forked from glen/dyna3
53d8c38047
In previous commits, the `circles-in-triangle` example converged much more slowly in BigFloat precision than in Float64 precision. This turned out to be a sign of a bug in the Float64 computation: converting the Gram matrix using `Float64.()` dropped the explicit zeros, removing many constraints and making the problem much easier to solve. This commit corrects the Gram matrix conversion. The Float64 search now solves the same problem as the BigFloat search, with comparable performance.
108 lines
No EOL
3.2 KiB
Julia
108 lines
No EOL
3.2 KiB
Julia
include("Engine.jl")
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using SparseArrays
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# initialize the partial gram matrix for a sphere inscribed in a regular
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# tetrahedron
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J = Int64[]
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K = Int64[]
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values = BigFloat[]
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for j in 1:9
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for k in 1:9
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filled = false
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if j == k
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push!(values, j < 9 ? 1 : 0)
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filled = true
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elseif (j == 9)
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if (k <= 5 && k != 2)
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push!(values, 0)
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filled = true
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end
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elseif (k == 9)
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if (j <= 5 && j != 2)
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push!(values, 0)
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filled = true
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end
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elseif (j == 1 || k == 1)
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push!(values, 0)
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filled = true
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elseif (j == 2 || k == 2)
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push!(values, -1)
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filled = true
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end
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if filled
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push!(J, j)
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push!(K, k)
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end
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end
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end
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append!(J, [6, 4, 6, 5, 7, 5, 7, 3, 8, 3, 8, 4])
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append!(K, [4, 6, 5, 6, 5, 7, 3, 7, 3, 8, 4, 8])
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append!(values, fill(-1, 12))
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#= make construction rigid
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append!(J, [3, 4, 4, 5])
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append!(K, [4, 3, 5, 4])
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append!(values, fill(-0.5, 4))
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=#
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gram = sparse(J, K, values)
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# set initial guess (random)
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## Random.seed!(58271) # stuck; step size collapses on step 48
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## Random.seed!(58272) # good convergence
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## Random.seed!(58273) # stuck; step size collapses on step 18
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## Random.seed!(58274) # stuck
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## Random.seed!(58275) #
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## guess = Engine.rand_on_shell(fill(BigFloat(-1), 8))
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# set initial guess
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guess = hcat(
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Engine.plane(BigFloat[0, 0, 1], BigFloat(0)),
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Engine.sphere(BigFloat[0, 0, 0], BigFloat(1//2)),
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Engine.plane(BigFloat[1, 0, 0], BigFloat(1)),
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Engine.plane(BigFloat[cos(2pi/3), sin(2pi/3), 0], BigFloat(1)),
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Engine.plane(BigFloat[cos(-2pi/3), sin(-2pi/3), 0], BigFloat(1)),
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Engine.sphere(BigFloat[-1, 0, 0], BigFloat(1//5)),
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Engine.sphere(BigFloat[cos(-pi/3), sin(-pi/3), 0], BigFloat(1//5)),
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Engine.sphere(BigFloat[cos(pi/3), sin(pi/3), 0], BigFloat(1//5)),
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BigFloat[0, 0, 0, 1, 1]
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)
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#=
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guess = hcat(
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Engine.plane(BigFloat[0, 0, 1], BigFloat(0)),
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Engine.sphere(BigFloat[0, 0, 0], BigFloat(0.9)),
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Engine.plane(BigFloat[1, 0, 0], BigFloat(1)),
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Engine.plane(BigFloat[cos(2pi/3), sin(2pi/3), 0], BigFloat(1)),
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Engine.plane(BigFloat[cos(-2pi/3), sin(-2pi/3), 0], BigFloat(1)),
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Engine.sphere(4//3*BigFloat[-1, 0, 0], BigFloat(1//3)),
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Engine.sphere(4//3*BigFloat[cos(-pi/3), sin(-pi/3), 0], BigFloat(1//3)),
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Engine.sphere(4//3*BigFloat[cos(pi/3), sin(pi/3), 0], BigFloat(1//3)),
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BigFloat[0, 0, 0, 1, 1]
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)
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=#
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# complete the gram matrix using gradient descent followed by Newton's method
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#=
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L, history = Engine.realize_gram_gradient(gram, guess, scaled_tol = 0.01)
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L_pol, history_pol = Engine.realize_gram_newton(gram, L, rate = 0.3, scaled_tol = 1e-9)
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L_pol2, history_pol2 = Engine.realize_gram_newton(gram, L_pol)
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=#
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L, success, history = Engine.realize_gram(Engine.convertnz(Float64, gram), Float64.(guess))
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completed_gram = L'*Engine.Q*L
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println("Completed Gram matrix:\n")
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display(completed_gram)
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#=
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println(
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"\nSteps: ",
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size(history.scaled_loss, 1),
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" + ", size(history_pol.scaled_loss, 1),
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" + ", size(history_pol2.scaled_loss, 1)
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)
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println("Loss: ", history_pol2.scaled_loss[end], "\n")
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=#
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if success
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println("\nTarget accuracy achieved!")
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else
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println("\nFailed to reach target accuracy")
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end
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println("Steps: ", size(history.scaled_loss, 1))
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println("Loss: ", history.scaled_loss[end], "\n") |