Use Newton's method for polishing

This commit is contained in:
Aaron Fenyes 2024-07-11 13:43:52 -07:00
parent d538cbf716
commit 3910b9f740
4 changed files with 103 additions and 18 deletions

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@ -51,11 +51,21 @@ end
# the Lorentz form
Q = diagm([1, 1, 1, 1, -1])
# project a matrix onto the subspace of matrices whose entries vanish at the
# given indices
function proj_to_entries(mat, indices)
result = zeros(size(mat))
for (j, k) in indices
result[j, k] = mat[j, k]
end
result
end
# 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)...)
result = zeros(size(target))
for (j, k, val) in zip(J, K, values)
result[j, k] = val - attempt[j, k]
end
@ -87,7 +97,7 @@ 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(
function realize_gram_gradient(
gram::SparseMatrixCSC{T, <:Any},
guess::Matrix{T};
scaled_tol = 1e-30,
@ -111,7 +121,7 @@ function realize_gram(
# do gradient descent
Δ_proj = proj_diff(gram, L'*Q*L)
loss = dot(Δ_proj, Δ_proj)
for step in 1:max_descent_steps
for _ in 1:max_descent_steps
# stop if the loss is tolerably low
if loss < tol
break
@ -160,4 +170,73 @@ function realize_gram(
L, history
end
function basis_matrix(::Type{T}, j, k, dims) where T
result = zeros(T, dims)
result[j, k] = one(T)
result
end
# seek a matrix `L` for which `L'QL` matches the sparse matrix `gram` at every
# explicit entry of `gram`. use Newton's method starting from `guess`
function realize_gram_newton(
gram::SparseMatrixCSC{T, <:Any},
guess::Matrix{T};
scaled_tol = 1e-30,
rate = 1,
max_steps = 100
) where T <: Number
# start history
history = DescentHistory{T}()
# find the dimension of the search space
dims = size(guess)
element_dim, construction_dim = dims
total_dim = element_dim * construction_dim
# list the constrained entries of the gram matrix
J, K, _ = findnz(gram)
constrained = zip(J, K)
# scale the tolerance
scale_adjustment = sqrt(T(length(constrained)))
tol = scale_adjustment * scaled_tol
# use newton's method
L = copy(guess)
for step in 0:max_steps
# evaluate the loss function
Δ_proj = proj_diff(gram, L'*Q*L)
loss = dot(Δ_proj, Δ_proj)
# store the current loss
push!(history.scaled_loss, loss / scale_adjustment)
# stop if the loss is tolerably low
if loss < tol || step > max_steps
break
end
# find the negative gradient of loss function
neg_grad = 4*Q*L*Δ_proj
# find the negative Hessian of the loss function
hess = Matrix{T}(undef, total_dim, total_dim)
indices = [(j, k) for k in 1:construction_dim for j in 1:element_dim]
for (j, k) in indices
basis_mat = basis_matrix(T, j, k, dims)
neg_dΔ = basis_mat'*Q*L + L'*Q*basis_mat
neg_dΔ_proj = proj_to_entries(neg_dΔ, constrained)
deriv_grad = 4*Q*(-basis_mat*Δ_proj + L*neg_dΔ_proj)
hess[:, (k-1)*element_dim + j] = reshape(deriv_grad, total_dim)
end
# compute the newton step
step = hess \ reshape(neg_grad, total_dim)
L += rate * reshape(step, dims)
end
# return the factorization and its history
L, history
end
end

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@ -55,7 +55,6 @@ gram = sparse(J, K, values)
## guess = Engine.rand_on_shell(fill(BigFloat(-1), 8))
# set initial guess
#=
guess = hcat(
Engine.plane(BigFloat[0, 0, 1], BigFloat(0)),
Engine.sphere(BigFloat[0, 0, 0], BigFloat(1//2)),
@ -67,7 +66,7 @@ guess = hcat(
Engine.sphere(BigFloat[cos(pi/3), sin(pi/3), 0], BigFloat(1//5)),
BigFloat[0, 0, 0, 1, 1]
)
=#
#=
guess = hcat(
Engine.plane(BigFloat[0, 0, 1], BigFloat(0)),
Engine.sphere(BigFloat[0, 0, 0], BigFloat(0.9)),
@ -79,11 +78,19 @@ guess = hcat(
Engine.sphere(4//3*BigFloat[cos(pi/3), sin(pi/3), 0], BigFloat(1//3)),
BigFloat[0, 0, 0, 1, 1]
)
=#
# complete the gram matrix using gradient descent
L, history = Engine.realize_gram(gram, guess, max_descent_steps = 200)
# complete the gram matrix using gradient descent followed by Newton's method
L, history = Engine.realize_gram_gradient(gram, guess, scaled_tol = 0.01)
L_pol, history_pol = Engine.realize_gram_newton(gram, L, rate = 0.3, scaled_tol = 1e-9)
L_pol2, history_pol2 = Engine.realize_gram_newton(gram, L_pol)
completed_gram = L'*Engine.Q*L
println("Completed Gram matrix:\n")
display(completed_gram)
println("\nSteps: ", size(history.stepsize, 1))
println("Loss: ", history.scaled_loss[end], "\n")
println(
"\nSteps: ",
size(history.scaled_loss, 1),
" + ", size(history_pol.scaled_loss, 1),
" + ", size(history_pol2.scaled_loss, 1)
)
println("Loss: ", history_pol2.scaled_loss[end], "\n")

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@ -47,13 +47,14 @@ guess = hcat(
Engine.rand_on_shell(fill(BigFloat(-1), 2))
)
# complete the gram matrix using gradient descent
L, history = Engine.realize_gram(gram, guess)
# complete the gram matrix using gradient descent followed by Newton's method
L, history = Engine.realize_gram_gradient(gram, guess, scaled_tol = 0.01)
L_pol, history_pol = Engine.realize_gram_newton(gram, L)
completed_gram = L'*Engine.Q*L
println("Completed Gram matrix:\n")
display(completed_gram)
println("\nSteps: ", size(history.stepsize, 1))
println("Loss: ", history.scaled_loss[end], "\n")
println("\nSteps: ", size(history.scaled_loss, 1), " + ", size(history_pol.scaled_loss, 1))
println("Loss: ", history_pol.scaled_loss[end], "\n")
# === algebraic check ===

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@ -1,8 +1,6 @@
include("Engine.jl")
using SparseArrays
using AbstractAlgebra
using PolynomialRoots
using Random
# initialize the partial gram matrix for a sphere inscribed in a regular
@ -35,10 +33,10 @@ guess = sqrt(1/BigFloat(3)) * BigFloat[
1 1 1 1 1
] + 0.2*Engine.rand_on_shell(fill(BigFloat(-1), 5))
# complete the gram matrix using gradient descent
L, history = Engine.realize_gram(gram, guess)
# complete the gram matrix using Newton's method
L, history = Engine.realize_gram_newton(gram, guess)
completed_gram = L'*Engine.Q*L
println("Completed Gram matrix:\n")
display(completed_gram)
println("\nSteps: ", size(history.stepsize, 1))
println("\nSteps: ", size(history.scaled_loss, 1))
println("Loss: ", history.scaled_loss[end], "\n")