/
analyticLBFGS.jl
101 lines (89 loc) · 2.5 KB
/
analyticLBFGS.jl
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include("radialProfiles.jl")
#=
Functions for Cost and Gradient used in the Optimize step with LBFGS
NB: Reparameterization for [s, g1, g2] via [σ, e1, e2] to constraint update steps inside R+ x B_2(r)
=#
function cost(params; r = r, c= c, starL=starCatalog[iteration], radial=fGaussian, AnalyticStampSize=AnalyticStampSize, get_middle_nxn=get_middle_nxn)
Totalcost = 0
σ = params[1]
s_guess = σ^2
e1_guess = params[2]
e2_guess = params[3]
ellipticity = sqrt((e1_guess)^2 + (e2_guess)^2)
normG = sqrt(1 + 0.5*( (1/ellipticity^2) - sqrt( (4/ellipticity^2)+ (1/ellipticity^4) ) ))
ratio = ellipticity/normG
g1_guess = e1_guess/ratio
g2_guess = e2_guess/ratio
starL = get_middle_nxn(starL, AnalyticStampSize)
r = AnalyticStampSize
c = AnalyticStampSize
sum = 0
for u in 1:r
for v in 1:c
try
sum += radial(u,v, g1_guess, g2_guess, s_guess, r/2,c/2)
catch
sum += 0
end
end
end
A_guess = 1/sum
for u in 1:r
for v in 1:c
if isnan(starL[u,v])
Totalcost += 0
else
Totalcost += 0.5*(A_guess*radial(u, v, g1_guess, g2_guess, s_guess, r/2, c/2) - starL[u, v])^2
end
end
end
return Totalcost
end
function costD(params; r=r, c=c, starL=pixelGridFits[iteration], radial=fGaussian, AnalyticStampSize=AnalyticStampSize, get_middle_nxn=get_middle_nxn)
Totalcost = 0
σ = params[1]
s_guess = σ^2
e1_guess = params[2]
e2_guess = params[3]
ellipticity = sqrt((e1_guess)^2 + (e2_guess)^2)
normG = sqrt(1 + 0.5*( (1/ellipticity^2) - sqrt( (4/ellipticity^2)+ (1/ellipticity^4) ) ))
ratio = ellipticity/normG
g1_guess = e1_guess/ratio
g2_guess = e2_guess/ratio
starL = get_middle_nxn(starL, AnalyticStampSize)
r = AnalyticStampSize
c = AnalyticStampSize
sum = 0
for u in 1:r
for v in 1:c
try
sum += radial(u,v, g1_guess, g2_guess, s_guess, r/2,c/2)
catch
sum += 0
end
end
end
A_guess = 1/sum
for u in 1:r
for v in 1:c
if isnan(starL[u,v])
Totalcost += 0
else
Totalcost += 0.5*(A_guess*radial(u, v, g1_guess, g2_guess, s_guess, r/2, c/2) - starL[u, v])^2
end
end
end
return Totalcost
end
function g!(storage, p)
grad_cost = ForwardDiff.gradient(cost, p)
storage[1] = grad_cost[1]
storage[2] = grad_cost[2]
storage[3] = grad_cost[3]
end
function gD!(storage, p)
grad_cost = ForwardDiff.gradient(costD, p)
storage[1] = grad_cost[1]
storage[2] = grad_cost[2]
storage[3] = grad_cost[3]
end