Example of a fixed point algorithm
Stopping can also be used for fixed point methods Example here concerns the AlternatingDirections Algorithm to find a feasible point in the intersection of 2 convex sets A and B. This algorithm relies on a fixed point argument, hence it stopped if it finds a fixed point.
Example: A={ (x,y) | x=y} and B = {(x,y) | y=0} Clearly the unique intersection point is (0,0)
Note that in this case the projection on A and the projection on B are trivial
Takeaway: the 2nd scenario illustrates a situation where the algorithm stalls as it reached a personal success. (optimalsubpb is true)
The Julia file corresponding to this tutorial can be found here.
using LinearAlgebra, NLPModels, Stopping, Test
Main algorithm
function AlternatingDirections(stp)
xk = stp.current_state.x
OK = update_and_start!(stp, cx = cons(stp.pb, x0))
@show OK, xk
while !OK
#First projection
xk1 = 0.5 * (xk[1] + xk[2]) * ones(2)
#Second projection
xk2 = [xk1[1],0.0]
#check if we have a fixed point
Fix = dot(xk-xk2,xk-xk2)
if Fix <= min(eps(Float64),stp.meta.atol) stp.meta.suboptimal = true end
#call the stopping
OK = update_and_stop!(stp, x = xk2, cx = cons(stp.pb, xk2))
xk = xk2
@show OK, xk
end
return stp
end
We model the problem using the NLPModels without objective function Formulate the problem with NLPModels
c(x) = [x[1] - x[2], x[2]]
lcon = [0.0, 0.0]
ucon = [0.0, 0.0]
nlp = ADNLPModel(x->0.0, zeros(2), c=c, lcon=lcon, ucon=ucon)
1st scenario: we solve the problem
printstyled("1st scenario:\n")
#Prepare the Stopping
x0 = [0.0, 5.0]
state = NLPAtX(x0)
#Recall that for the optimality_check function x is the pb and y is the state
#Here we take the infinite norm of the residual.
stop = NLPStopping(nlp, (x,y) -> norm(y.cx,Inf), state)
AlternatingDirections(stop)
@show status(stop)
@test status(stop) == :Optimal
2nd scenario: the user gives an irrealistic optimality condition
printstyled("2nd scenario:\n")
reinit!(stop, rstate = true, x = x0)
stop.optimality_check = (x,y) -> norm(y.cx,Inf)+0.5
AlternatingDirections(stop)
#In this scenario, the algorithm stops because it attains a fixed point
#Hence, status is :SubOptimal.
@show status(stop)
@test status(stop) == :SubOptimal