pointsource_algs: comparison src/fb.rs

-:9738b51d90d7
+:4f468d35fa29
 \end{aligned}
 $$
 </p>
 We solve this with either SSN or FB as determined by
-[`crate::subproblem::InnerSettings`] in [`FBGenericConfig::inner`].
+[`crate::subproblem::InnerSettings`] in [`InsertionConfig::inner`].
 */
+use crate::measures::merging::SpikeMerging;
+use crate::measures::{DiscreteMeasure, RNDM};
+use crate::plot::Plotter;
+pub use crate::prox_penalty::{InsertionConfig, ProxPenalty, StepLengthBound};
+use crate::regularisation::RegTerm;
+use crate::types::*;
+use alg_tools::error::DynResult;
+use alg_tools::instance::Instance;
+use alg_tools::iterate::AlgIteratorFactory;
+use alg_tools::mapping::DifferentiableMapping;
+use alg_tools::nalgebra_support::ToNalgebraRealField;
 use colored::Colorize;
 use numeric_literals::replace_float_literals;
 use serde::{Deserialize, Serialize};
-use alg_tools::euclidean::Euclidean;
-use alg_tools::instance::Instance;
-use alg_tools::iterate::AlgIteratorFactory;
-use alg_tools::linops::{Mapping, GEMV};
-use alg_tools::mapping::RealMapping;
-use alg_tools::nalgebra_support::ToNalgebraRealField;
-use crate::dataterm::{calculate_residual, DataTerm, L2Squared};
-use crate::forward_model::{AdjointProductBoundedBy, ForwardModel};
-use crate::measures::merging::SpikeMerging;
-use crate::measures::{DiscreteMeasure, RNDM};
-use crate::plot::{PlotLookup, Plotting, SeqPlotter};
-pub use crate::prox_penalty::{FBGenericConfig, ProxPenalty};
-use crate::regularisation::RegTerm;
-use crate::types::*;
 /// Settings for [`pointsource_fb_reg`].
 #[derive(Clone, Copy, Eq, PartialEq, Serialize, Deserialize, Debug)]
 #[serde(default)]
 pub struct FBConfig<F: Float> {
 /// Step length scaling
 pub τ0: F,
+// Auxiliary variable step length scaling for [`crate::forward_pdps::pointsource_fb_pair`]
+pub σp0: F,
 /// Generic parameters
-pub generic: FBGenericConfig<F>,
+pub insertion: InsertionConfig<F>,
 }
 #[replace_float_literals(F::cast_from(literal))]
 impl<F: Float> Default for FBConfig<F> {
 fn default() -> Self {
 FBConfig {
 τ0: 0.99,
-generic: Default::default(),
+σp0: 0.99,
+insertion: Default::default(),
 }
 }
 }
-pub(crate) fn prune_with_stats<F: Float, const N: usize>(μ: &mut RNDM<F, N>) -> usize {
+pub(crate) fn prune_with_stats<F: Float, const N: usize>(μ: &mut RNDM<N, F>) -> usize {
 let n_before_prune = μ.len();
 μ.prune();
 debug_assert!(μ.len() <= n_before_prune);
 n_before_prune - μ.len()
 }
 #[replace_float_literals(F::cast_from(literal))]
-pub(crate) fn postprocess<
+pub(crate) fn postprocess<F: Float, Dat: Fn(&RNDM<N, F>) -> F, const N: usize>(
-F: Float,
+mut μ: RNDM<N, F>,
-V: Euclidean<F> + Clone,
+config: &InsertionConfig<F>,
-A: GEMV<F, RNDM<F, N>, Codomain = V>,
+f: Dat,
-D: DataTerm<F, V, N>,
+) -> DynResult<RNDM<N, F>>
-const N: usize,
->(
-mut μ: RNDM<F, N>,
-config: &FBGenericConfig<F>,
-dataterm: D,
-opA: &A,
-b: &V,
-) -> RNDM<F, N>
 where
-RNDM<F, N>: SpikeMerging<F>,
+RNDM<N, F>: SpikeMerging<F>,
-for<'a> &'a RNDM<F, N>: Instance<RNDM<F, N>>,
+for<'a> &'a RNDM<N, F>: Instance<RNDM<N, F>>,
 {
-μ.merge_spikes_fitness(
+//μ.merge_spikes_fitness(config.final_merging_method(), |μ̃| f.apply(μ̃), |&v| v);
-config.final_merging_method(),
+μ.merge_spikes_fitness(config.final_merging_method(), f, |&v| v);
-|μ̃| dataterm.calculate_fit_op(μ̃, opA, b),
-|&v| v,
-);
 μ.prune();
-μ
+Ok(μ)
 }
 /// Iteratively solve the pointsource localisation problem using forward-backward splitting.
 ///
 /// The settings in `config` have their [respective documentation](FBConfig). `opA` is the
 /// operator. Finally, the `iterator` is an outer loop verbosity and iteration count control
 /// as documented in [`alg_tools::iterate`].
 ///
 /// For details on the mathematical formulation, see the [module level](self) documentation.
 ///
-/// The implementation relies on [`alg_tools::bisection_tree::BTFN`] presentations of
-/// sums of simple functions usign bisection trees, and the related
-/// [`alg_tools::bisection_tree::Aggregator`]s, to efficiently search for component functions
-/// active at a specific points, and to maximise their sums. Through the implementation of the
-/// [`alg_tools::bisection_tree::BT`] bisection trees, it also relies on the copy-on-write features
-/// of [`std::sync::Arc`] to only update relevant parts of the bisection tree when adding functions.
-///
 /// Returns the final iterate.
 #[replace_float_literals(F::cast_from(literal))]
-pub fn pointsource_fb_reg<F, I, A, Reg, P, const N: usize>(
+pub fn pointsource_fb_reg<F, I, Dat, Reg, P, Plot, const N: usize>(
-opA: &A,
+f: &Dat,
-b: &A::Observable,
+reg: &Reg,
-reg: Reg,
 prox_penalty: &P,
 fbconfig: &FBConfig<F>,
 iterator: I,
-mut plotter: SeqPlotter<F, N>,
+mut plotter: Plot,
-) -> RNDM<F, N>
+μ0 : Option<RNDM<N, F>>,
+) -> DynResult<RNDM<N, F>>
 where
 F: Float + ToNalgebraRealField,
-I: AlgIteratorFactory<IterInfo<F, N>>,
+I: AlgIteratorFactory<IterInfo<F>>,
-for<'b> &'b A::Observable: std::ops::Neg<Output = A::Observable>,
+RNDM<N, F>: SpikeMerging<F>,
-A: ForwardModel<RNDM<F, N>, F> + AdjointProductBoundedBy<RNDM<F, N>, P, FloatType = F>,
+Dat: DifferentiableMapping<RNDM<N, F>, Codomain = F>,
-A::PreadjointCodomain: RealMapping<F, N>,
+Dat::DerivativeDomain: ClosedMul<F>,
-PlotLookup: Plotting<N>,
+Reg: RegTerm<Loc<N, F>, F>,
-RNDM<F, N>: SpikeMerging<F>,
+P: ProxPenalty<Loc<N, F>, Dat::DerivativeDomain, Reg, F> + StepLengthBound<F, Dat>,
-Reg: RegTerm<F, N>,
+Plot: Plotter<P::ReturnMapping, Dat::DerivativeDomain, RNDM<N, F>>,
-P: ProxPenalty<F, A::PreadjointCodomain, Reg, N>,
 {
 // Set up parameters
-let config = &fbconfig.generic;
+let config = &fbconfig.insertion;
-let τ = fbconfig.τ0 / opA.adjoint_product_bound(prox_penalty).unwrap();
+let τ = fbconfig.τ0 / prox_penalty.step_length_bound(&f)?;
 // 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.tolerance * τ * reg.tolerance_scaling();
 let mut ε = tolerance.initial();
 // Initialise iterates
-let mut μ = DiscreteMeasure::new();
+let mut μ = μ0.unwrap_or_else(|| DiscreteMeasure::new());
-let mut residual = -b;
 // Statistics
-let full_stats = |residual: &A::Observable, μ: &RNDM<F, N>, ε, stats| IterInfo {
+let full_stats = |μ: &RNDM<N, F>, ε, stats| IterInfo {
-value: residual.norm2_squared_div2() + reg.apply(μ),
+value: f.apply(μ) + reg.apply(μ),
 n_spikes: μ.len(),
 ε,
 //postprocessing: config.postprocessing.then(|| μ.clone()),
 ..stats
 };
 let mut stats = IterInfo::new();
 // Run the algorithm
-for state in iterator.iter_init(|| full_stats(&residual, &μ, ε, stats.clone())) {
+for state in iterator.iter_init(|| full_stats(&μ, ε, stats.clone())) {
 // Calculate smooth part of surrogate model.
-let mut τv = opA.preadjoint().apply(residual * τ);
+// TODO: optimise τ to be applied to residual.
+let mut τv = f.differential(&μ) * τ;
 // Save current base point
 let μ_base = μ.clone();
 // Insert and reweigh
 let (maybe_d, _within_tolerances) = prox_penalty.insert_and_reweigh(
 &mut μ, &mut τv, &μ_base, None, τ, ε, config, &reg, &state, &mut stats,
-);
+)?;
 // Prune and possibly merge spikes
 if config.merge_now(&state) {
 stats.merged += prox_penalty.merge_spikes(
 &mut μ,
 None,
 τ,
 ε,
 config,
 &reg,
-Some(|μ̃: &RNDM<F, N>| L2Squared.calculate_fit_op(μ̃, opA, b)),
+Some(|μ̃: &RNDM<N, F>| f.apply(μ̃)),
 );
 }
 stats.pruned += prune_with_stats(&mut μ);
-// Update residual
-residual = calculate_residual(&μ, opA, b);
 let iter = state.iteration();
 stats.this_iters += 1;
 // Give statistics if needed
 state.if_verbose(|| {
 plotter.plot_spikes(iter, maybe_d.as_ref(), Some(&τv), &μ);
-full_stats(
+full_stats(&μ, ε, std::mem::replace(&mut stats, IterInfo::new()))
-&residual,
-&μ,
-ε,
-std::mem::replace(&mut stats, IterInfo::new()),
-)
 });
 // Update main tolerance for next iteration
 ε = tolerance.update(ε, iter);
 }
-postprocess(μ, config, L2Squared, opA, b)
+//postprocess(μ_prev, config, f)
+postprocess(μ, config, |μ̃| f.apply(μ̃))
 }
 /// Iteratively solve the pointsource localisation problem using inertial forward-backward splitting.
 ///
 /// The settings in `config` have their [respective documentation](FBConfig). `opA` is the
 /// operator. Finally, the `iterator` is an outer loop verbosity and iteration count control
 /// as documented in [`alg_tools::iterate`].
 ///
 /// For details on the mathematical formulation, see the [module level](self) documentation.
 ///
-/// The implementation relies on [`alg_tools::bisection_tree::BTFN`] presentations of
-/// sums of simple functions usign bisection trees, and the related
-/// [`alg_tools::bisection_tree::Aggregator`]s, to efficiently search for component functions
-/// active at a specific points, and to maximise their sums. Through the implementation of the
-/// [`alg_tools::bisection_tree::BT`] bisection trees, it also relies on the copy-on-write features
-/// of [`std::sync::Arc`] to only update relevant parts of the bisection tree when adding functions.
-///
 /// Returns the final iterate.
 #[replace_float_literals(F::cast_from(literal))]
-pub fn pointsource_fista_reg<F, I, A, Reg, P, const N: usize>(
+pub fn pointsource_fista_reg<F, I, Dat, Reg, P, Plot, const N: usize>(
-opA: &A,
+f: &Dat,
-b: &A::Observable,
+reg: &Reg,
-reg: Reg,
 prox_penalty: &P,
 fbconfig: &FBConfig<F>,
 iterator: I,
-mut plotter: SeqPlotter<F, N>,
+mut plotter: Plot,
-) -> RNDM<F, N>
+μ0: Option<RNDM<N, F>>
+) -> DynResult<RNDM<N, F>>
 where
 F: Float + ToNalgebraRealField,
-I: AlgIteratorFactory<IterInfo<F, N>>,
+I: AlgIteratorFactory<IterInfo<F>>,
-for<'b> &'b A::Observable: std::ops::Neg<Output = A::Observable>,
+RNDM<N, F>: SpikeMerging<F>,
-A: ForwardModel<RNDM<F, N>, F> + AdjointProductBoundedBy<RNDM<F, N>, P, FloatType = F>,
+Dat: DifferentiableMapping<RNDM<N, F>, Codomain = F>,
-A::PreadjointCodomain: RealMapping<F, N>,
+Dat::DerivativeDomain: ClosedMul<F>,
-PlotLookup: Plotting<N>,
+Reg: RegTerm<Loc<N, F>, F>,
-RNDM<F, N>: SpikeMerging<F>,
+P: ProxPenalty<Loc<N, F>, Dat::DerivativeDomain, Reg, F> + StepLengthBound<F, Dat>,
-Reg: RegTerm<F, N>,
+Plot: Plotter<P::ReturnMapping, Dat::DerivativeDomain, RNDM<N, F>>,
-P: ProxPenalty<F, A::PreadjointCodomain, Reg, N>,
 {
 // Set up parameters
-let config = &fbconfig.generic;
+let config = &fbconfig.insertion;
-let τ = fbconfig.τ0 / opA.adjoint_product_bound(prox_penalty).unwrap();
+let τ = fbconfig.τ0 / prox_penalty.step_length_bound(&f)?;
 let mut λ = 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.tolerance * τ * reg.tolerance_scaling();
 let mut ε = tolerance.initial();
 // Initialise iterates
-let mut μ = DiscreteMeasure::new();
+let mut μ = μ0.unwrap_or_else(|| DiscreteMeasure::new());
-let mut μ_prev = DiscreteMeasure::new();
+let mut μ_prev = μ.clone();
-let mut residual = -b;
 let mut warned_merging = false;
 // Statistics
-let full_stats = |ν: &RNDM<F, N>, ε, stats| IterInfo {
+let full_stats = |ν: &RNDM<N, F>, ε, stats| IterInfo {
-value: L2Squared.calculate_fit_op(ν, opA, b) + reg.apply(ν),
+value: f.apply(ν) + reg.apply(ν),
 n_spikes: ν.len(),
 ε,
 // postprocessing: config.postprocessing.then(|| ν.clone()),
 ..stats
 };
 let mut stats = IterInfo::new();
 // Run the algorithm
 for state in iterator.iter_init(|| full_stats(&μ, ε, stats.clone())) {
 // Calculate smooth part of surrogate model.
-let mut τv = opA.preadjoint().apply(residual * τ);
+let mut τv = f.differential(&μ) * τ;
 // Save current base point
 let μ_base = μ.clone();
 // Insert new spikes and reweigh
 let (maybe_d, _within_tolerances) = prox_penalty.insert_and_reweigh(
 &mut μ, &mut τv, &μ_base, None, τ, ε, config, &reg, &state, &mut stats,
-);
+)?;
 // (Do not) merge spikes.
 if config.merge_now(&state) && !warned_merging {
 let err = format!("Merging not supported for μFISTA");
 println!("{}", err.red());
 // μ_prev = μ;
 // μ = μ_new;
 debug_assert!(μ.len() <= n_before_prune);
 stats.pruned += n_before_prune - μ.len();
-// Update residual
-residual = calculate_residual(&μ, opA, b);
 let iter = state.iteration();
 stats.this_iters += 1;
 // Give statistics if needed
 state.if_verbose(|| {
 // Update main tolerance for next iteration
 ε = tolerance.update(ε, iter);
 }
-postprocess(μ_prev, config, L2Squared, opA, b)
+//postprocess(μ_prev, config, f)
-}
+postprocess(μ_prev, config, |μ̃| f.apply(μ̃))
+}

Mercurial > repos > pointsource_algs / file comparison

comparison: src/fb.rs

src/fb.rs