modified accelerator for variable timestep

simplify expression

format

rm typo in UKI that causes warning

needs a sqrt

remove r

add ref into docstring

re-add original nesterov scheme as ConstantStepNesterovAccelerator for comparisons

format

github actions takes ages, processing bug I expect, increasing timeout

bors.toml

@@ -4,5 +4,5 @@ status = [
   'format',
 ]
 delete_merged_branches = true
-timeout_sec = 3600
+timeout_sec = 7200
 block_labels = [ "do-not-merge-yet" ]

src/Accelerators.jl

@@ -1,6 +1,6 @@
 # included in EnsembleKalmanProcess.jl
 
-export DefaultAccelerator, NesterovAccelerator
+export DefaultAccelerator, NesterovAccelerator, ConstantStepNesterovAccelerator
 export accelerate!, set_initial_acceleration!
 
 """
@@ -10,21 +10,52 @@ Default accelerator provides no acceleration, runs traditional EKI
 """
 struct DefaultAccelerator <: Accelerator end
 
+
+
 """
 $(TYPEDEF)
 
-Accelerator that adapts Nesterov's momentum method for EKI. 
+Accelerator that adapts a Constant-timestep version of Nesterov's momentum method for EKI. 
 Stores a previous state value u_prev for computational purposes (note this is distinct from state returned as "ensemble value")
 
 $(TYPEDFIELDS)
 """
-mutable struct NesterovAccelerator{FT <: AbstractFloat} <: Accelerator
+mutable struct ConstantStepNesterovAccelerator{FT <: AbstractFloat} <: Accelerator
     r::FT
     u_prev::Any
 end
 
-function NesterovAccelerator(r = 3.0, initial = Float64[])
-    return NesterovAccelerator(r, initial)
+function ConstantStepNesterovAccelerator(r = 3.0, initial = Float64[])
+    return ConstantStepNesterovAccelerator(r, initial)
+end
+
+
+"""
+Sets u_prev to the initial parameter values
+"""
+function set_ICs!(
+    accelerator::ConstantStepNesterovAccelerator{FT},
+    u::MA,
+) where {FT <: AbstractFloat, MA <: AbstractMatrix}
+    accelerator.u_prev = u
+end
+
+
+"""
+$(TYPEDEF)
+
+Accelerator that adapts Nesterov's momentum method for EKI. 
+Stores a previous state value u_prev for computational purposes (note this is distinct from state returned as "ensemble value")
+
+$(TYPEDFIELDS)
+"""
+mutable struct NesterovAccelerator{FT <: AbstractFloat} <: Accelerator
+    u_prev::Array{FT}
+    θ_prev::FT
+end
+
+function NesterovAccelerator(initial = Float64[])
+    return NesterovAccelerator(initial, 1.0)
 end
 
 
@@ -48,17 +79,27 @@ end
 
 """
 Performs state update with modified Nesterov momentum approach.
+The dependence of the momentum parameter for variable timestep can be found e.g. here "https://www.damtp.cam.ac.uk/user/hf323/M19-OPT/lecture5.pdf"
 """
 function accelerate!(
     ekp::EnsembleKalmanProcess{FT, IT, P, LRS, NesterovAccelerator{FT}},
     u::MA,
 ) where {FT <: AbstractFloat, IT <: Int, P <: Process, LRS <: LearningRateScheduler, MA <: AbstractMatrix}
     ## update "v" state:
-    k = get_N_iterations(ekp) + 2
-    v = u .+ (1 - ekp.accelerator.r / k) * (u .- ekp.accelerator.u_prev)
+    #v = u .+ 2 / (get_N_iterations(ekp) + 2) * (u .- ekp.accelerator.u_prev)
+    Δt_prev = length(ekp.Δt) == 1 ? 1 : ekp.Δt[end - 1]
+    Δt = ekp.Δt[end]
+    θ_prev = ekp.accelerator.θ_prev
+
+    # condition θ_prev^2 * (1 - θ) * Δt \leq Δt_prev * θ^2
+    a = sqrt(θ_prev^2 * Δt / Δt_prev)
+    θ = (-a + sqrt(a^2 + 4)) / 2
+
+    v = u .+ θ * (1 / θ_prev - 1) * (u .- ekp.accelerator.u_prev)
 
     ## update "u" state: 
     ekp.accelerator.u_prev = u
+    ekp.accelerator.θ_prev = θ
 
     ## push "v" state to EKP object
     push!(ekp.u, DataContainer(v, data_are_columns = true))
@@ -67,13 +108,85 @@ end
 
 """
 State update method for UKI with Nesterov Accelerator.
+The dependence of the momentum parameter for variable timestep can be found e.g. here "https://www.damtp.cam.ac.uk/user/hf323/M19-OPT/lecture5.pdf"
 Performs identical update as with other methods, but requires reconstruction of mean and covariance of the accelerated positions prior to saving.
 """
 function accelerate!(
     uki::EnsembleKalmanProcess{FT, IT, P, LRS, NesterovAccelerator{FT}},
     u::AM,
 ) where {FT <: AbstractFloat, IT <: Int, P <: Unscented, LRS <: LearningRateScheduler, AM <: AbstractMatrix}
 
+    #identical update stage as before
+    ## update "v" state:
+    Δt_prev = length(uki.Δt) == 1 ? 1 : uki.Δt[end - 1]
+    Δt = uki.Δt[end]
+    θ_prev = uki.accelerator.θ_prev
+
+
+    # condition θ_prev^2 * (1 - θ) * Δt \leq Δt_prev * θ^2
+    a = sqrt(θ_prev^2 * Δt / Δt_prev)
+    θ = (-a + sqrt(a^2 + 4)) / 2
+
+    v = u .+ θ * (1 / θ_prev - 1) * (u .- uki.accelerator.u_prev)
+
+    ## update "u" state: 
+    uki.accelerator.u_prev = u
+    uki.accelerator.θ_prev = θ
+
+    ## push "v" state to UKI object
+    push!(uki.u, DataContainer(v, data_are_columns = true))
+
+    # additional complication: the stored "u_mean" and "uu_cov" are not the mean/cov of this ensemble
+    # the ensemble comes from the prediction operation acted upon this. we invert the prediction of the mean/cov of the sigma ensemble from u_mean/uu_cov
+    # u_mean = 1/alpha*(mean(v) - r) + r
+    # uu_cov = 1/alpha^2*(cov(v) - Σ_ω)
+    α_reg = uki.process.α_reg
+    r = uki.process.r
+    Σ_ω = uki.process.Σ_ω
+    Δt = uki.Δt[end]
+
+    v_mean = construct_mean(uki, v)
+    vv_cov = construct_cov(uki, v, v_mean)
+    u_mean = 1 / α_reg * (v_mean - r) + r
+    uu_cov = (1 / α_reg)^2 * (vv_cov - Σ_ω * Δt)
+
+    # overwrite the saved u_mean/uu_cov
+    uki.process.u_mean[end] = u_mean # N_ens x N_params 
+    uki.process.uu_cov[end] = uu_cov # N_ens x N_data
+
+end
+
+
+
+
+"""
+Performs state update with modified constant timestep Nesterov momentum approach.
+"""
+function accelerate!(
+    ekp::EnsembleKalmanProcess{FT, IT, P, LRS, ConstantStepNesterovAccelerator{FT}},
+    u::MA,
+) where {FT <: AbstractFloat, IT <: Int, P <: Process, LRS <: LearningRateScheduler, MA <: AbstractMatrix}
+    ## update "v" state:
+    k = get_N_iterations(ekp) + 2
+    v = u .+ (1 - ekp.accelerator.r / k) * (u .- ekp.accelerator.u_prev)
+
+    ## update "u" state: 
+    ekp.accelerator.u_prev = u
+
+    ## push "v" state to EKP object
+    push!(ekp.u, DataContainer(v, data_are_columns = true))
+end
+
+
+"""
+State update method for UKI with constant timestep Nesterov Accelerator.
+Performs identical update as with other methods, but requires reconstruction of mean and covariance of the accelerated positions prior to saving.
+"""
+function accelerate!(
+    uki::EnsembleKalmanProcess{FT, IT, P, LRS, ConstantStepNesterovAccelerator{FT}},
+    u::AM,
+) where {FT <: AbstractFloat, IT <: Int, P <: Unscented, LRS <: LearningRateScheduler, AM <: AbstractMatrix}
+
     #identical update stage as before
     ## update "v" state:
     k = get_N_iterations(uki) + 2

src/EnsembleKalmanProcess.jl

@@ -214,7 +214,7 @@ function EnsembleKalmanProcess(
     end
     AC = typeof(acc)
 
-    if AC <: NesterovAccelerator
+    if !(AC <: DefaultAccelerator)
         set_ICs!(acc, params)
         if P <: Sampler
             @warn "Acceleration is experimental for Sampler processes and may affect convergence."

src/UnscentedKalmanInversion.jl

@@ -551,10 +551,7 @@ end
 
 UKI prediction step : generate sigma points.
 """
-function update_ensemble_prediction!(
-    process::Unscented,
-    Δt::FT,
-) where {FT <: AbstractFloat, AV <: AbstractVector, AM <: AbstractMatrix}
+function update_ensemble_prediction!(process::Unscented, Δt::FT) where {FT <: AbstractFloat}
 
     process.iter += 1
     # update evolution covariance matrix

test/EnsembleKalmanProcess/runtests.jl

@@ -82,13 +82,29 @@ end
 @testset "Accelerators" begin
     # Get an inverse problem
     y_obs, G, Γy, _ = inv_problems[end - 2] # additive noise inv problem (deterministic map)
+    inv_sqrt_Γy = sqrt(inv(Γy))
+
     rng = Random.MersenneTwister(rng_seed)
     N_ens_tmp = 5
     initial_ensemble = EKP.construct_initial_ensemble(rng, prior, N_ens_tmp)
 
     # build accelerated and non-accelerated processes
     ekiobj = EKP.EnsembleKalmanProcess(initial_ensemble, y_obs, Γy, Inversion(), accelerator = NesterovAccelerator())
     eksobj = EKP.EnsembleKalmanProcess(initial_ensemble, y_obs, Γy, Sampler(prior), accelerator = NesterovAccelerator())
+    ekiobj_const = EKP.EnsembleKalmanProcess(
+        initial_ensemble,
+        y_obs,
+        Γy,
+        Inversion(),
+        accelerator = ConstantStepNesterovAccelerator(),
+    )
+    eksobj_const = EKP.EnsembleKalmanProcess(
+        initial_ensemble,
+        y_obs,
+        Γy,
+        Sampler(prior),
+        accelerator = ConstantStepNesterovAccelerator(),
+    )
     ekiobj_noacc = EKP.EnsembleKalmanProcess(initial_ensemble, y_obs, Γy, Inversion())
     eksobj_noacc = EKP.EnsembleKalmanProcess(initial_ensemble, y_obs, Γy, Sampler(prior))
     ekiobj_noacc_specified =
@@ -99,26 +115,45 @@ end
     ## test EKP object's accelerator type is consistent (EKP constructor reassigns object in some cases)
     @test typeof(ekiobj.accelerator) <: NesterovAccelerator
     @test typeof(eksobj.accelerator) <: NesterovAccelerator
+    @test typeof(ekiobj_const.accelerator) <: ConstantStepNesterovAccelerator
+    @test typeof(eksobj_const.accelerator) <: ConstantStepNesterovAccelerator
     @test typeof(ekiobj_noacc.accelerator) <: DefaultAccelerator
     @test typeof(eksobj_noacc.accelerator) <: DefaultAccelerator
     @test typeof(ekiobj_noacc_specified.accelerator) <: DefaultAccelerator
     @test typeof(eksobj_noacc_specified.accelerator) <: DefaultAccelerator
 
     ## test NesterovAccelerators satisfy desired ICs
-    @test ekiobj.accelerator.r ≈ 3.0
     @test ekiobj.accelerator.u_prev == initial_ensemble
-    @test eksobj.accelerator.r ≈ 3.0
+    @test ekiobj.accelerator.θ_prev == 1.0
     @test eksobj.accelerator.u_prev == initial_ensemble
+    @test eksobj.accelerator.θ_prev == 1.0
+
+    @test ekiobj_const.accelerator.r ≈ 3.0
+    @test ekiobj_const.accelerator.u_prev == initial_ensemble
+    @test eksobj_const.accelerator.r ≈ 3.0
+    @test eksobj_const.accelerator.u_prev == initial_ensemble
 
     ## test method convergence
     # Note: this test only requires that the final ensemble is an improvement on the initial ensemble,
     # NOT that the accelerated processes are more effective than the default, as this is not guaranteed.
     # Specific cost values are printed to give an idea of acceleration.
-    processes = [Inversion(), TransformInversion(inv(Γy)), Unscented(prior; impose_prior = true), Sampler(prior)]
-    schedulers = [repeat([DefaultScheduler(0.1)], 3)..., EKSStableScheduler()]
+    processes = [
+        repeat([Inversion(), TransformInversion(inv(Γy)), Unscented(prior; impose_prior = true)], 2)...,
+        Sampler(prior),
+    ]
+    schedulers = [
+        repeat([DefaultScheduler(0.1)], 3)..., # for constant timestep Nesterov
+        repeat([DataMisfitController(terminate_at = 100)], 3)..., # for general Nesterov
+        EKSStableScheduler(), # for general Nesterov
+    ]
     for (process, scheduler) in zip(processes, schedulers)
-        accelerators = [DefaultAccelerator(), NesterovAccelerator()]
-        N_iters = [5, 5, 5, 5]
+        if typeof(scheduler) <: DefaultScheduler
+            accelerators = [DefaultAccelerator(), ConstantStepNesterovAccelerator(), NesterovAccelerator()]
+            N_iters = [20, 20, 20]
+        else #don't test the constantstep accelerator with variable timesteppers
+            accelerators = [DefaultAccelerator(), NesterovAccelerator()]
+            N_iters = [20, 20]
+        end
         init_means = []
         final_means = []
 
@@ -160,9 +195,8 @@ end
             push!(init_means, vec(mean(get_u_prior(ekpobj), dims = 2)))
             push!(final_means, vec(mean(get_u_final(ekpobj), dims = 2)))
 
-            inv_sqrt_Γy = sqrt(inv(Γy))
             cost_initial =
-                norm(inv_sqrt_Γy * (y_obs .- G(transform_unconstrained_to_constrained(prior, init_means[end]))))
+                norm(inv_sqrt_Γy * (y_obs .- G(transform_unconstrained_to_constrained(prior, initial_ensemble))))
             cost_final =
                 norm(inv_sqrt_Γy * (y_obs .- G(transform_unconstrained_to_constrained(prior, final_means[end]))))
             @info "Convergence:" cost_initial cost_final