Expose backtracking factor, rename increase factor

src/algorithms/fast_forward_backward.jl

@@ -33,7 +33,8 @@ See also: [`FastForwardBackward`](@ref).
 - `gamma=nothing`: stepsize, defaults to `1/Lf` if `Lf` is set, and `nothing` otherwise.
 - `adaptive=true`: makes `gamma` adaptively adjust during the iterations; this is by default `gamma === nothing`.
 - `minimum_gamma=1e-7`: lower bound to `gamma` in case `adaptive == true`.
-- `regret_gamma=1.0`: factor to enlarge `gamma` in case `adaptive == true`, before backtracking.
+- `reduce_gamma=0.5`: factor by which to reduce `gamma` in case `adaptive == true`, during backtracking.
+- `increase_gamma=1.0`: factor by which to increase `gamma` in case `adaptive == true`, before backtracking.
 - `extrapolation_sequence=nothing`: sequence (iterator) of extrapolation coefficients to use for acceleration.
 
 # References
@@ -49,7 +50,8 @@ Base.@kwdef struct FastForwardBackwardIteration{R,Tx,Tf,Tg,TLf,Tgamma,Textr}
     gamma::Tgamma = Lf === nothing ? nothing : (1 / Lf)
     adaptive::Bool = gamma === nothing
     minimum_gamma::R = real(eltype(x0))(1e-7)
-    regret_gamma::R = real(eltype(x0))(1.0)
+    reduce_gamma::R = real(eltype(x0))(0.5)
+    increase_gamma::R = real(eltype(x0))(1.0)
     extrapolation_sequence::Textr = nothing
 end
 
@@ -107,7 +109,7 @@ function Base.iterate(
     state::FastForwardBackwardState{R,Tx},
 ) where {R,Tx}
     state.gamma = if iter.adaptive == true
-        state.gamma *= iter.regret_gamma
+        state.gamma *= iter.increase_gamma
         gamma, state.g_z = backtrack_stepsize!(
             state.gamma,
             iter.f,
@@ -123,6 +125,7 @@ function Base.iterate(
             state.z,
             nothing,
             minimum_gamma = iter.minimum_gamma,
+            reduce_gamma = iter.reduce_gamma,
         )
         gamma
     else

src/algorithms/forward_backward.jl

@@ -28,7 +28,8 @@ See also: [`ForwardBackward`](@ref).
 - `gamma=nothing`: stepsize to use, defaults to `1/Lf` if not set (but `Lf` is).
 - `adaptive=false`: forces the method stepsize to be adaptively adjusted.
 - `minimum_gamma=1e-7`: lower bound to `gamma` in case `adaptive == true`.
-- `regret_gamma=1.0`: factor to enlarge `gamma` in case `adaptive == true`, before backtracking.
+- `reduce_gamma=0.5`: factor by which to reduce `gamma` in case `adaptive == true`, during backtracking.
+- `increase_gamma=1.0`: factor by which to increase `gamma` in case `adaptive == true`, before backtracking.
 
 # References
 1. Lions, Mercier, “Splitting algorithms for the sum of two nonlinear operators,” SIAM Journal on Numerical Analysis, vol. 16, pp. 964–979 (1979).
@@ -42,7 +43,8 @@ Base.@kwdef struct ForwardBackwardIteration{R,Tx,Tf,Tg,TLf,Tgamma}
     gamma::Tgamma = Lf === nothing ? nothing : (1 / Lf)
     adaptive::Bool = gamma === nothing
     minimum_gamma::R = real(eltype(x0))(1e-7)
-    regret_gamma::R = real(eltype(x0))(1.0)
+    reduce_gamma::R = real(eltype(x0))(0.5)
+    increase_gamma::R = real(eltype(x0))(1.0)
 end
 
 Base.IteratorSize(::Type{<:ForwardBackwardIteration}) = Base.IsInfinite()
@@ -87,7 +89,7 @@ function Base.iterate(
     state::ForwardBackwardState{R,Tx},
 ) where {R,Tx}
     if iter.adaptive == true
-        state.gamma *= iter.regret_gamma
+        state.gamma *= iter.increase_gamma
         state.gamma, state.g_z, state.f_x = backtrack_stepsize!(
             state.gamma,
             iter.f,
@@ -103,6 +105,7 @@ function Base.iterate(
             state.z,
             state.grad_f_z,
             minimum_gamma = iter.minimum_gamma,
+            reduce_gamma = iter.reduce_gamma,
         )
         state.x, state.z = state.z, state.x
         state.grad_f_x, state.grad_f_z = state.grad_f_z, state.grad_f_x

src/utilities/fb_tools.jl

@@ -37,15 +37,16 @@ function backtrack_stepsize!(
     res,
     Az,
     grad_f_Az = nothing;
-    alpha = 1,
-    minimum_gamma = 1e-7,
+    alpha = R(1),
+    minimum_gamma = R(1e-7),
+    reduce_gamma = R(0.5),
 ) where {R}
     f_Az_upp = f_model(f_Ax, At_grad_f_Ax, res, alpha / gamma)
     _mul!(Az, A, z)
     f_Az, cl = value_and_gradient_closure(f, Az)
     tol = 10 * eps(R) * (1 + abs(f_Az))
     while f_Az > f_Az_upp + tol && gamma >= minimum_gamma
-        gamma /= 2
+        gamma *= reduce_gamma
         y .= x .- gamma .* At_grad_f_Ax
         g_z = prox!(z, g, y, gamma)
         res .= x .- z
@@ -63,7 +64,16 @@ function backtrack_stepsize!(
     return gamma, g_z, f_Az, f_Az_upp
 end
 
-function backtrack_stepsize!(gamma, f, A, g, x; alpha = 1, minimum_gamma = 1e-7)
+function backtrack_stepsize!(
+    gamma::R,
+    f,
+    A,
+    g,
+    x;
+    alpha = R(1),
+    minimum_gamma = R(1e-7),
+    reduce_gamma = R(0.5),
+) where {R}
     Ax = A * x
     f_Ax, cl = value_and_gradient_closure(f, Ax)
     grad_f_Ax = cl()
@@ -86,5 +96,6 @@ function backtrack_stepsize!(gamma, f, A, g, x; alpha = 1, minimum_gamma = 1e-7)
         grad_f_Ax;
         alpha = alpha,
         minimum_gamma = minimum_gamma,
+        reduce_gamma = reduce_gamma,
     )
 end

test/problems/test_lasso_small.jl

@@ -68,7 +68,11 @@ using ProximalAlgorithms:
     @testset "ForwardBackward (adaptive step, regret)" begin
         x0 = zeros(T, n)
         x0_backup = copy(x0)
-        solver = ProximalAlgorithms.ForwardBackward(tol = TOL, adaptive = true, regret_gamma=R(1.01))
+        solver = ProximalAlgorithms.ForwardBackward(
+            tol = TOL,
+            adaptive = true,
+            increase_gamma = R(1.01),
+        )
         x, it = @inferred solver(x0 = x0, f = fA_autodiff, g = g)
         @test eltype(x) == T
         @test norm(x - x_star, Inf) <= TOL
@@ -101,7 +105,11 @@ using ProximalAlgorithms:
     @testset "FastForwardBackward (adaptive step, regret)" begin
         x0 = zeros(T, n)
         x0_backup = copy(x0)
-        solver = ProximalAlgorithms.FastForwardBackward(tol = TOL, adaptive = true, regret_gamma=R(1.01))
+        solver = ProximalAlgorithms.FastForwardBackward(
+            tol = TOL,
+            adaptive = true,
+            increase_gamma = R(1.01),
+        )
         x, it = @inferred solver(x0 = x0, f = fA_autodiff, g = g)
         @test eltype(x) == T
         @test norm(x - x_star, Inf) <= TOL

test/problems/test_lasso_small_strongly_convex.jl

@@ -70,7 +70,7 @@ using ProximalAlgorithms
         @test it < 110
         @test x0 == x0_backup
     end
-    
+
     @testset "ForwardBackward (adaptive step)" begin
         solver = ProximalAlgorithms.ForwardBackward(tol = TOL, adaptive = true)
         y, it = solver(x0 = x0, f = fA_autodiff, g = g)
@@ -81,7 +81,11 @@ using ProximalAlgorithms
     end
 
     @testset "ForwardBackward (adaptive step, regret)" begin
-        solver = ProximalAlgorithms.ForwardBackward(tol = TOL, adaptive = true, regret_gamma=T(1.01))
+        solver = ProximalAlgorithms.ForwardBackward(
+            tol = TOL,
+            adaptive = true,
+            increase_gamma = T(1.01),
+        )
         y, it = solver(x0 = x0, f = fA_autodiff, g = g)
         @test eltype(y) == T
         @test norm(y - x_star, Inf) <= TOL
@@ -108,7 +112,11 @@ using ProximalAlgorithms
     end
 
     @testset "FastForwardBackward (adaptive step, regret)" begin
-        solver = ProximalAlgorithms.FastForwardBackward(tol = TOL, adaptive = true, regret_gamma=T(1.01))
+        solver = ProximalAlgorithms.FastForwardBackward(
+            tol = TOL,
+            adaptive = true,
+            increase_gamma = T(1.01),
+        )
         y, it = solver(x0 = x0, f = fA_autodiff, g = g)
         @test eltype(y) == T
         @test norm(y - x_star, Inf) <= TOL

test/problems/test_sparse_logistic_small.jl

@@ -49,7 +49,11 @@ using LinearAlgebra
     @testset "ForwardBackward (adaptive step, regret)" begin
         x0 = zeros(T, n)
         x0_backup = copy(x0)
-        solver = ProximalAlgorithms.ForwardBackward(tol = TOL, adaptive = true, regret_gamma=R(1.01))
+        solver = ProximalAlgorithms.ForwardBackward(
+            tol = TOL,
+            adaptive = true,
+            increase_gamma = R(1.01),
+        )
         x, it = solver(x0 = x0, f = fA_autodiff, g = g)
         @test eltype(x) == T
         @test norm(x - x_star, Inf) <= 1e-4
@@ -71,7 +75,11 @@ using LinearAlgebra
     @testset "FastForwardBackward (adaptive step, regret)" begin
         x0 = zeros(T, n)
         x0_backup = copy(x0)
-        solver = ProximalAlgorithms.FastForwardBackward(tol = TOL, adaptive = true, regret_gamma=R(1.01))
+        solver = ProximalAlgorithms.FastForwardBackward(
+            tol = TOL,
+            adaptive = true,
+            increase_gamma = R(1.01),
+        )
         x, it = solver(x0 = x0, f = fA_autodiff, g = g)
         @test eltype(x) == T
         @test norm(x - x_star, Inf) <= 1e-4