buffer.jl

###############################################################################
#
# We already illustrated the use of Stopping for optimization algorithm,
# however, in the case where one algorithm/solver is not Stopping-compatible,
# a buffer solver is required to unify the formalism.
# We illustrate this situation here with the Ipopt solver.
#
# Remark in the buffer function: in case the solver stops with success
# but the stopping condition is not satisfied, one option is to iterate
# and reduce the various tolerances.
#
# Documentation for Ipopt options can be found here:
# https://coin-or.github.io/Ipopt/OPTIONS.html#OPTIONS_REF
###############################################################################

using Ipopt, NLPModels, NLPModelsIpopt, Stopping

include("../test-stopping/rosenbrock.jl")
x0  = 1.5 * ones(6)
nlp = ADNLPModel(rosenbrock,  x0)

#The traditional way to solve an optimization problem using NLPModelsIpopt
#https://github.com/JuliaSmoothOptimizers/NLPModelsIpopt.jl
printstyled("Oth scenario:\n")

stats = ipopt(nlp, print_level = 0, x0 = x0)
#Use y0 (general), zL (lower bound), zU (upper bound)
#for initial guess of Lagrange multipliers.
@show stats.solution, stats.status

#Using Stopping, the idea is to create a buffer function
function solveIpopt(stp :: NLPStopping)

 #xk = solveIpopt(stop.pb, stop.current_state.x)
 stats = ipopt(nlp, print_level     = 0,
                    tol             = stp.meta.rtol,
                    x0              = stp.current_state.x,
                    max_iter        = stp.meta.max_iter,
                    max_cpu_time    = stp.meta.max_time,
                    dual_inf_tol    = stp.meta.atol,
                    constr_viol_tol = stp.meta.atol,
                    compl_inf_tol   = stp.meta.atol)

 #Update the meta boolean with the output message
 if stats.status == :first_order stp.meta.suboptimal      = true end
 if stats.status == :acceptable  stp.meta.suboptimal      = true end
 if stats.status == :infeasible  stp.meta.infeasible      = true end
 if stats.status == :small_step  stp.meta.stalled         = true end
 if stats.status == :max_iter    stp.meta.iteration_limit = true end
 if stats.status == :max_time    stp.meta.tired           = true end

 stp.meta.nb_of_stop = stats.iter
 #stats.elapsed_time

 x = stats.solution

 #Not mandatory, but in case some entries of the State are used to stop
 fill_in!(stp, x)

 stop!(stp)

 return stp
end

nlp_at_x = NLPAtX(x0)
stop = NLPStopping(nlp, unconstrained_check, nlp_at_x)

#1st scenario, we solve again the problem with the buffer solver
printstyled("1st scenario:\n")
solveIpopt(stop)
@show stop.current_state.x, status(stop)
nbiter = stop.meta.nb_of_stop

#2nd scenario: we check that we control the maximum iterations.
printstyled("2nd scenario:\n")
#rstate is set as true to allow reinit! modifying the State
reinit!(stop, rstate = true, x = x0)
stop.meta.max_iter = max(nbiter-4,1)

solveIpopt(stop)
#Final status is :IterationLimit
@show stop.current_state.x, status(stop)

printstyled("The End.\n")