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Fixing Nelder-Mead in box optimization #1080

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Fixing Nelder-Mead in box optimization #1080

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17 changes: 15 additions & 2 deletions src/multivariate/solvers/constrained/fminbox.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -41,6 +41,7 @@ function _barrier_term_value(x::T, l, u) where T
end
vl = ifelse(isfinite(dxl), -log(dxl), T(0))
vu = ifelse(isfinite(dxu), -log(dxu), T(0))

return vl + vu
end
function _barrier_term_gradient(x::T, l, u) where T
Expand Down Expand Up @@ -98,17 +99,21 @@ function value_gradient!(bb::BarrierWrapper, x)
end
value(bb::BoxBarrier, x) = mapreduce(x->_barrier_term_value(x...), +, zip(x, bb.lower, bb.upper))
function value!(obj::BarrierWrapper, x)

obj.Fb = value(obj.b, x)
obj.Ftotal = obj.mu*obj.Fb
if in_box(obj, x)
value!(obj.obj, x)
obj.Ftotal += value(obj.obj)
end

obj.Ftotal
end
value(obj::BarrierWrapper) = obj.Ftotal
function value(obj::BarrierWrapper, x)

F = obj.mu*value(obj.b, x)

if in_box(obj, x)
F += value(obj.obj, x)
end
Expand Down Expand Up @@ -279,7 +284,6 @@ function optimize(

T = eltype(initial_x)
t0 = time()

outer_iterations = options.outer_iterations
allow_outer_f_increases = options.allow_outer_f_increases
show_trace, store_trace, extended_trace = options.show_trace, options.store_trace, options.extended_trace
Expand Down Expand Up @@ -314,12 +318,15 @@ function optimize(
@warn("Initial position cannot be on the boundary of the box. Moving elements to the interior.\nElement indices affected: $boundaryidx")
end

dfbox = BarrierWrapper(df, zero(T), l, u)
# We start out with a small but nonzero value of mu so that the objective doesn't evaluate to NaN for points outside the box
dfbox = BarrierWrapper(df, one(T) * 1e-05, l, u)
# Use the barrier-aware preconditioner to define
# barrier-aware optimization method instance (precondition relevance)
_optimizer = barrier_method(F.method, P, (P, x) -> F.precondprep(P, x, l, u, dfbox))


state = initial_state(_optimizer, options, dfbox, x)

# we wait until state has been initialized to set the initial mu because
# we need the gradient of the objective and initial_state will value_gradient!!
# the objective, so that forces an evaluation
Expand All @@ -328,6 +335,7 @@ function optimize(
end
dfbox.mu = initial_mu(dfbox, F)
if F.method isa NelderMead

for i = 1:length(state.f_simplex)
x = state.simplex[i]
boxval = value(dfbox.b, x)
Expand Down Expand Up @@ -359,6 +367,7 @@ function optimize(
stopped = false
_time = time()
while !converged && !stopped && iteration < outer_iterations

fval0 = dfbox.obj.F
# Increment the number of steps we've had to perform
iteration += 1
Expand Down Expand Up @@ -404,6 +413,10 @@ function optimize(
end
copyto!(x, minimizer(results))
boxdist = min(minimum(x-l), minimum(u-x))

# Sanity check
(boxdist < 0) && error("Optimizer produced a nonfeasible point")

if show_trace > 0
println()
println("Exiting inner optimizer with x = ", x)
Expand Down
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