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Merged
merged 3 commits into from
Dec 15, 2017
Merged

Improving printing output #1

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9d84ac3
improved print its
nantonel Dec 11, 2017
20c0573
added verbose_freq and cost in FB and DR
nantonel Dec 15, 2017
636b0ef
removed wrong cost!
nantonel Dec 15, 2017
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2 changes: 2 additions & 0 deletions src/ProximalAlgorithms.jl
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Expand Up @@ -39,9 +39,11 @@ function run(solver::ProximalAlgorithm{I, T})::Tuple{I, T} where {I, T}
# [...]
# end
#
if verbose(solver) display(solver) end
for (it, point) in enumerate(solver)
if verbose(solver, it) display(solver, it) end
end
if verbose(solver) display(solver, it) end
return (it, point)
end

Expand Down
31 changes: 24 additions & 7 deletions src/algorithms/DouglasRachford.jl
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Expand Up @@ -5,14 +5,16 @@
# [1] Eckstein, Bertsekas "On the Douglas-Rachford Splitting Method and the Proximal Point Algorithm for Maximal Monotone Operators*", Mathematical Programming, vol. 55, no. 1, pp. 293-318 (1989).
#

struct DRSIterator{I <: Integer, R <: Real, T <: BlockArray{R}} <: ProximalAlgorithm{I, T}
mutable struct DRSIterator{I <: Integer, R <: Real, T <: BlockArray{R}} <: ProximalAlgorithm{I, T}
x::T
f
g
cost::R
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@lostella lostella Dec 15, 2017

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Forget about the cost for now, this we will add later. Let's not modify anything in the initialize and iterate methods in this PR, only visualization stuff.

gamma::R
maxit::I
tol::R
verbose::I
verbose_freq::I
y::T
r::T
z::T
Expand All @@ -22,13 +24,13 @@ end
################################################################################
# Constructor(s)

function DRSIterator(x0::BlockArray{R}; f=Zero(), g=Zero(), gamma::R=1.0, maxit::I=10000, tol::R=1e-4, verbose=1) where {I, R}
function DRSIterator(x0::BlockArray{R}; f=Zero(), g=Zero(), gamma::R=1.0, maxit::I=10000, tol::R=1e-4, verbose=1, verbose_freq = 100) where {I, R}
y = blockcopy(x0)
r = blockcopy(x0)
z = blockcopy(x0)
FPR_x = blockcopy(x0)
FPR_x .= Inf
return DRSIterator{I, R, typeof(x0)}(x0, f, g, gamma, maxit, tol, verbose, y, r, z, FPR_x)
return DRSIterator{I, R, typeof(x0)}(x0, f, g, R(Inf), gamma, maxit, tol, verbose, verbose_freq, y, r, z, FPR_x)
end

################################################################################
Expand All @@ -38,9 +40,24 @@ maxit(sol::DRSIterator) = sol.maxit

converged(sol::DRSIterator, it) = blockmaxabs(sol.FPR_x)/sol.gamma <= sol.tol

verbose(sol::DRSIterator, it) = sol.verbose > 0
verbose(sol::DRSIterator) = sol.verbose > 0
verbose(sol::DRSIterator, it) = sol.verbose > 0 && (sol.verbose == 2 ? true : (it == 1 || it%sol.verbose_freq == 0))

display(sol::DRSIterator, it) = println("$(it) $(blockmaxabs(sol.FPR_x)/sol.gamma)")
function display(sol::DRSIterator)
@printf("%6s | %10s | %10s | %10s |\n ", "it", "gamma", "fpr","cost")
@printf("------|------------|------------|------------|\n")
end

function display(sol::DRSIterator, it)
@printf("%6d | %7.4e | %7.4e | %7.4e | \n", it, sol.gamma, blockmaxabs(sol.FPR_x)/sol.gamma, sol.cost)
end

function Base.show(io::IO, sol::DRSIterator)
println(io, "Douglas-Rachford Splitting" )
println(io, "fpr : $(blockmaxabs(sol.FPR_x))")
println(io, "gamma : $(sol.gamma)")
print(io, "cost : $(sol.cost)")
end

################################################################################
# Initialization
Expand All @@ -53,9 +70,9 @@ end
# Iteration

function iterate(sol::DRSIterator{I, T}, it::I) where {I, T}
prox!(sol.y, sol.f, sol.x, sol.gamma)
sol.cost = prox!(sol.y, sol.f, sol.x, sol.gamma)
sol.r .= 2.*sol.y .- sol.x
prox!(sol.z, sol.g, sol.r, sol.gamma)
sol.cost += prox!(sol.z, sol.g, sol.r, sol.gamma)
sol.FPR_x .= sol.y .- sol.z
sol.x .-= sol.FPR_x
return sol.z
Expand Down
29 changes: 23 additions & 6 deletions src/algorithms/ForwardBackward.jl
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Expand Up @@ -15,12 +15,14 @@ mutable struct FBSIterator{I <: Integer, R <: Real, T <: BlockArray{R}} <: Proxi
fq
Aq
g
cost::R
gamma::R
maxit::I
tol::R
adaptive::Bool
fast::Bool
verbose::I
verbose_freq::I
theta::R # extrapolation parameter
y # gradient step
z # proximal-gradient step
Expand All @@ -42,7 +44,7 @@ end
################################################################################
# Constructor

function FBSIterator(x0::T; fs=Zero(), As=Identity(blocksize(x0)), fq=Zero(), Aq=Identity(blocksize(x0)), g=Zero(), gamma::R=-1.0, maxit::I=10000, tol::R=1e-4, adaptive=false, fast=false, verbose=1) where {I, R, T}
function FBSIterator(x0::T; fs=Zero(), As=Identity(blocksize(x0)), fq=Zero(), Aq=Identity(blocksize(x0)), g=Zero(), gamma::R=-1.0, maxit::I=10000, tol::R=1e-4, adaptive=false, fast=false, verbose=1, verbose_freq = 100) where {I, R, T}
n = blocksize(x0)
mq = size(Aq, 1)
ms = size(As, 1)
Expand All @@ -59,7 +61,7 @@ function FBSIterator(x0::T; fs=Zero(), As=Identity(blocksize(x0)), fq=Zero(), Aq
Aqz_prev = blockzeros(mq)
Asz_prev = blockzeros(ms)
gradfq_Aqz_prev = blockzeros(mq)
FBSIterator{I, R, T}(x, fs, As, fq, Aq, g, gamma, maxit, tol, adaptive, fast, verbose, 1.0, y, z, z_prev, FPR_x, Aqx, Asx, gradfq_Aqx, gradfs_Asx, 0.0, 0.0, 0.0, At_gradf_Ax, Aqz_prev, Asz_prev, gradfq_Aqz_prev)
FBSIterator{I, R, T}(x, fs, As, fq, Aq, g, R(Inf), gamma, maxit, tol, adaptive, fast, verbose, verbose_freq, 1.0, y, z, z_prev, FPR_x, Aqx, Asx, gradfq_Aqx, gradfs_Asx, 0.0, 0.0, 0.0, At_gradf_Ax, Aqz_prev, Asz_prev, gradfq_Aqz_prev)
end

################################################################################
Expand All @@ -69,10 +71,23 @@ maxit(sol::FBSIterator) = sol.maxit

converged(sol::FBSIterator, it) = blockmaxabs(sol.FPR_x)/sol.gamma <= sol.tol

verbose(sol::FBSIterator, it) = sol.verbose > 0
verbose(sol::FBSIterator) = sol.verbose > 0
verbose(sol::FBSIterator, it) = sol.verbose > 0 && (sol.verbose == 2 ? true : (it == 1 || it%sol.verbose_freq == 0))

function display(sol::FBSIterator)
@printf("%6s | %10s | %10s | %10s |\n ", "it", "gamma", "fpr", "cost")
@printf("------|------------|------------|------------|\n")
end

function display(sol::FBSIterator, it)
println("$(it) $(sol.gamma) $(blockmaxabs(sol.FPR_x)/sol.gamma)")
@printf("%6d | %7.4e | %7.4e | %7.4e | \n", it, sol.gamma, blockmaxabs(sol.FPR_x)/sol.gamma, sol.cost)
end

function Base.show(io::IO, sol::FBSIterator)
println(io, (sol.fast ? "Fast " : "")*"Forward-Backward Splitting" )
println(io, "fpr : $(blockmaxabs(sol.FPR_x))")
println(io, "gamma : $(sol.gamma)")
print( io, "cost : $(sol.cost)")
end

################################################################################
Expand Down Expand Up @@ -105,8 +120,9 @@ function initialize(sol::FBSIterator)
sol.gamma = 1.0/L
end

sol.cost = sol.fq_Aqx+sol.fs_Asx
blockaxpy!(sol.y, sol.x, -sol.gamma, sol.At_gradf_Ax)
prox!(sol.z, sol.g, sol.y, sol.gamma)
sol.cost += prox!(sol.z, sol.g, sol.y, sol.gamma)
blockaxpy!(sol.FPR_x, sol.x, -1.0, sol.z)

end
Expand Down Expand Up @@ -195,7 +211,8 @@ function iterate(sol::FBSIterator{I, R, T}, it) where {I, R, T}
sol.f_Ax = sol.fs_Asx + sol.fq_Aqx
end
blockaxpy!(sol.y, sol.x, -sol.gamma, sol.At_gradf_Ax)
prox!(sol.z, sol.g, sol.y, sol.gamma)
sol.cost = sol.f_Ax
sol.cost += prox!(sol.z, sol.g, sol.y, sol.gamma)
blockaxpy!(sol.FPR_x, sol.x, -1.0, sol.z)

return sol.z
Expand Down
6 changes: 6 additions & 0 deletions src/algorithms/Template.jl
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Expand Up @@ -20,10 +20,16 @@ maxit(sol::TemplateIterator) = sol.maxit

converged(sol::TemplateIterator, it) = false

verbose(sol::TemplateIterator) = true
verbose(sol::TemplateIterator, it) = true

display(sol::TemplateIterator) = println("its ")
display(sol::TemplateIterator, it) = println("$(it) iterations performed")

function Base.show(io::IO, sol::TemplateIterator)
print(io, "Template Solver" )
end

################################################################################
# Initialization

Expand Down
22 changes: 18 additions & 4 deletions src/algorithms/ZeroFPR.jl
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Expand Up @@ -13,6 +13,7 @@ mutable struct ZeroFPRIterator{I <: Integer, R <: Real, T <: BlockArray} <: Prox
tol::R
adaptive::Bool
verbose::I
verbose_freq::I
alpha::R
sigma::R
tau::R
Expand All @@ -38,7 +39,7 @@ end
################################################################################
# Constructor

function ZeroFPRIterator(x0::T; fs=Zero(), As=Identity(blocksize(x0)), fq=Zero(), Aq=Identity(blocksize(x0)), g=Zero(), gamma::R=-1.0, maxit::I=10000, tol::R=1e-4, adaptive=false, memory=10, verbose=1, alpha=0.95, sigma=0.5) where {I, R, T}
function ZeroFPRIterator(x0::T; fs=Zero(), As=Identity(blocksize(x0)), fq=Zero(), Aq=Identity(blocksize(x0)), g=Zero(), gamma::R=-1.0, maxit::I=10000, tol::R=1e-4, adaptive=false, memory=10, verbose=1, verbose_freq=100, alpha=0.95, sigma=0.5) where {I, R, T}
n = blocksize(x0)
mq = size(Aq, 1)
ms = size(As, 1)
Expand All @@ -52,7 +53,7 @@ function ZeroFPRIterator(x0::T; fs=Zero(), As=Identity(blocksize(x0)), fq=Zero()
gradfs_Asx = blockzeros(ms)
At_gradf_Ax = blockzeros(n)
d = blockzeros(x0)
ZeroFPRIterator{I, R, T}(x, fs, As, fq, Aq, g, gamma, maxit, tol, adaptive, verbose, alpha, sigma, 0.0, y, xbar, LBFGS(x, memory), FPR_x, Aqx, Asx, gradfq_Aqx, gradfs_Asx, 0.0, 0.0, 0.0, At_gradf_Ax, 0.0, 0.0, [], [], d)
ZeroFPRIterator{I, R, T}(x, fs, As, fq, Aq, g, gamma, maxit, tol, adaptive, verbose, verbose_freq, alpha, sigma, 0.0, y, xbar, LBFGS(x, memory), FPR_x, Aqx, Asx, gradfq_Aqx, gradfs_Asx, 0.0, 0.0, 0.0, At_gradf_Ax, 0.0, 0.0, [], [], d)
end

################################################################################
Expand All @@ -62,10 +63,23 @@ maxit(sol::ZeroFPRIterator) = sol.maxit

converged(sol::ZeroFPRIterator, it) = blockmaxabs(sol.FPR_x)/sol.gamma <= sol.tol

verbose(sol::ZeroFPRIterator, it) = sol.verbose > 0
verbose(sol::ZeroFPRIterator) = sol.verbose > 0
verbose(sol::ZeroFPRIterator, it) = sol.verbose > 0 && (sol.verbose == 2 ? true : (it == 1 || it%sol.verbose_freq == 0))

function display(sol::ZeroFPRIterator)
@printf("%6s | %10s | %10s | %10s | %10s |\n ", "it", "gamma", "fpr", "tau", "FBE")
@printf("------|------------|------------|------------|------------|\n")
end
function display(sol::ZeroFPRIterator, it)
println("$(it) $(sol.gamma) $(blockmaxabs(sol.FPR_x)/sol.gamma) $(blockmaxabs(sol.d)) $(sol.tau) $(sol.FBE_x)")
@printf("%6d | %7.4e | %7.4e | %7.4e | %7.4e | \n", it, sol.gamma, blockmaxabs(sol.FPR_x)/sol.gamma, sol.tau, sol.FBE_x)
end

function Base.show(io::IO, sol::ZeroFPRIterator)
println(io, "ZeroFPR" )
println(io, "fpr : $(blockmaxabs(sol.FPR_x))")
println(io, "gamma : $(sol.gamma)")
println(io, "tau : $(sol.tau)")
print( io, "FBE : $(sol.FBE_x)")
end

################################################################################
Expand Down
3 changes: 3 additions & 0 deletions test/test_l1logreg_small.jl
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Expand Up @@ -20,17 +20,20 @@ x0 = zeros(n)
@time it, x, sol = ProximalAlgorithms.FBS(x0; fs=f, As=A, g=g, tol=1e-6, adaptive=true)
@test vecnorm(x - x_star, Inf) <= 1e-4
@test it == 1658
println(sol)

# Fast/Adaptive

x0 = zeros(n)
@time it, x, sol = ProximalAlgorithms.FBS(x0; fs=f, As=A, g=g, tol=1e-6, adaptive=true, fast=true)
@test vecnorm(x - x_star, Inf) <= 1e-4
@test it == 473
println(sol)

# ZeroFPR/Adaptive

x0 = zeros(n)
@time it, x, sol = ProximalAlgorithms.ZeroFPR(x0; fs=f, As=A, g=g, tol=1e-6, adaptive=true)
@test vecnorm(x - x_star, Inf) <= 1e-4
@test it == 19
println(sol)
7 changes: 7 additions & 0 deletions test/test_lasso_small.jl
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Expand Up @@ -21,44 +21,51 @@ x0 = zeros(n)
@time it, x, sol = ProximalAlgorithms.FBS(x0; fq=f, Aq=A, g=g, gamma=1.0/norm(A)^2)
@test vecnorm(x - x_star, Inf) <= 1e-4
@test it == 140
println(sol)

# Nonfast/Adaptive

x0 = zeros(n)
@time it, x, sol = ProximalAlgorithms.FBS(x0; fq=f, Aq=A, g=g, adaptive=true)
@test vecnorm(x - x_star, Inf) <= 1e-4
@test it == 247
println(sol)

# Fast/Nonadaptive

x0 = zeros(n)
@time it, x, sol = ProximalAlgorithms.FBS(x0; fq=f, Aq=A, g=g, gamma=1.0/norm(A)^2, fast=true)
@test vecnorm(x - x_star, Inf) <= 1e-4
@test it == 94
println(sol)

# Fast/Adaptive

x0 = zeros(n)
@time it, x, sol = ProximalAlgorithms.FBS(x0; fq=f, Aq=A, g=g, adaptive=true, fast=true)
@test vecnorm(x - x_star, Inf) <= 1e-4
@test it == 156
println(sol)

# ZeroFPR/Nonadaptive

x0 = zeros(n)
@time it, x, sol = ProximalAlgorithms.ZeroFPR(x0; fq=f, Aq=A, g=g, gamma=1.0/norm(A)^2)
@test vecnorm(x - x_star, Inf) <= 1e-4
@test it == 8
println(sol)

# ZeroFPR/Adaptive

x0 = zeros(n)
@time it, x, sol = ProximalAlgorithms.ZeroFPR(x0; fq=f, Aq=A, g=g, adaptive=true)
@test vecnorm(x - x_star, Inf) <= 1e-4
@test it == 10
println(sol)

# Douglas-Rachford

x0 = zeros(n)
@time it, x, sol = ProximalAlgorithms.DRS(x0; f=f2, g=g, gamma=10.0/norm(A)^2)
@test vecnorm(x - x_star, Inf) <= 1e-4
println(sol)
6 changes: 6 additions & 0 deletions test/test_lasso_small_split_f.jl
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Expand Up @@ -24,38 +24,44 @@ x0 = ProximalAlgorithms.blockzeros(x_star)
@time it, x, sol = ProximalAlgorithms.FBS(x0; fq=f, Aq=opA, g=g, gamma=1.0/norm(A)^2)
@test vecnorm(x .- x_star, Inf) <= 1e-4
@test it == 140
println(sol)

# Nonfast/Adaptive

x0 = ProximalAlgorithms.blockzeros(x_star)
@time it, x, sol = ProximalAlgorithms.FBS(x0; fq=f, Aq=opA, g=g, adaptive=true)
@test vecnorm(x .- x_star, Inf) <= 1e-4
@test it == 247
println(sol)

# Fast/Nonadaptive

x0 = ProximalAlgorithms.blockzeros(x_star)
@time it, x, sol = ProximalAlgorithms.FBS(x0; fq=f, Aq=opA, g=g, gamma=1.0/norm(A)^2, fast=true)
@test vecnorm(x .- x_star, Inf) <= 1e-4
@test it == 94
println(sol)

# Fast/Adaptive

x0 = ProximalAlgorithms.blockzeros(x_star)
@time it, x, sol = ProximalAlgorithms.FBS(x0; fq=f, Aq=opA, g=g, adaptive=true, fast=true)
@test vecnorm(x .- x_star, Inf) <= 1e-4
@test it == 156
println(sol)

# ZeroFPR/Nonadaptive

x0 = ProximalAlgorithms.blockzeros(x_star)
@time it, x, sol = ProximalAlgorithms.ZeroFPR(x0; fq=f, Aq=opA, g=g, gamma=1.0/norm(A)^2)
@test vecnorm(x - x_star, Inf) <= 1e-4
@test it == 8
println(sol)

# ZeroFPR/Adaptive

x0 = ProximalAlgorithms.blockzeros(x_star)
@time it, x, sol = ProximalAlgorithms.ZeroFPR(x0; fq=f, Aq=opA, g=g, adaptive=true)
@test vecnorm(x - x_star, Inf) <= 1e-4
@test it == 10
println(sol)
6 changes: 6 additions & 0 deletions test/test_lasso_small_split_x.jl
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Expand Up @@ -31,31 +31,37 @@ x0 = ProximalAlgorithms.blockzeros(x_star)
@time it, x, sol = ProximalAlgorithms.FBS(x0; fq=f, Aq=opA, g=g, gamma=1.0/norm(A)^2)
@test ProximalAlgorithms.blockmaxabs(x .- x_star) <= 1e-4
@test it == 140
println(sol)

# Nonfast/Adaptive

x0 = ProximalAlgorithms.blockzeros(x_star)
@time it, x, sol = ProximalAlgorithms.FBS(x0; fq=f, Aq=opA, g=g, adaptive=true)
@test ProximalAlgorithms.blockmaxabs(x .- x_star) <= 1e-4
@test it == 247
println(sol)

# Fast/Nonadaptive

x0 = ProximalAlgorithms.blockzeros(x_star)
@time it, x, sol = ProximalAlgorithms.FBS(x0; fq=f, Aq=opA, g=g, gamma=1.0/norm(A)^2, fast=true)
@test ProximalAlgorithms.blockmaxabs(x .- x_star) <= 1e-4
@test it == 94
println(sol)

# Fast/Adaptive

x0 = ProximalAlgorithms.blockzeros(x_star)
@time it, x, sol = ProximalAlgorithms.FBS(x0; fq=f, Aq=opA, g=g, adaptive=true, fast=true)
@test ProximalAlgorithms.blockmaxabs(x .- x_star) <= 1e-4
@test it == 156
println(sol)

# ZeroFPR/Adaptive

x0 = ProximalAlgorithms.blockzeros(x_star)
@time it, x, sol = ProximalAlgorithms.ZeroFPR(x0; fq=f, Aq=opA, g=g, adaptive=true)
@test ProximalAlgorithms.blockmaxabs(x .- x_star) <= 1e-4
@test it == 10
println(sol)

1 change: 1 addition & 0 deletions test/test_template.jl
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Expand Up @@ -2,3 +2,4 @@ x0 = randn(10)
it, x, sol = ProximalAlgorithms.Template(x0)
@test it == 10
@test norm(x - x0) == 0.0
println(sol)