();
    let σ = σ_.to_nalgebra_mixed();
    let θ = θ_.to_nalgebra_mixed();
    let mut w = DVector::zeros(x.len());
    let mut tmp = DVector::zeros(x.len());
    let mut xprev = x.clone();
    let mut iters = 0;

    iterator.iterate(|state| {
        // Primal step: x^{k+1} = prox_{(τβ/2)|.-y|_1^2}(x^k - τ (w^k - g))
        x.axpy(-τ*θ, &w, 1.0);
        x.axpy(τ, g, 1.0);
        l1squared_prox(&mut tmp, x, y, τ*β);
        
        // Dual step: w^{k+1} = proj_{[-∞,λ]}(w^k + σ(2x^{k+1}-x^k))
        w.axpy(2.0*σ*θ, x, 1.0);
        w.axpy(-σ*θ, &xprev, 1.0);
        w.apply(|w_i| *w_i = w_i.min(λ));
        xprev.copy_from(x);
        
        iters +=1;

        state.if_verbose(|| {
            F::from_nalgebra_mixed(min_subdifferential(y, x, g, λ, β))
        })
    });

    iters
}

/// Alternative PDPS implementation of [`l1squared_nonneg`].
/// For detailed documentation of the inputs and outputs, refer to there.
///
/// The `λ` component of the model is handled in the proximal step instead of the gradient step
/// for potential performance improvements.
/// The parameter `θ` is used to multiply the rescale the operator (identity) of the PDPS model.
#[replace_float_literals(F::cast_from(literal).to_nalgebra_mixed())]
pub fn l1squared_nonneg_pdps_alt<F, I>(
    y : &DVector<F::MixedType>,
    g : &DVector<F::MixedType>,
    λ_ : F,
    β_ : F,
    x : &mut DVector<F::MixedType>,
    τ_ : F,
    σ_ : F,
    θ_ : F,
    iterator : I
) -> usize
where F : Float + ToNalgebraRealField,
      I : AlgIteratorFactory<F>
{
    let λ = λ_.to_nalgebra_mixed();
    let τ = τ_.to_nalgebra_mixed();
    let σ = σ_.to_nalgebra_mixed();
    let θ = θ_.to_nalgebra_mixed();
    let β = β_.to_nalgebra_mixed();
    let βdivθ = β / θ;
    let σθ = σ*θ;
    let τθ = τ*θ;
    let mut w = DVector::zeros(x.len());
    let mut tmp = DVector::zeros(x.len());
    let mut xprev = x.clone();
    let mut iters = 0;

    iterator.iterate(|state| {
        // Primal step: x^{k+1} = nonnegsoft_τλ(x^k - τ(θ w^k -g))
        x.axpy(-τθ, &w, 1.0);
        x.axpy(τ, g, 1.0);
        x.apply(|x_i| *x_i = nonneg_soft_thresholding(*x_i, τ*λ));
        
        // Dual step: w^{k+1} = prox_{σ[(β/2)‖./θ-y‖₁²]^*}(q) for q = w^k + σθ(2x^{k+1}-x^k)
        //                    = q - σ prox_{(β/(2σ))‖./θ-y‖₁²}(q/σ)
        //                    = q - (σθ) prox_{(β/(2σθ^2))‖.-y‖₁²}(q/(σθ))
        //                    = σθ(q/(σθ) - prox_{(β/(2σθ^2))‖.-y‖₁²}(q/(σθ))
        w /= σθ;
        w.axpy(2.0, x, 1.0);
        w.axpy(-1.0, &xprev, 1.0);
        xprev.copy_from(&w); // use xprev as temporary variable
        l1squared_prox(&mut tmp, &mut xprev, y, βdivθ/σθ);
        w -= &xprev;
        w *= σθ;
        xprev.copy_from(x);
        
        iters +=1;

        state.if_verbose(|| {
            F::from_nalgebra_mixed(min_subdifferential(y, x, g, λ, β))
        })
    });

    iters
}


/// This function applies an iterative method for the solution of the problem
/// <div>$$
///     \min_{x ∈ ℝ^n} \frac{β}{2} |x-y|_1^2 - g^⊤ x + λ\|x\|₁ + δ_{≥ 0}(x).
/// $$</div>
///
/// This function returns the number of iterations taken.
#[replace_float_literals(F::cast_from(literal))]
pub fn l1squared_nonneg<F, I>(
    y : &DVector<F::MixedType>,
    g : &DVector<F::MixedType>,
    λ : F,
    β : F,
    x : &mut DVector<F::MixedType>,
    inner : &InnerSettings<F>,
    iterator : I
) -> usize
where F : Float + ToNalgebraRealField,
      I : AlgIteratorFactory<F>
{
    match inner.method {
        InnerMethod::PDPS => {
            let inner_θ = 0.001;
            // Estimate of ‖K‖ for K=θ\Id.
            let normest = inner_θ;
            let (inner_τ, inner_σ) = (inner.pdps_τσ0.0 / normest, inner.pdps_τσ0.1 / normest);
            l1squared_nonneg_pdps_alt(y, g, λ, β, x, inner_τ, inner_σ, inner_θ, iterator)
        },
        InnerMethod::FB => {
            // The Lipschitz factor of ∇[x ↦ g^⊤ x + λ∑x]=g - λ𝟙 is FB is just a proximal point
            // method with on constraints on τ. We “accelerate” it by adding to τ the constant θ
            // on each iteration. Exponential growth does not seem stable.
            let inner_τ = inner.fb_τ0;
            let inner_θ = inner_τ;
            l1squared_nonneg_pp(y, g, λ, β, x, inner_τ, inner_θ, iterator)
        },
        other => unimplemented!("${other:?} is unimplemented"),
    }
}