Stochastic Tunneling

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Assessment

Difficulty
3/5
Estimated time
1-2 days
Newbie friendliness
45/100
Issue type
Documentation
Clarity
Mostly clear
Activity status
Stale
Tech stack
julia
Domain
documentation

Research direction

Start from the embedded Julia implementation of ObjDist and STUN, and read the surrounding documentation structure in the repository to find the appropriate tutorial or example location. Done means adding a clear write-up of stochastic tunneling that explains the shown usage and integrates it into the documentation.

Written by the indexing model from the issue text.

Description

@mohamed82008 wrote a nifty thing to do stochastic tunneling and it might be nice to do a little write up on it.

using Distributions, LinearAlgebra

mutable struct ObjDist{F, Tobj, Tsol, Tstep, Tbound} <: Distribution{Multivariate, Continuous}
    f::F
    best_obj::Tobj
    best_sol::Tsol
    step::Tstep
    lb::Tbound
    ub::Tbound
end

function ObjDist(f, N=1; step = 1.0, lb=-Inf, ub=Inf)
    x0 = rand.(TruncatedNormal.(zeros(N), step, lb, ub))
    obj = f(x0)
    return ObjDist(f, obj, x0, step, lb, ub)
end

function Base.rand(dist::ObjDist)
    N = length(dist.best_sol)
    r = rand.(TruncatedNormal.(dist.best_sol, dist.step, dist.lb, dist.ub))
    return r
end

function Distributions.logpdf(dist::ObjDist, x::AbstractVector)
    obj = dist.f(x)
    if obj > dist.best_obj || isnan(dist.best_obj)
        dist.best_obj = obj
        dist.best_sol .= x
    end
    return obj
end

using Turing

function STUN(f, N, alg = MH(10000))
    dist = ObjDist(f, N)
    @model obj_model() = begin
        obj ~ dist
    end
    sample(obj_model(), alg)
    return dist.best_sol, dist.best_obj
end

STUN(x->-norm(x .- 20), 3)
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Merged PRs (30d)
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