pointsource_algs: comparison src/sliding

-:6105b5cd8d89
+:f0e8704d3f0e
+/*!
+Solver for the point source localisation problem using a sliding
+primal-dual proximal splitting method.
+*/
+use numeric_literals::replace_float_literals;
+use serde::{Deserialize, Serialize};
+//use colored::Colorize;
+//use nalgebra::{DVector, DMatrix};
+use std::iter::Iterator;
+use alg_tools::convex::{Conjugable, Prox};
+use alg_tools::direct_product::Pair;
+use alg_tools::euclidean::Euclidean;
+use alg_tools::iterate::AlgIteratorFactory;
+use alg_tools::linops::{Adjointable, BoundedLinear, IdOp, AXPY, GEMV};
+use alg_tools::mapping::{DifferentiableRealMapping, Instance, Mapping};
+use alg_tools::nalgebra_support::ToNalgebraRealField;
+use alg_tools::norms::{Dist, Norm};
+use alg_tools::norms::{PairNorm, L2};
+use crate::forward_model::{AdjointProductPairBoundedBy, BoundedCurvature, ForwardModel};
+use crate::measures::merging::SpikeMerging;
+use crate::measures::{DiscreteMeasure, Radon, RNDM};
+use crate::types::*;
+// use crate::transport::TransportLipschitz;
+//use crate::tolerance::Tolerance;
+use crate::fb::*;
+use crate::plot::{PlotLookup, Plotting, SeqPlotter};
+use crate::regularisation::SlidingRegTerm;
+// use crate::dataterm::L2Squared;
+use crate::dataterm::{calculate_residual, calculate_residual2};
+use crate::sliding_fb::{
+aposteriori_transport, initial_transport, TransportConfig, TransportStepLength,
+};
+/// Settings for [`pointsource_sliding_pdps_pair`].
+#[derive(Clone, Copy, Eq, PartialEq, Serialize, Deserialize, Debug)]
+#[serde(default)]
+pub struct SlidingPDPSConfig<F: Float> {
+/// Primal step length scaling.
+pub τ0: F,
+/// Primal step length scaling.
+pub σp0: F,
+/// Dual step length scaling.
+pub σd0: F,
+/// Transport parameters
+pub transport: TransportConfig<F>,
+/// Generic parameters
+pub insertion: FBGenericConfig<F>,
+}
+#[replace_float_literals(F::cast_from(literal))]
+impl<F: Float> Default for SlidingPDPSConfig<F> {
+fn default() -> Self {
+SlidingPDPSConfig {
+τ0: 0.99,
+σd0: 0.05,
+σp0: 0.99,
+transport: TransportConfig {
+θ0: 0.9,
+..Default::default()
+},
+insertion: Default::default(),
+}
+}
+}
+type MeasureZ<F, Z, const N: usize> = Pair<RNDM<F, N>, Z>;
+/// Iteratively solve the pointsource localisation with an additional variable
+/// using sliding primal-dual proximal splitting
+///
+/// The parametrisation is as for [`crate::forward_pdps::pointsource_forward_pdps_pair`].
+#[replace_float_literals(F::cast_from(literal))]
+pub fn pointsource_sliding_pdps_pair<
+F,
+I,
+A,
+S,
+Reg,
+P,
+Z,
+R,
+Y,
+/*KOpM, */ KOpZ,
+H,
+const N: usize,
+>(
+opA: &A,
+b: &A::Observable,
+reg: Reg,
+prox_penalty: &P,
+config: &SlidingPDPSConfig<F>,
+iterator: I,
+mut plotter: SeqPlotter<F, N>,
+//opKμ : KOpM,
+opKz: &KOpZ,
+fnR: &R,
+fnH: &H,
+mut z: Z,
+mut y: Y,
+) -> MeasureZ<F, Z, N>
+where
+F: Float + ToNalgebraRealField,
+I: AlgIteratorFactory<IterInfo<F, N>>,
+A: ForwardModel<MeasureZ<F, Z, N>, F, PairNorm<Radon, L2, L2>, PreadjointCodomain = Pair<S, Z>>
++ AdjointProductPairBoundedBy<MeasureZ<F, Z, N>, P, IdOp<Z>, FloatType = F>
++ BoundedCurvature<FloatType = F>,
+S: DifferentiableRealMapping<F, N>,
+for<'b> &'b A::Observable: std::ops::Neg<Output = A::Observable> + Instance<A::Observable>,
+PlotLookup: Plotting<N>,
+RNDM<F, N>: SpikeMerging<F>,
+Reg: SlidingRegTerm<F, N>,
+P: ProxPenalty<F, S, Reg, N>,
+// KOpM : Linear<RNDM<F, N>, Codomain=Y>
+//     + GEMV<F, RNDM<F, N>>
+//     + Preadjointable<
+//         RNDM<F, N>, Y,
+//         PreadjointCodomain = S,
+//     >
+//     + TransportLipschitz<L2Squared, FloatType=F>
+//     + AdjointProductBoundedBy<RNDM<F, N>, 𝒟, FloatType=F>,
+// for<'b> KOpM::Preadjoint<'b> : GEMV<F, Y>,
+// Since Z is Hilbert, we may just as well use adjoints for K_z.
+KOpZ: BoundedLinear<Z, L2, L2, F, Codomain = Y>
++ GEMV<F, Z>
++ Adjointable<Z, Y, AdjointCodomain = Z>,
+for<'b> KOpZ::Adjoint<'b>: GEMV<F, Y>,
+Y: AXPY<F> + Euclidean<F, Output = Y> + Clone + ClosedAdd,
+for<'b> &'b Y: Instance<Y>,
+Z: AXPY<F, Owned = Z> + Euclidean<F, Output = Z> + Clone + Norm<F, L2> + Dist<F, L2>,
+for<'b> &'b Z: Instance<Z>,
+R: Prox<Z, Codomain = F>,
+H: Conjugable<Y, F, Codomain = F>,
+for<'b> H::Conjugate<'b>: Prox<Y>,
+{
+// Check parameters
+assert!(
+config.τ0 > 0.0
+&& config.τ0 < 1.0
+&& config.σp0 > 0.0
+&& config.σp0 < 1.0
+&& config.σd0 > 0.0
+&& config.σp0 * config.σd0 <= 1.0,
+"Invalid step length parameters"
+);
+config.transport.check();
+// Initialise iterates
+let mut μ = DiscreteMeasure::new();
+let mut γ1 = DiscreteMeasure::new();
+let mut residual = calculate_residual(Pair(&μ, &z), opA, b);
+let zero_z = z.similar_origin();
+// Set up parameters
+// TODO: maybe this PairNorm doesn't make sense here?
+// let opAnorm = opA.opnorm_bound(PairNorm(Radon, L2, L2), L2);
+let bigθ = 0.0; //opKμ.transport_lipschitz_factor(L2Squared);
+let bigM = 0.0; //opKμ.adjoint_product_bound(&op𝒟).unwrap().sqrt();
+let nKz = opKz.opnorm_bound(L2, L2);
+let ℓ = 0.0;
+let opIdZ = IdOp::new();
+let (l, l_z) = opA
+.adjoint_product_pair_bound(prox_penalty, &opIdZ)
+.unwrap();
+// We need to satisfy
+//
+//     τσ_dM(1-σ_p L_z)/(1 - τ L) + [σ_p L_z + σ_pσ_d‖K_z‖^2] < 1
+//                                  ^^^^^^^^^^^^^^^^^^^^^^^^^
+// with 1 > σ_p L_z and 1 > τ L.
+//
+// To do so, we first solve σ_p and σ_d from standard PDPS step length condition
+// ^^^^^ < 1. then we solve τ from  the rest.
+let σ_d = config.σd0 / nKz;
+let σ_p = config.σp0 / (l_z + config.σd0 * nKz);
+// Observe that = 1 - ^^^^^^^^^^^^^^^^^^^^^ = 1 - σ_{p,0}
+// We get the condition τσ_d M (1-σ_p L_z) < (1-σ_{p,0})*(1-τ L)
+// ⟺ τ [ σ_d M (1-σ_p L_z) + (1-σ_{p,0}) L ] < (1-σ_{p,0})
+let φ = 1.0 - config.σp0;
+let a = 1.0 - σ_p * l_z;
+let τ = config.τ0 * φ / (σ_d * bigM * a + φ * l);
+let ψ = 1.0 - τ * l;
+let β = σ_p * config.σd0 * nKz / a; // σ_p * σ_d * (nKz * nK_z) / a;
+assert!(β < 1.0);
+// Now we need κ‖K_μ(π_♯^1 - π_♯^0)γ‖^2 ≤ (1/θ - τ[ℓ_F + ℓ]) ∫ c_2 dγ for κ defined as:
+let κ = τ * σ_d * ψ / ((1.0 - β) * ψ - τ * σ_d * bigM);
+//  The factor two in the manuscript disappears due to the definition of 𝚹 being
+// for ‖x-y‖₂² instead of c_2(x, y)=‖x-y‖₂²/2.
+let (maybe_ℓ_F0, maybe_transport_lip) = opA.curvature_bound_components();
+let transport_lip = maybe_transport_lip.unwrap();
+let calculate_θ = |ℓ_F, max_transport| {
+let ℓ_r = transport_lip * max_transport;
+config.transport.θ0 / (τ * (ℓ + ℓ_F + ℓ_r) + κ * bigθ * max_transport)
+};
+let mut θ_or_adaptive = match maybe_ℓ_F0 {
+// We assume that the residual is decreasing.
+Some(ℓ_F0) => TransportStepLength::AdaptiveMax {
+l: ℓ_F0 * b.norm2(), // TODO: could estimate computing the real reesidual
+max_transport: 0.0,
+g: calculate_θ,
+},
+None => TransportStepLength::FullyAdaptive {
+l: F::EPSILON,
+max_transport: 0.0,
+g: calculate_θ,
+},
+};
+// Acceleration is not currently supported
+// let γ = dataterm.factor_of_strong_convexity();
+let ω = 1.0;
+// 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();
+let starH = fnH.conjugate();
+// Statistics
+let full_stats = |residual: &A::Observable, μ: &RNDM<F, N>, z: &Z, ε, stats| IterInfo {
+value: residual.norm2_squared_div2()
++ fnR.apply(z)
++ reg.apply(μ)
++ fnH.apply(/* opKμ.apply(μ) + */ opKz.apply(z)),
+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(&residual, &μ, &z, ε, stats.clone())) {
+// Calculate initial transport
+let Pair(v, _) = opA.preadjoint().apply(&residual);
+//opKμ.preadjoint().apply_add(&mut v, y);
+// We want to proceed as in Example 4.12 but with v and v̆ as in §5.
+// With A(ν, z) = A_μ ν + A_z z, following Example 5.1, we have
+// P_ℳ[F'(ν, z) + Ξ(ν, z, y)]= A_ν^*[A_ν ν + A_z z] + K_μ ν = A_ν^*A(ν, z) + K_μ ν,
+// where A_ν^* becomes a multiplier.
+// This is much easier with K_μ = 0, which is the only reason why are enforcing it.
+// TODO: Write a version of initial_transport that can deal with K_μ ≠ 0.
+let (μ_base_masses, mut μ_base_minus_γ0) =
+initial_transport(&mut γ1, &mut μ, τ, &mut θ_or_adaptive, v);
+// 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, z_new) = 'adapt_transport: loop {
+// Calculate τv̆ = τA_*(A[μ_transported + μ_transported_base]-b)
+let residual_μ̆ =
+calculate_residual2(Pair(&γ1, &z), Pair(&μ_base_minus_γ0, &zero_z), opA, b);
+let Pair(mut τv̆, τz̆) = opA.preadjoint().apply(residual_μ̆ * τ);
+// opKμ.preadjoint().gemv(&mut τv̆, τ, y, 1.0);
+// 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,
+&γ1,
+Some(&μ_base_minus_γ0),
+τ,
+ε,
+&config.insertion,
+&reg,
+&state,
+&mut stats,
+);
+// Do z variable primal update here to able to estimate B_{v̆^k-v^{k+1}}
+let mut z_new = τz̆;
+opKz.adjoint().gemv(&mut z_new, -σ_p, &y, -σ_p / τ);
+z_new = fnR.prox(σ_p, z_new + &z);
+// A posteriori transport adaptation.
+if aposteriori_transport(
+&mut γ1,
+&mut μ,
+&mut μ_base_minus_γ0,
+&μ_base_masses,
+Some(z_new.dist(&z, L2)),
+ε,
+&config.transport,
+) {
+break 'adapt_transport (maybe_d, within_tolerances, τv̆, z_new);
+}
+};
+stats.untransported_fraction = Some({
+assert_eq!(μ_base_masses.len(), γ1.len());
+let (a, b) = stats.untransported_fraction.unwrap_or((0.0, 0.0));
+let source = μ_base_masses.iter().map(|v| v.abs()).sum();
+(a + μ_base_minus_γ0.norm(Radon), b + source)
+});
+stats.transport_error = Some({
+assert_eq!(μ_base_masses.len(), γ1.len());
+let (a, b) = stats.transport_error.unwrap_or((0.0, 0.0));
+(a + μ.dist_matching(&γ1), b + γ1.norm(Radon))
+});
+// 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.
+let ins = &config.insertion;
+if ins.merge_now(&state) {
+stats.merged += prox_penalty.merge_spikes_no_fitness(
+&mut μ,
+&mut τv̆,
+&γ1,
+Some(&μ_base_minus_γ0),
+τ,
+ε,
+ins,
+&reg,
+//Some(|μ̃ : &RNDM<F, N>| calculate_residual(Pair(μ̃, &z), opA, b).norm2_squared_div2()),
+);
+}
+// Prune spikes with zero weight. To maintain correct ordering between μ and γ1, also the
+// latter needs to be pruned when μ is.
+// TODO: This could do with a two-vector Vec::retain to avoid copies.
+let μ_new = DiscreteMeasure::from_iter(μ.iter_spikes().filter(|δ| δ.α != F::ZERO).cloned());
+if μ_new.len() != μ.len() {
+let mut μ_iter = μ.iter_spikes();
+γ1.prune_by(|_| μ_iter.next().unwrap().α != F::ZERO);
+stats.pruned += μ.len() - μ_new.len();
+μ = μ_new;
+}
+// Do dual update
+// opKμ.gemv(&mut y, σ_d*(1.0 + ω), &μ, 1.0);    // y = y + σ_d K[(1+ω)(μ,z)^{k+1}]
+opKz.gemv(&mut y, σ_d * (1.0 + ω), &z_new, 1.0);
+// opKμ.gemv(&mut y, -σ_d*ω, μ_base, 1.0);// y = y + σ_d K[(1+ω)(μ,z)^{k+1} - ω (μ,z)^k]-b
+opKz.gemv(&mut y, -σ_d * ω, z, 1.0); // y = y + σ_d K[(1+ω)(μ,z)^{k+1} - ω (μ,z)^k]-b
+y = starH.prox(σ_d, y);
+z = z_new;
+// Update residual
+residual = calculate_residual(Pair(&μ, &z), opA, b);
+// Update step length parameters
+// let ω = pdpsconfig.acceleration.accelerate(&mut τ, &mut σ, γ);
+// Give statistics if requested
+let iter = state.iteration();
+stats.this_iters += 1;
+state.if_verbose(|| {
+plotter.plot_spikes(iter, maybe_d.as_ref(), Some(&τv̆), &μ);
+full_stats(
+&residual,
+&μ,
+&z,
+ε,
+std::mem::replace(&mut stats, IterInfo::new()),
+)
+});
+// Update main tolerance for next iteration
+ε = tolerance.update(ε, iter);
+}
+let fit = |μ̃: &RNDM<F, N>| {
+(opA.apply(Pair(μ̃, &z))-b).norm2_squared_div2()
+//+ fnR.apply(z) + reg.apply(μ)
++ fnH.apply(/* opKμ.apply(&μ̃) + */ opKz.apply(&z))
+};
+μ.merge_spikes_fitness(config.insertion.final_merging_method(), fit, |&v| v);
+μ.prune();
+Pair(μ, z)
+}

comparison: src/sliding_pdps.rs

src/sliding_pdps.rs

Mercurial > repos > pointsource_algs / file comparison

comparison: src/sliding_pdps.rs

src/sliding_pdps.rs