dyna3/engine-proto/gram-test/Engine.jl
2024-07-06 21:32:43 -07:00

130 lines
3.4 KiB
Julia

module Engine
using LinearAlgebra
using SparseArrays
using Random
export rand_on_shell, Q, DescentHistory, realize_gram
# === guessing ===
sconh(t, u) = 0.5*(exp(t) + u*exp(-t))
function rand_on_sphere(rng::AbstractRNG, ::Type{T}, n) where T
out = randn(rng, T, n)
tries_left = 2
while dot(out, out) < 1e-6 && tries_left > 0
out = randn(rng, T, n)
tries_left -= 1
end
normalize(out)
end
##[TO DO] write a test to confirm that the outputs are on the correct shells
function rand_on_shell(rng::AbstractRNG, shell::T) where T <: Number
space_part = rand_on_sphere(rng, T, 4)
rapidity = randn(rng, T)
sig = sign(shell)
[sconh(rapidity, sig)*space_part; sconh(rapidity, -sig)]
end
rand_on_shell(rng::AbstractRNG, shells::Array{T}) where T <: Number =
hcat([rand_on_shell(rng, sh) for sh in shells]...)
rand_on_shell(shells::Array{<:Number}) = rand_on_shell(Random.default_rng(), shells)
# === Gram matrix realization ===
# the Lorentz form
Q = diagm([1, 1, 1, 1, -1])
# the difference between the matrices `target` and `attempt`, projected onto the
# subspace of matrices whose entries vanish at each empty index of `target`
function proj_diff(target::SparseMatrixCSC{T, <:Any}, attempt::Matrix{T}) where T
J, K, values = findnz(target)
result = zeros(size(target)...)
for (j, k, val) in zip(J, K, values)
result[j, k] = val - attempt[j, k]
end
result
end
# a type for keeping track of gradient descent history
struct DescentHistory{T}
scaled_loss::Array{T}
stepsize::Array{T}
backoff_steps::Array{Int64}
function DescentHistory{T}(
scaled_loss = Array{T}(undef, 0),
stepsize = Array{T}(undef, 0),
backoff_steps = Int64[]
) where T
new(scaled_loss, stepsize, backoff_steps)
end
end
# seek a matrix `L` for which `L'QL` matches the sparse matrix `gram` at every
# explicit entry of `gram`. use gradient descent starting from `guess`
function realize_gram(
gram::SparseMatrixCSC{T, <:Any},
guess::Matrix{T};
scaled_tol = 1e-30,
target_improvement = 0.5,
init_stepsize = 1.0,
backoff = 0.9,
max_descent_steps = 600,
max_backoff_steps = 110
) where T <: Number
# start history
history = DescentHistory{T}()
# scale tolerance
scale_adjustment = sqrt(T(nnz(gram)))
tol = scale_adjustment * scaled_tol
# initialize variables
stepsize = init_stepsize
L = copy(guess)
# do gradient descent
Δ_proj = proj_diff(gram, L'*Q*L)
loss = norm(Δ_proj)
for step in 1:max_descent_steps
# stop if the loss is tolerably low
if loss < tol
break
end
# find negative gradient of loss function
neg_grad = 4*Q*L*Δ_proj
slope = norm(neg_grad)
# store current position and loss
L_last = L
loss_last = loss
push!(history.scaled_loss, loss / scale_adjustment)
# find a good step size using backtracking line search
push!(history.stepsize, 0)
push!(history.backoff_steps, max_backoff_steps)
for backoff_steps in 0:max_backoff_steps
history.stepsize[end] = stepsize
L = L_last + stepsize * neg_grad
Δ_proj = proj_diff(gram, L'*Q*L)
loss = norm(Δ_proj)
improvement = loss_last - loss
if improvement >= target_improvement * stepsize * slope
history.backoff_steps[end] = backoff_steps
break
end
stepsize *= backoff
end
end
# return the factorization and its history
push!(history.scaled_loss, loss / scale_adjustment)
L, history
end
end