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January 12, 2018 09:20
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module A | |
using Optim | |
import Optim: FirstOrderOptimizer, initial_state, update_state!, trace!, assess_convergence, AbstractOptimizerState, update!, value | |
struct MinimalGradientDescent <: FirstOrderOptimizer | |
η::Float64 | |
end | |
MinimalGradientDescent(; η=1e-1) = MinimalGradientDescent(η) | |
type MinimalGradientDescentState{T,N} <: AbstractOptimizerState | |
x::Array{T,N} | |
x_previous::Array{T,N} | |
f_x_previous::T | |
end | |
function initial_state(method::MinimalGradientDescent, options, d, initial_x) | |
# prepare cache variables etc here | |
MinimalGradientDescentState(initial_x,initial_x,Inf) | |
end | |
function update_state!{T}(d, state::MinimalGradientDescentState{T}, method::MinimalGradientDescent) | |
state.x += -method.η * gradient(d) | |
false # should the procedure force quit? | |
end | |
function trace!(tr, d, state, iteration, method::MinimalGradientDescent, options) | |
dt = Dict() | |
if options.extended_trace | |
dt["x"] = copy(state.x) | |
dt["g(x)"] = copy(gradient(d)) | |
end | |
g_norm = vecnorm(gradient(d), Inf) | |
update!(tr, | |
iteration, | |
value(d), | |
g_norm, | |
dt, | |
options.store_trace, | |
options.show_trace, | |
options.show_every, | |
options.callback) | |
end | |
function assess_convergence(state::MinimalGradientDescentState, d, options) | |
Optim.default_convergence_assessment(state, d, options) | |
end | |
end | |
f = x -> sum(x.^2) + π | |
mfit = optimize(f,rand(2),A.MinimalGradientDescent(),Optim.Options(iterations=500,store_trace=true,extended_trace=true)) |
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